BMJ Open · Setting: British Columbia administrative health data (inpatients, outpatients and pharmacy) Participants: 2,742 British Columbia residents who initiated a first course

For peer review only

Prescriber Preference for a Particular Tumor Necrosis Factor Antagonist Drug and Treatment Discontinuation:

Population-based Cohort

Journal: BMJ Open

Manuscript ID: bmjopen-2014-005532

Article Type: Research

Date Submitted by the Author: 23-Apr-2014

Complete List of Authors: Fisher, Anat; University of British Columbia, Anesthesiology, Pharmacology & Therapeutics Basset, Ken; University of British Columbia, Anesthesiology, Pharmacology

& Therapeutics; University of British Columbia, Family Practice Wright, James (Jim); University of British Columbia, Anesthesiology, Pharmacology & Therapeutics; University of British Columbia, Medicine Brookhart, M.; University of North Carolina at Chapel Hill, Epidemiology Freeman, Hugh; University of British Columbia, Medicine Dormuth, Colin; University of British Columbia, Department of Anesthesiology, Pharmacology and Therapeutics

<b>Primary Subject Heading</b>:

Epidemiology

Secondary Subject Heading: Epidemiology, Health services research, Patient-centred medicine, Research methods, Rheumatology

Keywords: EPIDEMIOLOGY, RHEUMATOLOGY, Health policy < HEALTH SERVICES

ADMINISTRATION & MANAGEMENT

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Comparative Persistence of TNF Antagonists in

Rheumatoid Arthritis – A Population-Based Cohort

Study

Anat Fisher, MD, PhD; Ken Bassett, MD, PhD; James M. Wright, MD, PhD; M. Alan

Brookhart, PhD; Hugh Freeman, MD; Colin R. Dormuth, ScD

Corresponding Author: Anat Fisher, MD, PhD; Department of Anesthesiology, Pharmacology

and Therapeutics, University of British Columbia, 2176 Health Sciences Mall, Vancouver, BC,

Canada V6T 1Z3, Telephone: 604-822-0700 Fax: 604-822-0701, Email: [email protected]

Ken Bassett: Department of Anesthesiology, Pharmacology & Therapeutics and Department of

Family Practice, University of British Columbia, Vancouver, Canada

James M. Wright: Department of Anesthesiology, Pharmacology & Therapeutics and

Department of Medicine, University of British Columbia, Vancouver, Canada

M. Alan Brookhart: Department of Epidemiology, Gillings School of Global Public Health,

University of North Carolina, Chapel Hill, North Carolina, USA

Hugh Freeman: Department of Medicine, University of British Columbia, Vancouver, Canada

Colin R. Dormuth: Department of Anesthesiology, Pharmacology & Therapeutics, University of

British Columbia, Vancouver, Canada

Keywords: physician prescribing pattern, rheumatoid arthritis, medication persistence, Tumor

Necrosis Factor-alpha

Number of words (excluding title page, abstract, references, figures and tables, endnotes): 2581

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ABSTRACT

Objective: To assess the effect of physician preference for a particular tumor necrosis factor

alpha (TNF) antagonist on the risk of treatment discontinuation in rheumatoid arthritis.

Design: Population-based cohort study.

Setting: British Columbia administrative health data (inpatients, outpatients and pharmacy)

Participants: 2,742 British Columbia residents who initiated a first course of a TNF antagonist

between 2001 and December 2008, had been diagnosed with rheumatoid arthritis, and were

treated by one of 58 medium to high-volume prescribers.

Independent variable: A level of physician preference for the drug (higher or lower) was

assigned based on preceding prescribing records of the care-providing physician. Higher

preference was defined as at least 60% of TNF antagonist courses initiated in the preceding year.

Sensitivity analysis was conducted, with different thresholds for higher preference.

Main Outcome Measure: Drug discontinuation was defined as a drug-free interval of 180 days

or switching to another TNF antagonist, anakinra, rituximab or abatacept. The risk of

discontinuation was compared between different levels of physician preference using survival

analysis.

Results: Higher preference for the prescribed TNF antagonist was associated with improved

persistence with the drug (4.28 years [95% confidence interval 3.70-4.90] versus 3.27 [2.84-

3.84], with log rank test p-value of 0.017). The adjusted hazard ratio for discontinuation was

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significantly lower in courses of drugs with higher preference (0.85 [0.76-0.96]). The results

were robust in a sensitivity analysis.

Conclusions: Higher physician preference was associated with decreased risk of discontinuing

TNF antagonists in patients with rheumatoid arthritis. This finding suggests that physicians who

strongly prefer a specific treatment help their patients to stay on treatment for a longer duration.

Similar research on other treatments is warranted.

Strengths and Limitations

• First study to explore within physician variation in prescribing habits, specifically the

effect of prescriber preference to a drug on the decision to discontinue the drug

• The universal nature of the Canadian health care system and a systematic and

standardized approach to data collection in British Columbia which ensured the

generalizability of our results, as well as the large sample and prolonged follow up

• To conquer the absence of access to clinical data we used multiple proxy variables to

adjust for disease severity

• Physician preference was based on previous prescribing habits and not directly measured

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INTRODUCTION

The term ‘physician preference’ usually refers to favoring a particular drug or a therapeutic

group among several alternatives, and it has been shown to predict treatment choice (1-4). In

studies of administrative heath (claim) data, this preference is often determined by identifying

dispensing of drug prescribed by the specific physician in a pre-determined period, prior to the

event of interest (a new prescribing). Despite an association with new prescribing desicions, the

role of physician preference in treatment discontinuation has not been studied. Recently, the term

‘preference’ has also been used to describe a second phenomenon – in the context of treatment

discontinuation is was used to describe the baseline risk of discontinuing treatment in patients

treated by a specific physician (the physician ‘preference for discontinuation’) (5). This baseline

risk may differ among physicians because physicians may respond differently to similar clinical

situations such as decreased benefit or harmful events. They could recommend patients to

discontinue treatment (or switch to a second drug) or to persist with the treatment (but to adjust

dose, add-on a second drug or be under frequent watch). In this paper, we use the term

‘preference’ to describe the first phenomenon (physician’s favorite drug) and ‘physician-specific

discontinuation risk’ to describe the second.

Treatment with TNF antagonists in patients with rheumatoid arthritis (RA) was considered

especially sensitive to physician preference for two main reasons. First, during the study period

(2001-2009) there was limited clinical evidence on the comparative effectiveness of the drugs,

due mostly to the absence of head-to-head randomized clinical trials, but also because

participants in placebo-controlled trials were not representative of patients treated in routine clinical

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settings (6-9). Second, published indications for discontinuation of TNF antagonists were vague

and confusing, and therefore care-providing physicians could reasonably be expected to reach

different clinical decisions given the same clinical situation. Consequently, the decisions about

which TNF antagonist to prescribe first and when to discontinue treatment were likely subject to

physicians’ individual preference.

This study analyzed data of first courses of a TNF antagonist in British Columbia patients with

RA. The prescriber recorded on the first dispensing claim for a TNF antagonist was used as a

proxy of the care-providing physician. The study objective was to estimate the effect of

physician preference on the risk of discontinuation. The null hypothesis tested was that physician

preference for a TNF antagonist when treatment has been initiated does not influence the risk of

discontinuing the treatment in RA patients.

PATIENTS AND METHODS

The study cohort was identified using four British Columbia Ministry of Health administrative

databases: PharmaNet (prescription dispensing data), Medical Service Plan (MSP) registration

information (demographic data), MSP Payment Information (fee-for-service payments to

physicians and alternative providers), and the Discharge Abstract Database (hospital

separations). The databases were linkable using a de-identified patient and physician numbers.

Follow-up data were available from 1995 until December 31, 2009. The study cohort included

British Columbia residents who fulfilled all of three conditions: (1) first exposure to a TNF

antagonist between March 1, 2001 and December 31, 2008; (2) a diagnosis of RA; and (3) TNF

antagonist treatment initiated by a medium or high-volume prescriber, defined as a prescriber

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who initiated 5 or more courses of TNF antagonists in RA patients during the years 2001-2008.

Exposure to a TNF antagonist was based on one or more recorded dispensing claims for

infliximab, adalimumab or etanercept between March 2001 and December 31, 2008. We

excluded patients who were previously treated with anakinra, rituximab or abatacept to decrease

heterogeneity in the population analyzed. The first dispensing claim for a TNF antagonist was

used to identify the study drug (infliximab, adalimumab or etanercept), index date and prescriber

for each patient in the cohort. We defined a pre-study period of three years preceding the index

date during which patients were required to have continuous provincial medical service

coverage. A gap shorter than 30 days was not considered to be an interruption in coverage. The

pre-study period ensured a standard run-in period of at least three years without TNF antagonist

exposure and a standard period during which diagnosis of RA was identified. RA patients were

selected based on similar criteria to previous studies in British Columbia (10-12): either two

outpatient visits in physician clinics with a diagnosis code of RA (ICD-9 714) at least 60 days

apart, or one hospitalization with recorded discharge diagnosis of RA. Patients were excluded if

sex or date of birth was not available, if they had a concurrent diagnosis of Crohn’s disease

(based on at least one outpatient or inpatient diagnosis code in the pre-study period) or if they

were younger than 18 years at the index date (to remove patients with juvenile RA). We also

excluded patients initiated on TNF antagonist treatment by prescribers who cumulatively

initiated less than five patients in the RA cohort (low-volume prescribers).

Physician preference was determined for each patient at the index date and coded as a Bernoulli

variable. It reflects prescribing patterns of the individual physician (who started this course)

during the preceding year (Figure 1). For each individual patient, we determined the date of

course initiation, the prescriber recorded in the first dispensing event and the drug (infliximab,

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adalimumab or etanercept). From first dispensing records for each patient in the study cohort, we

identified other patients that received any TNF antagonists from the same prescriber during the

year preceding the index date of the current patient, and the drug they were treated with. Next we

calculated the proportion of these patients who received the same drug as the patient of interest.

Based on this proportion, we categorized the prescriber preference to ‘higher’ when the

calculated proportion for this drug and that prescriber was 60% or higher, and ‘lower’ otherwise.

We expected that this threshold would provide reasonable assurance that a course categorized as

having ‘higher” preference for drug actually reflects that the prescriber favored that particular

TNF antagonist at this time, even if only two TNF antagonists were available. We reassessed

preference for drug based on annual data to allow for changes in preference over time and for

accommodating the more recent availability of adalimumab. Adalimumab was available in

Canada since October 2004, while infliximab and etanercept were on the market since 2001. By

including a year of data, differently from other studies that used only the last prescription in

assigning preference (1-4), we created a more accurate indicator. This way we minimized the

effect of factors not related to preference that might have influence a specific prescribing

decision. Sensitivity analysis was conducted to examine the robustness of main results, using

thresholds of 70% or 80% for the level of physician preference.

Multiple covariates, mainly patient characteristics that may influence drug selection and/or

discontinuation were included in the final models and are listed in Table 1. We were limited by

lack of access to clinical data and inability to adjust for clinical variables not captured in

administrative health data, such as disease severity. Hence, we used multiple proxies, including

disease duration, age, extra-articular involvement, antirheumatic drug use, health services

utilization and consumption of pain medications to adjust for disease severity. In order to control

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for secular trends and late availability of adalimumab in Canada, we included a categorical

variable for the year of treatment initiation.

Table 1 - List of covariates included in the final models

Variable Description

Demographics

Sex Bernoulli variable

Age at index Four categories: 18-29 years, 30-69, 70-79 and ≥80 (categories were

assigned based on similar discontinuation risk in preliminary

analysis, which was not linear)

The annual deductible for

prescription cost at index

(Fair PharmaCare)

The deductible is based on annual income(13). Six categories:

annual deductible of $0, $1-500, $501-2250, >$2250, other

programs and no coverage

Geographical area at

index

Based on first 3 digit of postal code. Five mutually exclusive

categories: Greater Vancouver, Greater Victoria, Vancouver Island,

urban areas and rural areas

Clinical status

Physicians encounters in

the year preceding the

index date

Continuous variable

Number of inpatients

admissions in the year

preceding the index date

Four categories:0, 1, 2, >2

Comorbidities (presence

and severity) during the

pre-study period

Charlson comorbidity score (14) was determined using Quan’s

algorithm for administrative databases (15), excluding rheumatic

diseases. At least two outpatient or one inpatient encounter with the

diagnosis were required. Four categories: 0, 1, 2, >2

Disease duration Measured from the first diagnosis of RA (inpatient or outpatient) in

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the data. Continuous variable

The presence of extra-

articular manifestation

during the pre-study

period

Based on recorded at least one diagnoses with ICD-9 codes 3571,

3596, 7141, 71481, 7142 (outpatient) or ICD-10 codes G636, G737,

I39, I418, J990, M050, M051, M052, M053 (inpatient). Bernoulli

variable

Drug therapies

Concomitant MTX

during 200 days


Based on mean plus two standard deviations of between-dispensing

intervals of MTX in the study cohort. Bernoulli variable

Dispensing claims for

NSAIDs during the year


Bernoulli variable

Number of different

antirheumatic drugs

dispensed in the pre-

study period

Dispensing claims of 10 drugs were included: MTX,

hydroxychloroquine, sulfasalazine, leflunomide, azathioprine,

minocycline, penicillamine, sodium aurothiomalate, prednisone, and

intra-articular triamanolone or methylprednisolone. Four categories:

no drug, 1-2, 3-6, >6 different drugs

Other

Calendar year at index

date

The variable allowed controlling for secular trends in clinical

practice (16-19) and availability of drugs. Eight yearly categories

were included for the years 2001-2008

ICD-10- International Classification of Diseases, 10th edition; ICD-9-CM - International Classification of Diseases, 9th edition, clinical modification; MTX – methotrexate; NSAID – nonsteroidal anti-inflammatory drug

The outcome variable was a discontinuation defined as either switching to another TNF

antagonist including certolizumab and golimumab, anakinra, rituximab or abatacept (‘biologic’

antirheumatic drugs), or a drug-free interval of 180 days after exhaustion of the dispensed days-

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supply of the latest refill. The discontinuation date was set to the end of the days-supply of the

last refill before discontinuation or the date of the first dispensing of a second ‘biologic’

antirheumatic drug, whichever was earliest. Unless experienced discontinuation, patients were

followed up until December 31, 2009 (end of follow-up period) or until an interruption of more

than six days in the provincial MSP coverage, at which point data were considered censored. The

most common causes of such interruptions were death and emigration from the province. Change

of dose of the TNF antagonist or adding-on a second drug (not TNF antagonist) was not

considered discontinuation, and patients were continued to be followed.

Discrepancies between recorded days-supply and dispensed quantity were common in the

PharmaNet database; hence, we also calculated days-supply based on the quantity dispensed,

which, if required, was imputed using the recorded total cost. Cost was considered the most

accurate and reliable field, since this field serves for claim and payment processing. We used the

longest duration of days-supply, recorded or calculated, to determine both the length of drug-free

intervals and a discontinuation date.

Sample size calculation

Assuming 1:1 ratio of courses with ‘higher’ preference versus courses with ‘lower’ preference

and in the absence of prior estimates, if the true hazard ratio for discontinuing courses with

‘higher’ preference relative to courses with ‘higher’ preference is 0.8, we required 847 courses

with each preference level to be able to reject the null hypothesis with type I error probability

0.05 and power of 0.80.

Statistical Analysis

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Summary statistics of baseline characteristics were compared across the three drug groups. The

adjusted risk of discontinuation was compared using two different approaches in a Cox

proportional hazards regression. First, we conducted an analysis of nonclustered data. The

multivariate model also included a series of Bernoulli variables, one for each physician (1=the

particular physician; 0=other physicians), that allowed individualization of the risk of drug

discontinuation by physician (physician-specific discontinuation risk). In the second approach, a

marginal model of clustered data allowed an adjustment for possible correlation between patients

treated by the same physician (20-22). We tested for model assumptions (proportional hazard

and absence of interactions) and found them to be valid. We also checked for the linearity of

continuous variables and categorized non-linear variables. All statistical tests were two-sided.

All calculations were performed using the SAS software package (SAS Institute Inc., Cary, NC).

RESULTS

The study cohort included 2,742 RA patients prescribed by a total of 58 medium-high volume

physicians. Figure 2 presents reasons for excluding patients who used TNF antagonists from the

analysis. Baseline characteristics across the three drug groups are presented in Table 2. Not only

was etanercept was the most frequently prescribed drug (1718 patients, 63%), but also it was the

only drug prescribed by all 58 study physicians. Etanercept was usually initiated by a physician

with high preference for etanercept (70% of etanercept courses). Infliximab or adalimumab, on

the other hand, were usually initiated by physicians with lower preference for these drugs (only

34% or 19% of courses were initiated by a physician with high preference for drug,

respectively). Patients treated with adalimumab were significantly older and had a lower income

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(reflected in lower annual deductible level for prescribing costs). Patients treated with infliximab

had the highest prevalence of concomitant methotrexate therapy and patients with etanercept the

lowest. This reflects differences in the indications mention in the product monographs; while

infliximab is indicated for use in combination with methotrexate (23), the monograph of

etanercept mentioned that it “can be initiated in combination with methotrexate … or used alone”

(24). Similarly, while the provincial special authority policy requires that infliximab is used in

combination with methotrexate (or other drug) and such requirement does not exist for treatment

with etanercept or adalimumab (25).

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Table 2 - Baseline characteristics

Variable Infliximab Adalimumab Etanercept P-value

(between

groups

differences)

Number of patients (% from

cohort)

571 (21) 453 (16) 1718 (63)

Number of prescribers (based on

first dispensing event for each

patient) (total 58 prescribers)

49 46 58

Patients treated with a drug with

higher preference, No (%)

193 (34) 84 (19) 1198 (70) <0.0001

Demographics

Females, No (%) 403 (71) 326 (72) 1239 (72) 0.77

Age at index, median (range) y 56 (18-87) 58 (22-91) 56 (18-92) 0.003

Annual deductible for prescription

cost, No (%)

Very low ($0) 47 (8.3) 80 (18) 199 (12) <0.0001

Low ($1-500) 33 (5.8) 53 (12) 152 (8.9) 0.004

Medium ($501-2250) 92 (16) 109 (24) 315 (18) 0.004

High (>$2250) 35 (6.1) 38 (8.4) 143 (8.3) 0.22

Residence in Greater

Vancouver/Victoria, No (%)

341 (60) 224 (50) 782 (46) <0.0001

Clinical status

Number of physician visits median

(range)

33 (3-158) 31 (2-112) 32 (3-136) 0.25

At least one admission to hospital

No (%)

104 (18) 63 (14) 340 (20) 0.01

Extra-articular manifestations, No 28 (4.9) 14 (3.1) 60 (3.5) 0.23

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(%)

Presence of comorbidity (score>0),

No (%)

113 (20) 95 (21) 383 (22) 0.41

RA disease duration median,

(range) y

9.2 (0.1-

17.9)

7.7 (0.3-17.9) 8.0 (0-17.8) 0.21

RA drugs

Concomitant MTX, No (%) 388 (68) 264 (58) 856 (50) <0.0001

Dispensing claims for NSAIDs,

No (%)

307 (54) 214 (47) 923 (54) 0.04

Number of different antirheumatic

drugs, median (range)

4 (0-8) 4 (0-8) 4 (0-9) 0.46

%- percent, unless otherwise specified – percentage from patients treated with this drug; $-Canadian dollars; MTX-methotrexate; No-number of patients; NSAID- nonsteroidal anti-inflammatory drug; NSS – not statistically significant; y-years

For detailed descriptions of the variables refer to Table 1

Persistence with the three TNF antagonists was similar (log rank test p-value=0.15). The product

limit median persistence estimates were 3.9 years (95% confidence interval 3.0-5.3), 3.3 (2.6-4.0)

and 3.9 years (3.4-4.4) for infliximab, adalimumab and etanercept, respectively. Higher physician

preference was associated with improved persistence compared to lower preference (Figure 3),

with median time to discontinuation 4.28 years (confidence interval 3.70-4.90) in 1475 courses

with ‘high’ preference, compared with 3.27 (2.85-3.84) in 1267 courses with ‘low’ preference .

In both groups about 50% of the patients discontinued and the other 50% were censored.

Higher physician preference was associated with a significant decrease of 14-15% in the adjusted

hazard for discontinuation (Table 3). No significant interaction between drug and preference was

observed. The results of sensitivity analysis were similar, with overall adjusted hazard ratio

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between 0.85-0.88 in all models. The results of clustered data analyses with thresholds of 60%,

70% and 80% for physician preference were robust (Table 3). The results of nonclustered data

analyses for thresholds of 70% and 80%, on the other hand, did not reach the significance level.

That may be a result of decreasing numbers of patients treated with a drug of higher physician

preference and therefore decreased power to detect significant difference.

Table 3 - Hazard ratios for drug discontinuation, higher preference versus lower

preference

Approach

Hazard ratio (95% confidence interval)

Preference threshold

60%


70%


80%

Patients with higher

preference N (%)

1426 (52) 1234 (45) 987 (36)

Nonclustered

data analysis

Crude 0.88 (0.79-0.98) 0.89 (0.80-0.99) 0.83 (0.81-0.996)

Adjusted* 0.86 (0.76-0.98) 0.88 (0.77-1.01) 0.87 (0.75-1.004)

Marginal

modeling of

clustered data

Crude 0.88 (0.77-1.001) 0.89 (0.78-1.01) 0.89 (0.79-1.01)

Adjusted# 0.85 (0.76-0.96) 0.87 (0.78-0.97) 0.87 (0.78-0.98)

% - percent from overall patients (2742); N – Number of patients; *Adjusted by drug, calendar year, patient’s demographics and clinical status, and prescriber #Adjusted by drug, calendar year and patient’s demographics and clinical status

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DISCUSSION

We demonstrated two types of variations of physician decisions to discontinue treatment with TNF

antagonists: between-physician and within-physician. Between-physician variability in response

to similar clinical situations, such as decreased benefit or a harmful event is expressed as a

different baseline risk of discontinuation, depending on the treating physician. We used the term

physician-specific discontinuation risk for this phenomenon. It could be a result of differences in

education or experience, adherence to different guidelines and possible differences in patient

case mix. Within-physician variability, on the other hand, is a different response to a similar

clinical situation (similar patients, similar drug effects) by the same physician. In this study we

showed that this variability correlated to physician preference for drug – and specifically, higher

physician preference for a specific TNF antagonist drug was associated with a decreased risk of

discontinuing TNF antagonists in RA patients. This preference most probably reflected the

physician’s beliefs regarding the relative effectiveness and safety of the specific drug compared

to the alternatives, where a preferred drug is thought to be superior. When a physician treated

with a drug believed to be superior compared to comparators, patients were encouraged to stay

longer on the drug and the risk of discontinuation, measured in this study, decreased. We believe

that a different response (level of encouragement) to a similar clinical situation applied in

indefinite clinical situations, such as mild harmful effect or questionable benefit.

Strengths and weaknesses of the study

The main advantages of our study are the universal nature of the Canadian health care system

and a systematic and standardized approach to data collection in British Columbia which ensured

the generalizability of our results, as well as the large sample and prolonged follow up that

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increased the power of the study to detect differences in discontinuation risk. The main

disadvantage is the absence of access to clinical data; hence adjustment to patient mix was

difficult. To conquer this problem, we used multiple proxy variables to adjust for disease

severity, such as extra-articular involvement, antirheumatic drug use, health services utilization

and consumption of pain medications.

There are several limitations to ascertaining physician preference based on previous prescribing

habits. First, preference was not defined for all patients. For example, it was not defined for the

first patient treated by the physician, or if the physician did not prescribe this class of drugs in

the preceding year. Second, we assumed that increased loyalty for a particular TNF antagonist

reflects preference, but we did not measure preference directly. Nevertheless, stated preference

and prescribing habits were shown to correlate in previous research (26). Third, we measured

preference at the time of treatment initiation and not at discontinuation, and preference might

have changed over time. Finally, we assumed strong preference for one drug only and regarded

situations in which the physician preferred two of the TNF antagonists to be a situation of

treatment with a drug of lower preference.

Overview of previous studies

Treatment persistence was hypothesized to reflect therapeutic benefit and harm in chronic non-

curable diseases, such as rheumatoid arthritis (27). In support of this hypothesis, previous studies

in RA patients using TNF antagonists demonstrated that the main reasons for discontinuing or

switching were decreased benefit (36-67% of the discontinuations) or perceived harm (30-58%)

(28-33). As such, the risk of discontinuing treatment is assumed to be influenced solitarily by

drug properties and patient characteristics. The demonstrated effect of care-provider

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characteristics, specifically prescribing habits, on the risk of discontinuation calls into question

this hypothesis.

Between-physician differences in response to the same clinical situation (physician-specific

discontinuation risk) have previously been studies in RA patients treated by TNF antagonists.

Differences in response to harm were reported by Cush et al 2005 based on an on-line survey of

rheumatologists (1) and in response to decreased benefit – by Zhang et al 2011 (5). Using mixed

effect model with a random intercept to cluster patients at physician level, Zhang demonstrated

the importance of clustering by physicians, even after adjustment for both baseline disease

activity and improvement in disease activity.

Within-physician variation in prescribing decisions and the effect of physician preference have

infrequently been studied. While the effect of physician preference on discontinuation has not

been previously studied, in this therapeutic class or in other conditions, physician preference was

found to be an important predictor of drug selection in treatment initiation. Physician preference

for therapeutic class was the most important determinant in initiating treatment with TNF

antagonists compared to the alternative: prescribing synthetic antirheumatic drugs (1) and in

other treatment situations (3,4,34). Physician preference for an individual TNF antagonist was

studied by Kamal et al 2006 (35). In response to mailed questionnaires, most American

rheumatologists said they preferred etanercept over adalimumab or infliximab and considered

etanercept the most efficacious of the three drugs with less harm. In our study, etanercept was the

most prescribed TNF drug, and it was prescribed by all 58 medium to high-volume prescribers.

Explanations and interpretation

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We suggest several possible explanations for the finding of decreased risk of discontinuation

with increased physician preference for the prescribed TNF antagonist. First, the results could be

explained in light of the theory of cognitive dissonance (36). Dissonance is an uncomfortable

feeling caused by holding conflicting ideas simultaneously. In line with this theory, treating with

a drug believed to be inferior (lower preference) induces dissonance. In indefinite clinical

situations, such as mild harmful effect or questionable benefit, early drug discontinuation

supports the physician’s belief that the selected drug was inferior compared to the alternatives.

On the other hand, in a similar situation, a drug with a higher preference might be continued, to

support a belief in its superiority. The interpretation of our results using the theory of cognitive

dissonance is restricted by the lack of direct measurement of physician dissonance at the time of

treatment discontinuation. The indirect measure of dissonance we used, physician preference,

could also be influenced by factors unrelated to prescriber beliefs, such as limited availability of

a drug or higher relative cost. Direct measures of cognitive dissonance are complicated and

involve activities beyond the scope of the current study (37). Second, the observed association

between higher preference and decreased risk of discontinuation might be confounded by

experience with the drug, namely the total number of patients a physician has previously treated

with the same drug. Increased experience with a specific drug may be associated with higher

preference as a result of the algorithm we used to assign the level of preference, but also because

of a tendency to continue doing what is familiar. Theoretically, increased experience with a

specific drug would improve patient selection, and therefore is associated with improved benefit,

decreased harm and decreased risk of discontinuation in these patients. Lastly, a physician’s

stated preference for a particular drug has been shown to correlate with patient preference. The

selection of a specific TNF antagonist mostly depends on physician and/or patient preference

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because the benefit and harm profiles of the three drugs are considered to be similar (38-40)

despite limited relative effectiveness data. Physician and patient preferences for an individual

TNF antagonist are likely to be correlated, as was showed in studies of NSAID treatment in RA

of RA patients (41,42), although this was not studied in patients treated with TNF antagonists. In

addition, 67% of 77 Canadian rheumatologists surveyed reported concordance with the patient

preference more than 80% of the time (43). If physician and patient preference for TNF

antagonist do correlate, then improved persistence may have been a result of higher patient rather

than physician preference.

Our findings suggest that administrative restrictions alone, without a change in physicians’

preference and beliefs may not achieve the desired effect on practice. A change in drug coverage

policy, for example, may achieve the desired effect in short-term, since physicians would follow

the new policy and prescribe mainly the reimbursed drug. In the longer term, however, unless a

change in physician preference is achieved, the recommended drug would be discontinued early,

and patients would be switched to a second-line drug. As a result, the overall effect of a change

in policy alone would be less than desired.

Unanswered questions and future research

We theorize that in other conditions, when treating with a therapeutic class in which drugs are

considered to have similar benefits and harms, a similar association exists. Further study of these

conditions is required. In addition, we suggest exploring the explanation we suggested for the

association observed, such as measuring cognitive dissonance during prescribing decision (37) or

patient preference. Awareness of the possible role of cognitive dissonance in clinical decision-

making, based on beliefs not necessarily supported by clinical evidence, can contribute to the

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development of education programs for physicians. Further research is warranted to identifying

effective policy implementation approaches.

CONCLUSIONS

Higher physician preference, estimated using their prescribing habits, was associated with

decreased discontinuation risk in RA patients treated with TNF antagonists. This finding

highlights the limitation of introducing a new drug coverage policy without encouraging change

in physicians’ drug preference. Similar research on other treatments is warranted.

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Contributors: All authors contributed to the study design and interpreted the data, drafted the

article or revised it critically for important intellectual content, and gave final approval of the

version to be published. Analysis was performed as part of research conducted under the

supervision of CD as part of the requirements for AF’s PhD project.

Funding: The authors declare (1) The study was supported by the University of British Columbia

Graduate Fellowship and a grant to the University of British Columbia from the British

Columbia Ministry of Health. The publication of study results was not contingent on their

approval; (2) no financial relationships with commercial entities that might have an interest in

the submitted work; (3) no spouses, partners, or children with relationships with commercial

entities that might have an interest in the submitted work; and (4) no non-financial interests that

may be relevant to the submitted work.

Ethical approval: The study protocol was approved by the Clinical Research Ethics Board of the

University of British Columbia (number H11-00303).

Data sharing: patient level data available from Population Data BC (https://www.popdata.bc.ca/).

Patient consent was not obtained but the presented data are anonymised and risk of identification

is low. Statistical code available from the corresponding author.

Copyrights: The Corresponding Author has the right to grant on behalf of all authors and does

grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in

perpetuity, in all forms, formats and media (whether known now or created in the future), to i)

publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution

into other languages, create adaptations, reprints, include within collections and create

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summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative work(s)

based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v) the inclusion

of electronic links from the Contribution to third party material where-ever it may be located;

and, vi) licence any third party to do any or all of the above.

Transparency: the lead author affirms that the manuscript is an honest, accurate, and transparent

account of the study being reported; that no important aspects of the study have been omitted;

and that any discrepancies from the study as planned have been explained.

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Figure 1: Calculating the value of the independent variable physician preference

Figure 2: Study patients’ flaw

Figure 3: Persistence with TNF antagonists by physician preference levels

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Calculating the value of the independent variable physician preference 136x174mm (300 x 300 DPI)

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Study patients’ flaw

170x98mm (300 x 300 DPI)

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Persistence with TNF antagonists by physician preference levels 40x30mm (300 x 300 DPI)

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Prescriber Preference for a Particular Tumor Necrosis Factor Antagonist Drug and

Treatment Discontinuation: Population-based Cohort

STROBE Statement—Checklist of items that should be included in reports of cohort studies

Item

No Recommendation

Page

number

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or

the abstract

2

(b) Provide in the abstract an informative and balanced summary of

what was done and what was found

2-3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation

being reported

4-5

Objectives 3 State specific objectives, including any prespecified hypotheses 5

Methods

Study design 4 Present key elements of study design early in the paper 5

Setting 5 Describe the setting, locations, and relevant dates, including periods of

recruitment, exposure, follow-up, and data collection

5-6

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection

of participants. Describe methods of follow-up

5-6

(b) For matched studies, give matching criteria and number of exposed

and unexposed

n/a

Variables 7 Clearly define all outcomes, exposures, predictors, potential

confounders, and effect modifiers. Give diagnostic criteria, if applicable

5-11

Data sources/

measurement

8* For each variable of interest, give sources of data and details of

methods of assessment (measurement). Describe comparability of

assessment methods if there is more than one group

5-11

Bias 9 Describe any efforts to address potential sources of bias 8-11

Study size 10 Explain how the study size was arrived at 11

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If

applicable, describe which groupings were chosen and why

5-11

Statistical methods 12 (a) Describe all statistical methods, including those used to control for

confounding

5-11

(b) Describe any methods used to examine subgroups and interactions 11

(c) Explain how missing data were addressed n/a

(d) If applicable, explain how loss to follow-up was addressed 10

(e) Describe any sensitivity analyses 7,11

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers

potentially eligible, examined for eligibility, confirmed eligible,

included in the study, completing follow-up, and analysed

11, figure

2

(b) Give reasons for non-participation at each stage Figure 2

(c) Consider use of a flow diagram Figure 2

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical,

social) and information on exposures and potential confounders

11-12,

table 2

(b) Indicate number of participants with missing data for each variable

of interest

n/a

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2

(c) Summarise follow-up time (eg, average and total amount) 14, figure

3

Outcome data 15* Report numbers of outcome events or summary measures over time Figure 3

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted

estimates and their precision (eg, 95% confidence interval). Make clear

which confounders were adjusted for and why they were included

Table 3

(b) Report category boundaries when continuous variables were

categorized

Table 1

(c) If relevant, consider translating estimates of relative risk into

absolute risk for a meaningful time period

n/a

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions,

and sensitivity analyses

14-15,

table 3

Discussion

Key results 18 Summarise key results with reference to study objectives 16

Limitations 19 Discuss limitations of the study, taking into account sources of potential

bias or imprecision. Discuss both direction and magnitude of any

potential bias

16-17

Interpretation 20 Give a cautious overall interpretation of results considering objectives,

limitations, multiplicity of analyses, results from similar studies, and

other relevant evidence

18-20

Generalisability 21 Discuss the generalisability (external validity) of the study results 16-17, 20

Other information

Funding 22 Give the source of funding and the role of the funders for the present

study and, if applicable, for the original study on which the present

article is based

22

*Give information separately for exposed and unexposed groups.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and

published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely

available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is

available at http://www.strobe-statement.org.

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Prescriber Preference for a Particular Tumor Necrosis Factor Antagonist Drug and Treatment Discontinuation:


Journal: BMJ Open

Manuscript ID: bmjopen-2014-005532.R1

Article Type: Research

Date Submitted by the Author: 10-Sep-2014

Complete List of Authors: Fisher, Anat; University of British Columbia, Anesthesiology, Pharmacology & Therapeutics Basset, Ken; University of British Columbia, Anesthesiology, Pharmacology

& Therapeutics; University of British Columbia, Family Practice Wright, James (Jim); University of British Columbia, Anesthesiology, Pharmacology & Therapeutics; University of British Columbia, Medicine Brookhart, M.; University of North Carolina at Chapel Hill, Epidemiology Freeman, Hugh; University of British Columbia, Medicine Dormuth, Colin; University of British Columbia, Department of Anesthesiology, Pharmacology and Therapeutics

<b>Primary Subject Heading</b>:

Epidemiology

Secondary Subject Heading: Epidemiology, Health services research, Patient-centred medicine, Research methods, Rheumatology

Keywords: EPIDEMIOLOGY, RHEUMATOLOGY, Health policy < HEALTH SERVICES

ADMINISTRATION & MANAGEMENT


BMJ Open on June 29, 2020 by guest. P

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1

Prescriber Preference for a Particular Tumor Necrosis

Factor Antagonist Drug and Treatment Discontinuation:



















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ABSTRACT











Sensitivity analysis was conducted with different thresholds for higher preference.




analysis.




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• First study to explore within-physician variation in prescribing habits, specifically the



standardized approach to data collection in British Columbia, which ensured the


• To conquer the absence of access to clinical data, we used multiple proxy variables to


• Physician preference was not directly measured but instead based on previous prescribing

habits.

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INTRODUCTION


group among several alternatives, and it has been shown to predict treatment choice.1-4 In studies

of administrative health (claim) data, this preference is often determined by identifying


event of interest (a new prescribing). Despite an association with new prescribing decisions, the

role of physician preference in treatment discontinuation has not been studied. Recently, the term

‘preference’ has also been used to describe a second phenomenon – in the context of treatment

discontinuation, it was used to describe the baseline risk of discontinuing treatment in patients

treated by a specific physician (the physician ‘preference for discontinuation’). 5 This baseline

risk may differ among physicians because physicians may respond differently to similar clinical

situations such as decreased benefit or harmful events. They could recommend patients to

discontinue treatment (with or without switching to a second drug) or to persist with the

treatment (but to adjust dose, add-on a second drug or be under frequent watch). In this paper, we

use the term ‘preference’ to describe the first phenomenon (physician’s favorite drug) and

‘physician-specific discontinuation risk’ to describe the second.

Treatment with tumor necrosis factor alpha (TNF) antagonists in patients with rheumatoid

arthritis (RA) was considered especially sensitive to physician preference for two main reasons.

First, during the study period (2001-2009) there was limited clinical evidence on the comparative

effectiveness of the drugs, mainly due to the absence of head-to-head randomized clinical trials,

but also because participants in placebo-controlled trials were not representative of patients treated

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in routine clinical settings.6-9 Second, published indications for discontinuation of TNF

antagonists were vague and confusing, and therefore care-providing physicians could reasonably

be expected to reach different clinical decisions given the same clinical situation. Consequently,

the decisions about which TNF antagonist to prescribe first and when to discontinue treatment

were likely subject to physicians’ individual preference.

















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selected based on similar criteria to previous studies in British Columbia.10-12 either two












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assigning preference 1-4, we created a more accurate indicator. This way we minimized the effect

of factors not related to preference that might have influenced a specific prescribing decision.

Sensitivity analysis was conducted to examine the robustness of main results, using thresholds of

70% or 80% for the level of physician preference.







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Demographics







(Fair PharmaCare)

The deductible is based on annual income.13 Six categories: annual

deductible of $0, $1-500, $501-2250, >$2250, other programs and

no coverage


index




Clinical status



index date

Continuous variable




Four categories: 0, 1, 2, >2



pre-study period

Charlson comorbidity score14 was determined using Quan’s

algorithm for administrative databases,15 excluding rheumatic




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the data. RA disease duration captured in Canadian administrative

data has been found to agree with reported duration by physicians.16

Continuous variable




period




variable

Drug therapies

Concomitant MTX

during 200 days







Bernoulli variable

Number of different

antirheumatic drugs


study period






Other


date


practice17-20 and availability of drugs. Eight yearly categories were

included for the years 2001-2008


The outcome variable was discontinuation defined as either switching to another TNF antagonist

including certolizumab and golimumab, anakinra, rituximab or abatacept (‘biologic’

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supply of the latest refill. Discontinuation date was set to the end of the days-supply of the last

refill before the 180-days drug-free interval or the date of the first dispensing of a second

‘biologic’ antirheumatic drug, whichever was earliest. Unless discontinuation occurred, patients

were followed up until either December 31, 2009 (end of follow-up period) or an interruption of

more than six days in the provincial MSP coverage, at which point data were considered

censored. The most common causes of coverage interruptions were death and emigration from

the province. Change of dose of the TNF antagonist or addition of a second drug (not TNF

antagonist) was not considered discontinuation.










‘higher’ preference relative to courses with ‘lower’ preference is 0.8, we required 847 courses at

each preference level to be able to reject the null hypothesis with type I error probability of 0.05

and power of 0.80.

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Patients’ characteristics were compared across the three drugs. The adjusted risk of

discontinuation was compared using two different approaches in a Cox proportional hazards

regression. First, we conducted an analysis of nonclustered data. The multivariate model also

included a series of Bernoulli variables, one for each physician (1=the particular physician;

0=other physicians), which allowed individualization of the risk of drug discontinuation by

physician (physician-specific discontinuation risk). In the second approach, a marginal model of

clustered data allowed an adjustment for possible correlation between patients treated by the

same physician.21-23 We tested for model assumptions (proportional hazard and absence of

interactions) and found them to be valid. We also checked for the linearity of continuous

variables and categorized non-linear variables. All statistical tests were two-sided. All

calculations were performed using the SAS software package (SAS Institute Inc., Cary, NC).

RESULTS



analysis. Baseline characteristics across users of the three drugs are presented in Table 2. Not

only was etanercept was the most frequently prescribed drug (1718 patients, 63%), but it was

also the only drug prescribed by all 58 study physicians. Etanercept was usually initiated by a

physician with high preference for etanercept (70% of etanercept courses). Infliximab or

adalimumab, on the other hand, were usually initiated by physicians with lower preference for

these drugs (only 34% or 19% of courses were initiated by a physician with high preference for

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drug, respectively). Patients treated with adalimumab were significantly older and had a lower

income (reflected in lower annual deductible level for prescribing costs). Patients treated with

infliximab had the highest prevalence of concomitant methotrexate therapy and patients with

etanercept the lowest. This reflects differences in the indications mentioned in the product

monographs; while infliximab is indicated for use in combination with methotrexate,24 the

monograph of etanercept mentioned that it “can be initiated in combination with methotrexate …

or used alone”.25 Similarly, while the provincial special authority policy requires that infliximab

is used in combination with methotrexate (or other drug), such requirement does not exist for

treatment with etanercept or adalimumab.26

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(between

groups

differences)


cohort)

571 (21) 453 (16) 1718 (63)




49 46 58



193 (34) 84 (19) 1198 (70) <0.0001

Demographics

Females, No (%) 403 (71) 326 (72) 1239 (72) 0.77

Age at index, median y (range) 56 (18-87) 58 (22-91) 56 (18-92) 0.003


cost, No (%)

Very low ($0) 47 (8.3) 80 (18) 199 (12) <0.0001

Low ($1-500) 33 (5.8) 53 (12) 152 (8.9) 0.004

Medium ($501-2250) 92 (16) 109 (24) 315 (18) 0.004

High (>$2250) 35 (6.1) 38 (8.4) 143 (8.3) 0.22



341 (60) 224 (50) 782 (46) <0.0001

Clinical status


(range)

33 (3-158) 31 (2-112) 32 (3-136) 0.25


No (%)

104 (18) 63 (14) 340 (20) 0.01


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(%)


No (%)

113 (20) 95 (21) 383 (22) 0.41

RA disease duration median, y

(range)

9.2 (0.1-

17.9)

7.7 (0.3-17.9) 8.0 (0-17.8) 0.21

RA drugs



No (%)

307 (54) 214 (47) 923 (54) 0.04



4 (0-8) 4 (0-8) 4 (0-9) 0.46







with median time to discontinuation of 4.28 years (confidence interval 3.70-4.90) in 1475

courses with ‘high’ preference, compared with 3.27 (2.85-3.84) in 1267 courses with ‘low’

preference. In both groups, about 50% of the patients discontinued and the other 50% were

censored.



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That may be a result of decreased numbers of patients treated with a drug of higher physician

preference and therefore decreased power to detect significant difference.


preference

Approach



60%


70%


80%


preference N (%)

1426 (52) 1234 (45) 987 (36)

Nonclustered

data analysis

Crude 0.88 (0.79-0.98) 0.89 (0.80-0.99) 0.83 (0.81-0.996)

Adjusted* 0.86 (0.76-0.98) 0.88 (0.77-1.01) 0.87 (0.75-1.004)

Marginal

modeling of

clustered data

Crude 0.88 (0.77-1.001) 0.89 (0.78-1.01) 0.89 (0.79-1.01)

Adjusted# 0.85 (0.76-0.96) 0.87 (0.78-0.97) 0.87 (0.78-0.98)


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DISCUSSION

We demonstrated two types of variations of physician decisions to discontinue treatment with

TNF antagonists: between-physician and within-physician. Between-physician variability is

expressed as a different baseline risk of discontinuation depending on the treating physician in

response to similar clinical situations, such as decreased benefit or a harmful event. We used the

term physician-specific discontinuation risk for this phenomenon. It could be a result of

differences in education or experience, adherence to different guidelines and possible differences

in patient case mix. Within-physician variability, on the other hand, is a different response to a

similar clinical situation (similar patients, similar drug effects) by the same physician. In this

study we showed that this variability correlated to physician preference for drug – and

specifically, higher physician preference for a specific TNF antagonist drug was associated with

a decreased risk of discontinuing TNF antagonists in RA patients. This preference most probably

reflected the physician’s beliefs regarding the relative effectiveness and safety of the specific

drug compared to the alternatives, where a preferred drug is thought to be superior. When a

physician prescribed a drug believed to be superior compared to comparators, patients were

encouraged to stay longer on the drug and the risk of discontinuation, as measured in this study,

decreased. We believe that a different response (level of encouragement) to a similar clinical

situation applied in indefinite clinical situations, such as mild harmful effect or questionable

benefit.



and a systematic and standardized approach to data collection in British Columbia, which

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ensured the generalizability of our results, as well as the large sample and prolonged follow up

that increased the power of the study to detect differences in discontinuation risk. The main










and prescribing habits were shown to correlate in previous research.27 Third, we measured







curable diseases, such as RA.28 In support of this hypothesis, previous studies in RA patients

using TNF antagonists demonstrated that the main reasons for discontinuing or switching were

decreased benefit (36-67% of the discontinuations) or perceived harm (30-58%).29-34 As such, the

risk of discontinuing treatment is assumed to be influenced solitarily by drug properties and

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patient characteristics. The demonstrated effect of care-provider characteristics, specifically

prescribing habits, on the risk of discontinuation calls into question this hypothesis.


discontinuation risk) have previously been studied in RA patients treated by TNF antagonists.

Differences in response to harm were reported by Cush et al 2005 based on an online survey of

rheumatologists 1 and in response to decreased benefit – by Zhang et al 2011 5. Using mixed

effect model with a random intercept to cluster patients at physician level, Zhang demonstrated

the importance of clustering by physicians, even after adjustment for both baseline disease

activity and improvement in disease activity.



been previously studied in this therapeutic class or in other conditions, physician preference was



antagonists compared to the alternative: prescribing synthetic antirheumatic drugs 1 and in other

treatment situations.3, 4, 35 Physician preference for an individual TNF antagonist was studied by

Kamal et al 2006.36 In response to mailed questionnaires, most American rheumatologists said

they preferred etanercept over adalimumab or infliximab and considered etanercept the most

efficacious of the three drugs with less harm. In our study, etanercept was the most prescribed

TNF drug, and it was prescribed by all 58 medium to high-volume prescribers.


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explained in light of the theory of cognitive dissonance.37 Dissonance is an uncomfortable










a drug or higher relative cost (although during the study period, availability or cost were not

factors). Direct measures of cognitive dissonance are complicated and involve activities beyond

the scope of the current study.38 Second, the observed association between higher preference and

decreased risk of discontinuation might be confounded by experience with the drug, namely the

total number of patients a physician has previously treated with the same drug. Increased

experience with a specific drug may be associated with higher preference as a result of the

algorithm we used to assign the level of preference, but also because of a tendency to continue

doing what is familiar. Theoretically, increased experience with a specific drug would improve

patient selection, and therefore is associated with improved benefit, decreased harm and

decreased risk of discontinuation in these patients. Lastly, a physician’s stated preference for a

particular drug has been shown to correlate with patient preference. The selection of a specific

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TNF antagonist mostly depends on physician and/or patient preference because the benefit and

harm profiles of the three drugs are considered to be similar39-41 despite limited relative

effectiveness data. Physician and patient preferences for an individual TNF antagonist are likely

to be correlated, as was shown in studies of nonsteroidal anti-inflammatory drug (NSAID)

treatment in RA patients.42, 43 In regard to TNF antagonists, studies have showed that patients

preferred shared treatment decisions or responsibility of the health professional when choosing a

TNF antagonist.44 In addition, 67% of 77 Canadian rheumatologists surveyed reported

concordance with the patient preference more than 80% of the time.44 In addition, 67% of 77

Canadian rheumatologists surveyed reported concordance with the patient preference more than

80% of the time.45 If physician and patient preference for TNF antagonist do correlate, then

improved persistence may have been a result of higher patient rather than physician preference.







in policy alone would be less than desired. In a similar manner, the study indicates that the

pharmaceutical industry may have a strong interest in influencing physicians despite

administrative restrictions.46


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association observed, such as measuring cognitive dissonance during prescribing decision38 or



development of education programs for physicians. We also suggest exploring whether patients

who had been initiated on a non-preferred TNF inhibitor were more likely to be switched to a

preferred TNF antagonist. Further research is warranted to identify effective approaches to

policy implementations.

CONCLUSIONS




in the physicians’ drug preference. Similar research on other treatments is warranted.

Contributors: All authors contributed to the study conception and design and interpretation of the

data, drafting and revising the article critically for important intellectual content, and gave final

approval of the version to be published. Analysis was performed as part of research conducted

under the supervision of CD as part of the requirements for AF’s PhD project.

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Funding: The authors declare that (1) the study was supported by the University of British

Columbia Graduate Fellowship and a grant to the University of British Columbia from the

British Columbia Ministry of Health. The publication of study results was not contingent on their









is low. Statistical code is available from the corresponding author.

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the art. NATO ASI series. Series E, Applied sciences ed. Boston, Massachusetts, U.S.: Kluwer

Academic Publishers; 1992. p. 237-47.

22. Statistical analysis of clustered data using SAS® system (NESUG 2006). Available at:

http://www.nesug.org/proceedings/nesug06/an/da01.pdf. Accessed November 12, 2010.

23. Analysis of Survival Data with Clustered Events (paper 237-2009). Available at:

http://support.sas.com/resources/papers/proceedings09/237-2009.pdf;. Accessed November 12,

2010.

24. Remicade® (infliximab) in eCPS. Accessed October 24, 2008.

25. Enbrel® (Etanercept) in eCPS. Accessed June 03, 2009.

26. Special Authority request; Abatacept/ Adalimumab/ Certolozumab/ Etanercept/ Golimumab/

Infliximab Rheumatoid Arthritis (Initial or Switch). Available at:

https://www.health.gov.bc.ca/exforms/pharmacare/5345fil.pdf. Accessed May 10, 2012.

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27. Mark TL, Swait J. Using stated preference and revealed preference modeling to evaluate

prescribing decisions. Health Econ 2004; Jun;13(6):563-73.

28. Wolfe F. The epidemiology of drug treatment failure in rheumatoid arthritis. Baillieres Clin

Rheumatol 1995; Nov;9(4):619-32.

29. Hyrich KL, Lunt M, Watson KD, Symmons DPM, Silman AJ. Outcomes after switching



Arthritis Rheum 2007; Jan;56(1):13-20.

30. Carmona L, Gomez-Reino JJ, on behalf of the BIOBADASER Group. Survival of TNF


registry BIOBADASER. Arthritis Res Ther 2006; Apr;8(3):R72.

31. Hetland ML, Christensen IJ, Tarp U, Dreyer L, Hansen A, Hansen IT, et al. Direct




2010; Jan;62(1):22-32.

32. Fernandez-Nebro A, Irigoyen MV, Urena I, Belmonte-Lopez MA, Coret V, Jimenez-Nunez


(TNF) therapies in anti-TNF-naive rheumatoid arthritis. J Rheumatol 2007; Dec;34(12):2334-42.

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33. Kievit W, Fransen J, Adang EMM, den Broeder AA, Bernelot Moens HJ, Visser H, et al.


from the Dutch Rheumatoid Arthritis Monitoring Register. Rheumatology (Oxford) 2011;

Jan;50(1):196-203.

34. Filippini M, Bazzani C, Favalli EG, Marchesoni A, Atzeni F, Sarzi-Puttini P, et al. Efficacy


observational study. Clin Rev Allergy Immunol 2010; Apr;38(2-3):90-6.

35. Baser O, Wang L, Xie L, Dysinger A, Gust C, Yuce H, et al. Deriving doctors' prescribing

patterns from health care claims: An instrumental variable analysis [abstract]. Value Health

2010; November;13(7):A425.

36. Kamal KM, Madhavan SS, Hornsby JA, Miller L, Kavookjian J, Scott V. Use of tumor


rheumatologists. Joint Bone Spine 2006; Dec;73(6):718-24.

37. Festinger L. A theory of cognitive dissonance. Evanston, Ill.: Row, Peterson; 1957.

38. Visser PS, Cooper J. Dissonance and attitude change: A matter of measurement, in Chapter



39. Yazici Y, Simsek I. Treatment options for rheumatoid arthritis beyond TNF-alpha inhibitors.

Expert Rev Clin Pharmacol 2010; Sep;3(5):663-6.

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40. Dudler J, Moller B, Michel BA, Villiger PM. Biologics in rheumatoid arthritis (RA) -

Recommendations for Swiss practice. Swiss Med Wkly 2011;(w13189).

41. Lu TY, Hill C. Managing patients taking tumour necrosis factor inhibitors. Australian

Prescriber 2006; Jun;29(3):67-70.

42. Gall EP, Caperton EM, McComb JE, Messner R, Multz CV, O'Hanlan M, et al. Clinical


arthritis. J Rheumatol 1982; May-Jun;9(3):402-7.

43. Wasner C, Britton MC, Kraines RG, Kaye RL, Bobrove AM, Fries JF. Nonsteroidal anti-

inflammatory agents in rheumatoid arthritis and ankylosing spondylitis. Jama 1981; Nov

13;246(19):2168-72.

44. Chilton F, Collett RA. Treatment choices, preferences and decision-making by patients with

rheumatoid arthritis. Muscoskel Care 2008; Mar;6(1):1-14.

45. Rheumatoid Arthritis Patient Preference Survey (RAPPS) (Presentation number: 965).

Available at: http://acr.confex.com/acr/2006/webprogram/Paper5515.html;. Accessed December

05, 2011.

46. Spurling GK, Mansfield PR, Montgomery BD, Lexchin J, Doust J, Othman N, et al.

Information from pharmaceutical companies and the quality, quantity, and cost of physicians'

prescribing: a systematic review. PLoS Med 2010; Oct 19;7(10):e1000352.

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Figure 2: Participants’ flow


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Calculating the value of the independent variable physician preference 215x224mm (300 x 300 DPI)

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Participants’ flow

215x137mm (300 x 300 DPI)

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Persistence with TNF antagonists by physician preference levels 54x40mm (300 x 300 DPI)

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1

Prescriber Preference for a Particular Tumor Necrosis Factor Antagonist Drug and

Treatment Discontinuation: Population-based Cohort

STROBE Statement—Checklist of items that should be included in reports of cohort studies

Item

No Recommendation

Page

number

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or

the abstract

2

(b) Provide in the abstract an informative and balanced summary of

what was done and what was found

2-3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation

being reported

4-5

Objectives 3 State specific objectives, including any prespecified hypotheses 5

Methods

Study design 4 Present key elements of study design early in the paper 5

Setting 5 Describe the setting, locations, and relevant dates, including periods of

recruitment, exposure, follow-up, and data collection

5-6

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection

of participants. Describe methods of follow-up

5-6

(b) For matched studies, give matching criteria and number of exposed

and unexposed

n/a

Variables 7 Clearly define all outcomes, exposures, predictors, potential

confounders, and effect modifiers. Give diagnostic criteria, if applicable

5-11

Data sources/

measurement

8* For each variable of interest, give sources of data and details of

methods of assessment (measurement). Describe comparability of

assessment methods if there is more than one group

5-11

Bias 9 Describe any efforts to address potential sources of bias 8-11

Study size 10 Explain how the study size was arrived at 11

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If

applicable, describe which groupings were chosen and why

5-11

Statistical methods 12 (a) Describe all statistical methods, including those used to control for

confounding

5-11

(b) Describe any methods used to examine subgroups and interactions 11

(c) Explain how missing data were addressed n/a

(d) If applicable, explain how loss to follow-up was addressed 10

(e) Describe any sensitivity analyses 7,11

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers

potentially eligible, examined for eligibility, confirmed eligible,

included in the study, completing follow-up, and analysed

11, figure

2

(b) Give reasons for non-participation at each stage Figure 2

(c) Consider use of a flow diagram Figure 2

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical,

social) and information on exposures and potential confounders

11-12,

table 2

(b) Indicate number of participants with missing data for each variable

of interest

n/a

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(c) Summarise follow-up time (eg, average and total amount) 14, figure

3

Outcome data 15* Report numbers of outcome events or summary measures over time Figure 3

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted

estimates and their precision (eg, 95% confidence interval). Make clear

which confounders were adjusted for and why they were included

Table 3

(b) Report category boundaries when continuous variables were

categorized

Table 1

(c) If relevant, consider translating estimates of relative risk into

absolute risk for a meaningful time period

n/a

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions,

and sensitivity analyses

14-15,

table 3

Discussion

Key results 18 Summarise key results with reference to study objectives 16

Limitations 19 Discuss limitations of the study, taking into account sources of potential

bias or imprecision. Discuss both direction and magnitude of any

potential bias

16-17

Interpretation 20 Give a cautious overall interpretation of results considering objectives,

limitations, multiplicity of analyses, results from similar studies, and

other relevant evidence

18-20

Generalisability 21 Discuss the generalisability (external validity) of the study results 16-17, 20

Other information

Funding 22 Give the source of funding and the role of the funders for the present

study and, if applicable, for the original study on which the present

article is based

22

*Give information separately for exposed and unexposed groups.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and

published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely

available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is

available at http://www.strobe-statement.org.

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Prescriber Preference for a Particular Tumor Necrosis

Factor Antagonist Drug and Treatment Discontinuation:



















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ABSTRACT











Sensitivity analysis was conducted with different thresholds for higher preference.




analysis.




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• First study to explore within- physician variation in prescribing habits, specifically the



standardized approach to data collection in British Columbia, which ensured the


• To conquer the absence of access to clinical data, we used multiple proxy variables to


• Physician preference was not directly measured but instead based on previous prescribing

habits. and not directly measured

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INTRODUCTION


group among several alternatives, and it has been shown to predict treatment choice.1-4 In studies

of administrative heath health (claim) data, this preference is often determined by identifying


event of interest (a new prescribing). Despite an association with new prescribing

decisionsdesicions, the role of physician preference in treatment discontinuation has not been

studied. Recently, the term ‘preference’ has also been used to describe a second phenomenon –

in the context of treatment discontinuation, it is was used to describe the baseline risk of

discontinuing treatment in patients treated by a specific physician (the physician ‘preference for

discontinuation’). 5 This baseline risk may differ among physicians because physicians may

respond differently to similar clinical situations such as decreased benefit or harmful events.

They could recommend patients to discontinue treatment (with or without switchingswitch to a

second drug) or to persist with the treatment (but to adjust dose, add-on a second drug or be

under frequent watch). In this paper, we use the term ‘preference’ to describe the first

phenomenon (physician’s favorite drug) and ‘physician-specific discontinuation risk’ to describe

the second.

Treatment with tumor necrosis factor alpha (TNF) antagonists in patients with rheumatoid

arthritis (RA) was considered especially sensitive to physician preference for two main reasons.

First, during the study period (2001-2009) there was limited clinical evidence on the comparative

effectiveness of the drugs, mainly due mostly to the absence of head-to-head randomized clinical

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trials, but also because participants in placebo-controlled trials were not representative of patients

treated in routine clinical settings.6-9 Second, published indications for discontinuation of TNF

antagonists were vague and confusing, and therefore care-providing physicians could reasonably

be expected to reach different clinical decisions given the same clinical situation. Consequently,

the decisions about which TNF antagonist to prescribe first and when to discontinue treatment

were likely subject to physicians’ individual preference.
















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selected based on similar criteria to previous studies in British Columbia.10-12 either two











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assigning preference 1-4, we created a more accurate indicator. This way we minimized the effect

of factors not related to preference that might have influencedinfluence a specific prescribing

decision. Sensitivity analysis was conducted to examine the robustness of main results, using

thresholds of 70% or 80% for the level of physician preference.






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Demographics







(Fair PharmaCare)

The deductible is based on annual income.13 Six categories: annual

deductible of $0, $1-500, $501-2250, >$2250, other programs and

no coverage


index




Clinical status



index date

Continuous variable




Four categories: 0, 1, 2, >2



pre-study period

Charlson comorbidity score14 was determined using Quan’s

algorithm for administrative databases,15 excluding rheumatic


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the data. RA disease duration captured in Canadian administrative

data has been found to agree with reported duration by physicians.16

Continuous variable




period




variable

Drug therapies

Concomitant MTX

during 200 days







Bernoulli variable

Number of different

antirheumatic drugs


study period






Other


date


practice17-20 and availability of drugs. Eight yearly categories were

included for the years 2001-2008


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The outcome variable was a discontinuation defined as either switching to another TNF

antagonist including certolizumab and golimumab, anakinra, rituximab or abatacept (‘biologic’


supply of the latest refill. DiscontinuationThe discontinuation date was set to the end of the days-

supply of the last refill before the 180-days drug-free intervaldiscontinuation or the date of the

first dispensing of a second ‘biologic’ antirheumatic drug, whichever was earliest. Unless

experienced discontinuation occurred, patients were followed up until either December 31, 2009

(end of follow-up period) or until an interruption of more than six days in the provincial MSP

coverage, at which point data were considered censored. The most common causes of

coveragesuch interruptions were death and emigration from the province. Change of dose of the

TNF antagonist or addition ofadding-on a second drug (not TNF antagonist) was not considered

discontinuation, and patients were continued to be followed.










‘higher’ preference relative to courses with ‘lower’higher’ preference is 0.8, we required 847

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11

courses atwith each preference level to be able to reject the null hypothesis with type I error

probability of 0.05 and power of 0.80.


Patients’Summary statistics of baseline characteristics were compared across the three drugsdrug

groups. The adjusted risk of discontinuation was compared using two different approaches in a

Cox proportional hazards regression. First, we conducted an analysis of nonclustered data. The

multivariate model also included a series of Bernoulli variables, one for each physician (1=the

particular physician; 0=other physicians), whichthat allowed individualization of the risk of drug

discontinuation by physician (physician-specific discontinuation risk). In the second approach, a

marginal model of clustered data allowed an adjustment for possible correlation between patients

treated by the same physician.21-23 We tested for model assumptions (proportional hazard and

absence of interactions) and found them to be valid. We also checked for the linearity of

continuous variables and categorized non-linear variables. All statistical tests were two-sided.

All calculations were performed using the SAS software package (SAS Institute Inc., Cary, NC).

RESULTS



analysis. Baseline characteristics across users of the three drugsdrug groups are presented in

Table 2. Not only was etanercept was the most frequently prescribed drug (1718 patients, 63%),

but also it was also the only drug prescribed by all 58 study physicians. Etanercept was usually

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initiated by a physician with high preference for etanercept (70% of etanercept courses).

Infliximab or adalimumab, on the other hand, were usually initiated by physicians with lower

preference for these drugs (only 34% or 19% of courses were initiated by a physician with high

preference for drug, respectively). Patients treated with adalimumab were significantly older and

had a lower income (reflected in lower annual deductible level for prescribing costs). Patients

treated with infliximab had the highest prevalence of concomitant methotrexate therapy and

patients with etanercept the lowest. This reflects differences in the indications mentionedmention

in the product monographs; while infliximab is indicated for use in combination with

methotrexate,24 the monograph of etanercept mentioned that it “can be initiated in combination

with methotrexate … or used alone”.25 Similarly, while the provincial special authority policy

requires that infliximab is used in combination with methotrexate (or other drug),) and such

requirement does not exist for treatment with etanercept or adalimumab.26

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(between

groups

differences)


cohort)

571 (21) 453 (16) 1718 (63)




49 46 58



193 (34) 84 (19) 1198 (70) <0.0001

Demographics

Females, No (%) 403 (71) 326 (72) 1239 (72) 0.77

Age at index, median y (range) y 56 (18-87) 58 (22-91) 56 (18-92) 0.003


cost, No (%)

Very low ($0) 47 (8.3) 80 (18) 199 (12) <0.0001

Low ($1-500) 33 (5.8) 53 (12) 152 (8.9) 0.004

Medium ($501-2250) 92 (16) 109 (24) 315 (18) 0.004

High (>$2250) 35 (6.1) 38 (8.4) 143 (8.3) 0.22



341 (60) 224 (50) 782 (46) <0.0001

Clinical status


(range)

33 (3-158) 31 (2-112) 32 (3-136) 0.25


No (%)

104 (18) 63 (14) 340 (20) 0.01


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(%)


No (%)

113 (20) 95 (21) 383 (22) 0.41

RA disease duration median, y

(range) y

9.2 (0.1-

17.9)

7.7 (0.3-17.9) 8.0 (0-17.8) 0.21

RA drugs



No (%)

307 (54) 214 (47) 923 (54) 0.04



4 (0-8) 4 (0-8) 4 (0-9) 0.46







with median time to discontinuation of 4.28 years (confidence interval 3.70-4.90) in 1475

courses with ‘high’ preference, compared with 3.27 (2.85-3.84) in 1267 courses with ‘low’

preference . In both groups, about 50% of the patients discontinued and the other 50% were

censored.



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That may be a result of decreaseddecreasing numbers of patients treated with a drug of higher

physician preference and therefore decreased power to detect significant difference.


preference

Approach



60%


70%


80%


preference N (%)

1426 (52) 1234 (45) 987 (36)

Nonclustered

data analysis

Crude 0.88 (0.79-0.98) 0.89 (0.80-0.99) 0.83 (0.81-0.996)

Adjusted* 0.86 (0.76-0.98) 0.88 (0.77-1.01) 0.87 (0.75-1.004)

Marginal

modeling of

clustered data

Crude 0.88 (0.77-1.001) 0.89 (0.78-1.01) 0.89 (0.79-1.01)

Adjusted# 0.85 (0.76-0.96) 0.87 (0.78-0.97) 0.87 (0.78-0.98)


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DISCUSSION

We demonstrated two types of variations of physician decisions to discontinue treatment with

TNF antagonists: between-physician and within-physician. Between-physician variability in

response to similar clinical situations, such as decreased benefit or a harmful event is expressed

as a different baseline risk of discontinuation, depending on the treating physician in response to

similar clinical situations, such as decreased benefit or a harmful event. We used the term

physician-specific discontinuation risk for this phenomenon. It could be a result of differences in

education or experience, adherence to different guidelines and possible differences in patient

case mix. Within-physician variability, on the other hand, is a different response to a similar

clinical situation (similar patients, similar drug effects) by the same physician. In this study we

showed that this variability correlated to physician preference for drug – and specifically, higher

physician preference for a specific TNF antagonist drug was associated with a decreased risk of

discontinuing TNF antagonists in RA patients. This preference most probably reflected the

physician’s beliefs regarding the relative effectiveness and safety of the specific drug compared

to the alternatives, where a preferred drug is thought to be superior. When a physician

prescribedtreated with a drug believed to be superior compared to comparators, patients were

encouraged to stay longer on the drug and the risk of discontinuation, as measured in this study,

decreased. We believe that a different response (level of encouragement) to a similar clinical

situation applied in indefinite clinical situations, such as mild harmful effect or questionable

benefit.


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and a systematic and standardized approach to data collection in British Columbia, which

ensured the generalizability of our results, as well as the large sample and prolonged follow up

that increased the power of the study to detect differences in discontinuation risk. The main










and prescribing habits were shown to correlate in previous research.27 Third, we measured







curable diseases, such as RA.rheumatoid arthritis28 In support of this hypothesis, previous studies

in RA patients using TNF antagonists demonstrated that the main reasons for discontinuing or

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switching were decreased benefit (36-67% of the discontinuations) or perceived harm (30-

58%).29-34 As such, the risk of discontinuing treatment is assumed to be influenced solitarily by

drug properties and patient characteristics. The demonstrated effect of care-provider

characteristics, specifically prescribing habits, on the risk of discontinuation calls into question

this hypothesis.


discontinuation risk) have previously been studiedstudies in RA patients treated by TNF

antagonists. Differences in response to harm were reported by Cush et al 2005 based on an

onlineon-line survey of rheumatologists 1 and in response to decreased benefit – by Zhang et al

2011 5. Using mixed effect model with a random intercept to cluster patients at physician level,

Zhang demonstrated the importance of clustering by physicians, even after adjustment for both

baseline disease activity and improvement in disease activity.



been previously studied, in this therapeutic class or in other conditions, physician preference was



antagonists compared to the alternative: prescribing synthetic antirheumatic drugs 1 and in other

treatment situations.3, 4, 35 Physician preference for an individual TNF antagonist was studied by

Kamal et al 2006.36 In response to mailed questionnaires, most American rheumatologists said

they preferred etanercept over adalimumab or infliximab and considered etanercept the most

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efficacious of the three drugs with less harm. In our study, etanercept was the most prescribed

TNF drug, and it was prescribed by all 58 medium to high-volume prescribers.




explained in light of the theory of cognitive dissonance.37 Dissonance is an uncomfortable










a drug or higher relative cost (although during the study period, availability or cost were not

factors).. Direct measures of cognitive dissonance are complicated and involve activities beyond

the scope of the current study.38 Second, the observed association between higher preference and

decreased risk of discontinuation might be confounded by experience with the drug, namely the

total number of patients a physician has previously treated with the same drug. Increased

experience with a specific drug may be associated with higher preference as a result of the

algorithm we used to assign the level of preference, but also because of a tendency to continue

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doing what is familiar. Theoretically, increased experience with a specific drug would improve

patient selection, and therefore is associated with improved benefit, decreased harm and

decreased risk of discontinuation in these patients. Lastly, a physician’s stated preference for a

particular drug has been shown to correlate with patient preference. The selection of a specific

TNF antagonist mostly depends on physician and/or patient preference because the benefit and

harm profiles of the three drugs are considered to be similar39-41 despite limited relative

effectiveness data. Physician and patient preferences for an individual TNF antagonist are likely

to be correlated, as was shownshowed in studies of nonsteroidal anti-inflammatory drug

(NSAID) treatment in RA of RA patients.42, 43 In regard to, although this was not studied in

patients treated with TNF antagonists, studies have showed that patients preferred shared

treatment decisions or responsibility of the health professional when choosing a TNF

antagonist.44. In addition, 67% of 77 Canadian rheumatologists surveyed reported concordance

with the patient preference more than 80% of the time.44 In addition, 67% of 77 Canadian

rheumatologists surveyed reported concordance with the patient preference more than 80% of the

time.45 If physician and patient preference for TNF antagonist do correlate, then improved

persistence may have been a result of higher patient rather than physician preference.







in policy alone would be less than desired. In a similar manner, the study indicates that the

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pharmaceutical industry may have a strong interest in influencing physicians despite

administrative restrictions.46





association observed, such as measuring cognitive dissonance during prescribing decision38 or



development of education programs for physicians. We also suggest exploring whether patients

who had been initiated on a non-preferred TNF inhibitor were more likely to be switched to a

preferred TNF antagonist. Further research is warranted to identifyidentifying effective policy

implementation approaches to policy implementations.

CONCLUSIONS




in the physicians’ drug preference. Similar research on other treatments is warranted.

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Contributors: All authors contributed to the study conception and design and interpretation of the

data, drafting and revising the article critically for important intellectual content, and gave final

approval of the version to be published. Analysis was performed as part of research conducted

under the supervision of CD as part of the requirements for AF’s PhD project.

Funding: : The authors declare that (1) the study was supported by the University of British

Columbia Graduate Fellowship and a grant to the University of British Columbia from the

British Columbia Ministry of Health. The publication of study results was not contingent on their









is low. Statistical code is available from the corresponding author.

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Figure 2: Participants’ flow


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Documents

BMJ Open · Setting: British Columbia administrative health data (inpatients, outpatients and pharmacy) Participants: 2,742 British Columbia residents who initiated a first course