s3-eu-west-1.amazonaws.com€¦ · Web viewComparative Analyses of Published Cost Effectiveness Models Highlight Critical Considerations Which Are Useful to Inform Development of

Comparative Analyses of Published Cost Effectiveness Models Highlight

Critical Considerations Which Are Useful to Inform Development of New

Models.

Rautenberg TA1,2, George G1, Bwana MB4, Moosa MS1, Pillay S1, McCluskey SM3, Aturinda I4, Ard K3, Muyindike W4, Moodley P1, Brijkumar J1, Johnson BA5, Gandhi RT3, Sunpath H1, Marconi VC6,7, Siedner MJ3.

1. Division of Medicine, University of KwaZulu-Natal, Durban, South Africa

2. Centre for Applied Health Economics, Griffith University, Nathan, Australia

3. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

4. Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda

5. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA

6. Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

7. Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

Address for correspondence

Tamlyn Rautenberg

Griffith University

School of Medicine,

Centre for Applied Health Economics,

170 Kessels Road,

Nathan Qld 4111

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Contents1 Search strategy and search terms..............................................................................4

2 Literature Search and Selection................................................................................6

3 List of publications reviewed (main papers and related publications)...........................9

3.1 Weinstein........................................................................................................9

3.1.1 Main paper [6]...........................................................................................9

3.1.2 Related and reviewed papers.......................................................................9

3.2 Corzillius.......................................................................................................10

3.2.1 Main paper [11].......................................................................................10

3.2.2 Related and reviewed papers [12]..............................................................10

3.3 Sendi.............................................................................................................10

3.3.1 Main paper [13].......................................................................................10

3.4 Levison.........................................................................................................10

3.4.1 Main paper [14].......................................................................................10

3.4.2 Related and reviewed papers.....................................................................10

3.5 Phillips..........................................................................................................10

3.5.1 Main paper [19].......................................................................................10

3.5.2 Related and reviewed papers.....................................................................10

4 Comparative analysis............................................................................................10

4.1 Intervention...................................................................................................10

4.2 Comparator....................................................................................................11

4.3 Decision question...........................................................................................11

4.4 Reference case...............................................................................................12

4.5 Model scope...................................................................................................12

4.6 Model structure..............................................................................................13

4.7 Cycle duration:...............................................................................................14

4.8 Model perspective:.........................................................................................14

4.9 Prevalence of WT:..........................................................................................15

4.10 Modelled population:...................................................................................15

4.11 Viral load threshold determining failure:........................................................16

4.12 ART...........................................................................................................16

4.13 Opportunistic Infection................................................................................17

4.14 Modelling HIV-related and non-HIV-related mortality....................................17

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4.15 Resource use and valuation..........................................................................17

4.16 Model output measure:................................................................................19

4.17 Quality of life (continued from main paper)...................................................20

4.18 Raw results.................................................................................................20

4.19 Willingness to pay threshold: Reported results and standardization to 2017 USD20

4.20 Types of SA performed:...............................................................................34

4.21 Variables explored:......................................................................................35

5 Reference List......................................................................................................43

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1 Search strategy and search termsThe following MESH and search terms were used.

HIV

Mesh Terms Aditional Search Terms

Acquired Immunodeficiency Syndrome/drug therapy[Mesh]

Human Immunodeficiency Virus*[tiab]

Acquired Immunodeficiency Syndrome/economics[Mesh]

Acquired Immune Deficiency Syndrome[tiab]

Acquired Immunodeficiency Syndrome/virology[Mesh]

Acquired Immunodeficiency Syndrome[tiab]

HIV/drug effects[Mesh] Acquired Immuno-Deficiency Syndrome[tiab]

HIV/analysis[Mesh] AIDS[tiab]

HIV/genetics[Mesh] HIV[tiab]

HIV Infections/drug therapy[Mesh] HIV infect*[tw]

HIV Infections/economics[Mesh] HIV seropositivity[tw]

HIV Infections/virology[Mesh] HIV seronegativity[tw]

Anti-HIV Agents/economics[Mesh] Antiretroviral therap*[tw]

Anti-HIV Agents/therapeutic use[Mesh] Antiretroviral agent*[tw]

Antiretroviral Therapy, Highly Active/economics[Mesh]

Antiretroviral drug*[tw]

Antiretroviral Therapy, Highly Active/therapeutic use[Mesh]

Resistance

Mesh terms Additional Search Terms

Drug Resistance, Viral[Mesh] Resistance[tiab]

Viral Load[Mesh] Viral load[tw]

Treatment Failure[Mesh] CD4 count[tw]

CD4 lymphocyte count[tw]

Antiretroviral resistance*[tw]

Viral resistance[tw]

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Mesh terms Additional Search Terms

Virological failure*[tw]

Virologic failure*[tw]

Treatment failure*[tw]

Drug resistance[tw]

Drug resistant[tw]

Resistance mutation*[tw]

NNRTI resistance[tw]

Genotype resistance[tw]

Phenotypic Resistance[tw]

Testing

Mesh terms Other Search Terms

Genotype/analysis[Mesh] Test[tiab]

Genotyping Techniques/economics[Mesh] Tests[tiab]

Genotyping Techniques/methods[Mesh] Testing[tiab]

Resistance test*[tw]

Resistance assay*[tw]

Resistance monitoring[tw]

Resistance analys*[tw]

Genotype-resistance test*[tw]

Genotype testing[tw]

Genotype test*[tw]

Resistance measur*[tw]

Costs and economics

Mesh terms Other search terms

Economics[Mesh] Economic[tiab]

Costs and Cost Analysis[Mesh] Economics[tiab]

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Mesh terms Other search terms

Cost-Benefit Analysis[Mesh] Cost[tiab]

Cost of Illness[Mesh] Costs[tiab]

Health Care Cost[Mesh] Costing[tiab]

Health Expenditures[Mesh] Testing cost*[tw]

Markov Chains[Mesh] Cost analys*[tw]

Models, Economic[Mesh] Budget impact[tw]

Cost-Benefit Analysis[Mesh] Cost-effectiveness[tw]

Monte Carlo Method[Mesh] Cost-effective[tw]

Decision Trees[Mesh] Cost-benefit[tw]

Cost-utility[tw]

Markov[tw]

Monte Carlo[tw]

Decision Tree*[tw]

2 Literature Search and Selection

Literature search was performed on 25 January 2017 searching Medline (Pubmed) and the

Cochrane NHS Economic Evaluation Database (NHSEED) databases. Search terms and

MESH terms for Human Immunodeficiency Virus, resistance testing and economics were

combined. No publication limits were applied. Publications had to focus on the population of

HIV/AIDS, the intervention of resistance testing and specifically after first line failure

(secondary resistance) (i.e. not prior to start of ART or after 2L failure). The comparator was

no resistance testing, and the outcome was cost-consequence. Only studies meeting the

criteria of a full economic evaluation set out by Drummond (2005) were included in the

critical appraisal [1] namely a cost effectiveness, cost utility, or cost benefit approach with or

without modelling which includes cost and consequences. In addition, a distinction is made

between intermediate and final outcomes ‘(consequences). According to the range of possible

outcomes for HIV, intermediate outcomes include viral load suppression, resistance status

(resistance positives, resistance negatives) whereas final endpoints are mortality and/or

morbidity. Therefore, studies which include final outcomes are considered important. In

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accordance with the general recommendation to “choose an effectiveness measure relating to

a final output”[1].

A total of 1024 titles (pubmed n=883; NHSEED n=141) were identified. Two reviewers (TR,

GG) performed title/abstract review and 61 title/abstracts were selected for full text review of

which nine were included for deeper review as described below, after which four studies

were excluded as described below. The PRISMA diagram is provided in Figure 1.

Figure 1: PRISMA diagram

Reasons for exclusion of the 963 studies are is shown in Error: Reference source not found.

Rosen et al appears to be a cost minimization model which has an intermediate “outcome”

component because it reports the proportion of patients with virological failure, non-resistant

virological failures and second line switchers at five years. It is noted that these are the result

of the transition probabilities of virological failure and non-resistant virological failures

passing through the three strategies [2]. The decision analytic markov model appears to

model 8,500 patients (based on the Themba Lethu clinical cohort) at 6 monthly cycles

through a time horizon of five years [2]. Three strategies are explored: standard of care (no

resistance testing); resistance testing switching resistors and limited resistance testing within

the first three years [2].

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Similarly, the study by Chaix 2000 entitled “economic evaluation of drug resistance

genotyping for the adaptation of treatment in HIV-infected patients in the VIRADAPT study”

appears to be a cost minimization analysis rather than an economic evaluation. Despite the

title, the authors explain that “in the absence of results on survival, we did not attempt a cost-

effectiveness analysis, but a comparison of the cost in both groups (cost-minimization

analysis)” [3]. The study appears to describe a cost comparison of the two arms in the

VIRADAPT study, a study which compares genotype RT versus standard of care in a health

care setting in France [3]. Again, there seemed to be an outcome component namely viral

load reduction and percentage of patients with HIV RNA level below detection [3].

A study in 2014 by Phillips seeks to evaluate the value of resistance testing prior to ART [4]

and therefore was excluded.

Pham 2016 is an epidemiological model and it’s focus is on transmission of resistance rather

than evaluating the economic outcome of GART testing [5].

These four papers were therefore excluded from the comparative analysis. The table below

shows more detailed exclusion reasons for the 963 publications retrieved from pubmed (825)

and NHSEED (138).

Table 1: Exclusion reasons for 963 publications retrieved (825 pubmed+138 NHSEED):

Number of

studies

Reason Explanation

684 Study type Not the correct type of study, for example

not an economic evaluation but rather an

RCT, cost benefit analysis, cost of illness,

review, epidemiological model, statistical

model

262 Intervention Not GART testing, rather gene therapy,

CD4 testing, Point of care testing, HIV

screening

1 Timing of the Example primary testing prior to treatment.

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intervention

16 duplicates Slipped through deduplication

3 List of publications reviewed (main papers and related publications).

3.1 Weinstein

3.1.1 Main paper [6]

Supplementary appendix (at the end of the main paper).

3.1.2 Related and reviewed papers

It is not explicitly stated, however this appears to be the Cost-Effectiveness of Preventing

AIDS Complications (CEPAC) International model of human immunodeficiency virus

(HIV). One of the reasons which allude to this is the 2007 paper by Walensky, Weinstein’s

paper is referenced in the sentence “details of both models have been published elsewhere”

[7]. Also in Weinstein’s own paper he references two papers by Freedberg during the

description of the model [8]. “ We extrapolated from the results of two randomized clinical

trials, which compared clinical judgment informed by GART after HAART failure with

clinical judgment alone, by using a computer simulation model of HIV [8, 9].” The works

seem to be covered under the same grant numbers: Weinstein 2001 “By the National Institute

of Allergy and Infectious Diseases (R01-AI42006) and the Centers for Disease Control and

Prevention (U64/CCU 114927)”[6]. Freedberg 2001: Supported in part by grants from the

National Institute of Allergy and Infectious Diseases (R01-AI42006) and the Centers for

Disease Control and Prevention (U64/CCU 114927) to the Cost-Effectiveness of Preventing

AIDS Complications project, and by a grant from the Adult AIDS Clinical Trials Group

(UO1 AI38838)”[8].

[8], [10] ,[7] ,[9]

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3.2 Corzillius


3.2.2 Related and reviewed papers [12]

3.3 Sendi


Supplementary Table: Table S1. Transition probability matrix for HIV disease progression

generated by pooling consecutive six-month observations from patients on HAART enrolled

in the SHCS between 1996-2004.

3.4 Levison


Supplementary appendix


The CEPAC model itself is widely published and the review the related descriptions [8, 15-

18]

3.5 Phillips


Supplementary Methods and Results


The HIV Synthesis model appears to be first described in 2007 [20] and subsequently in 2008

[21]; 2011[22] along with three supplementary appendices and publications in 2013 and 2014

[23, 24] and supplementary appendix.

The following provides some further details of the comparisons of the fit of the model to

observed data[23, 24].

4 Comparative analysis

4.1 Intervention

For successful comparative analysis the intervention (GART) needs to be comparable in all

the models. Due the selection criteria of the systematic review, the intervention in all the

models is the same serving as a good basis for comparative analysis. All models include at

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least the evaluation of GART after 1L treatment failure (not further referenced in this

supplement) [6, 11, 13, 14, 19]. Weinstein explicitly combines GART “plus clinical

judgement”. It is not explicitly stated but implicitly assumed that Corzillius, Sedni, Levison

and Phillips model GART as the intervention coupled with clinical judgement.

4.2 Comparator

Best practice in modelling informs that the most appropriate comparator for a model is

current standard of care and that modelers need good grounds to select an alternate

comparator because selection of comparator may have a “major impact” on

effectiveness/efficacy (Roberts et al. 2012b, 2012a) and cost outcomes (Welte et al. 2004).

The exception is modelling alongside an RCT, when the comparators are defined by the trial.

It is recognised that “no treatment” comparators are not necessarily the least costly, however

it is also true that controlling for all other parameters, comparators which are relatively

inexpensive will increase the likelihood of making interventions appear relatively costly and

therefore less cost effective. Weinstein’s comparator is clinical judgement alone, which

Corzillius names “conventional wisdom”, Sendi “expert opinion” and Levison as “no

GART”. The terms clinical judgement, conventional wisdon and expert opinion are used

interchangeably to reflect the clinical training and expertise of clinicians who interpret the

findings of GART and apply appropriate therapy changes to patients. In the case of the

comparator the same terms apply when carrying out usual standard of care without GART.

The clinical evidence which informs the knowledge base/clinical expertise of clinicians has

arguably evolved over the last two decades as our understanding and evidence surrounding

HIV in the literature has also updated. This will nevertheless vary from country to country

according to medical training and clinical experience. Countries with comparatively

advanced healthcare systems are more likely to follow national or international clinical

practice guidelines. In contrast, in countries where healthcare systems are overburdened and

under-resourced there are many contributing factors affecting clinical judgement. However it

is not expected that this variation in clinical judgement will influence the model results.

Phillips compares all strategies to “no monitoring” (rather than no GART). This may have an

impact on the model results, as decribed in the discussion section of the main manuscript.

4.3 Decision question

The decision question that Phillips appears to address is: does it matter if GART is added to a

number of existing monitoring strategies? The other models seems to address the decision

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question: will patients who test positive for resistence do better if they switch to 2L from a

cost effectiveness perspective? So Phillips seems to be addressing a slightly different

question to the other models, nevertheless it remains included in this analysis because it

meets the inclusion criteria.

4.4 Reference case

One of the objectives of the PCEHM reference case is to improve the comparability of

models (Gold 1996; Neumann et al. 2016). Weinstein et al and Corzillius followed reference

case recommendations, none of the other models explicitly use a reference case. The

comparability of Weinstein and Corzillius is improved due to the use of a reference case,

comparability of Sendi, Levison and Phillips is more challenging.

4.5 Model scope

To compare the scope of the models an exhuastive cascade of HIV disease/treatment was

defined to create a visual representation of comparative scopes shown in Figure 2.

Figure 2: Cascade of HIV care to illustrate model scope.

Notes: Weinstein considered salvage therapy in the sensitivity analysis but options beyond 2L

were not included in the model.

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Weinstein follows treatment naïve patients to 2L failure and includes disease progression and

death. Corzillius models’ patients from diagnosis across four regimens of therapy to death

(excluding transmissions). Sendi and Levison follow patients from 1L treatment failure to 2L

treatment failure. Phillip’s HIV synthesis model begins with HIV incidence, treatment naïve

patients to 2L failure, disease progression and death, accounting for adherence and

transmission (related publications). However, the currently reported sub-analysis focuses on

ten years beginning with 1L failures and follows up patients for ten years including disease

progression and death.

4.6 Model structure

With respect to model structure, it is interpreted that Weinstein uses the CEPAC model, a

state-transition markov model with monte carlo simulation and monthly cycles [6, 7]. The

model is coded in Microsoft Visual Studio and has an excel-based user interface and is

“mostly written in standard ANSI C++” [10]. Corzillius uses a “markov state transition

model” programmed in TreeAge v3.5® [12]. Sendi uses a state transition model programmed

in TreeAge Pro 2005 (TreeAge Software Inc., Williamstown, USA). Levison explicitly uses

the CEPAC model [14] although it is not clear which components (or all) have been adapted

to the South African setting. A decision tree schematic is shown, which differs from the

health state transitions in the CEPAC program user’s guide [10, 14]. This seems to be a

simplification to illustrate the decision problem and the corresponding cohort results which

are reported [10, 14]. Phillips uses the HIV synthesis model, an individual-based stochastic

model incorporating transmission, progression, treatment, resistance and adherence (Phillips

2014) widely reported as per the publications above.

One of the critical questions when deciding which model structure to select is whether

interaction is relevant to the decision problem [25]. For example, in infectious disease,

interaction is important when modelling cross-infection and transmission of disease. Decision

trees, Markov models and individual sampling models assume that variables are independent

in the model [25]. Decision tree frameworks don’t work well when timing of events is

important [26], when risk is continuous over time and where important events may happen

more than once. Individual patients level models are warranted when it is important to

represent each patient individually and when interaction is important. Model structure is

recognized as being a major source of structural uncertainty [27-30] which potentially

influences model results to a greater degree than parameter uncertainty. However model

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structure uncertainty is a difficult aspect of models to analyze and compare. The software

used for models alludes to it’s structure, for example treeage uses a decision analytical

framework with markovian analysis possible, SAS uses statistical relationships. The

distinguishing feature between the structure of the models is that interaction is included in

Phillips and is only possible in an individual patient level model. This is the major difference

between the cohort models of Weinstein, Corzillius, Sendi and Levison and the individual

patient simulation of Phillips.

The respective model structures are illustrated below. The Phillips model is programmed in

SAS so diagrammatically it will look like SAS code. A diagram of the model structure of the

CEPAC model is shown in Figure 3, which us believed to be the Weinstein model.

Figure 3. Simplified CEPAC model structure.

The CEPAC Model User’s Guide (United States and International) Updated May 3, 2017

Senior Programmer: Taige Hou

Figure 4: Screenshots of detailed CEPAC model structure, extracted from user guide.

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Screenshots of CEPAC Patient Simulation Flow Chart (United States and International)

Senior Programmer: Taige Hou

Figure 5: Structure of the calculation of the Levison model.

Figure 6: Corzillius model structure

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4.7 Cycle duration

Related to the time horizon is the cycle duration used in a model. It may be that shorter cycle

durations increase the accuracy of a model thereby minimizing structural uncertainty. On the

other hand, the shorter the cycle duration the more likely it is that assumptions will need to be

because of insufficient evidence to inform the frequent switches between cycles. Monthly

cycle durations may be impractical from a programming/data perspective in lifetime models

and 6 monthly cycle durations seem reasonable to simplify the model and achieve the

simplest possible model structure (Ward 1998; Roberts et al. 2012b, 2012a). Weinstein and

Levison use monthly cycle duration over a lifetime horizon and Corzillius and Sendi use six-

monthly cycle duration over a lifetime horizon. Phillips uses a three monthly for the ten-year

period (?lifetime horizon) described. One and three month time horizons seem to necessitate

more data and assumptions, for a lifetime horizon a six month cycle duration seems a

reasonable trade-off between computational accuracy and pragmatism. Corzillius explains the

choice of 6 month cycle duration because clinicians often judge a drug regimen’s

effectiveness after this time period. As a trade-off between pragmatism and accuracy, a six

month cycle duration for health state transition models.

4.8 Model perspective

Best practice suggests that it is necessary to state and define the model perspective (Roberts

et al. 2012b, 2012a). Selection of the perspective is up to the modeler and depends on the

target audience. Perspective determines which costs/resources will be included in the model.

Using an inappropriate perspective can create bias, for example “using a perspective that is

too narrow (hospital versus societal) will mean that not all relevant costs/outcomes are

included“ (van Mastrigt et al. 2016). It is notable that the PCEHM reference case is a societal

perspective (Neumann et al. 2016). Garrison describes that in a societal perspective „all types

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of resources of value to society should be included; thus, patient’s time costs (lost work time,

lost leisure time) are counted“[31]. The Weinstein abstract states “perspective: societal”,

however the paper also states: “both patient time and non-medical costs were excluded"

implying it is a modified societal perspective. Corzillius adopted a German healthcare payer

perspective for the HTA [12], and a “public health insurance system” perspective for a

“central European healthcare setting” in the paper [11]. Sendi performs separate analyses for

Swiss healthcare and societal perspectives. Levison adopts a modified societal perspective for

South Africa [14]. Phillips does not explicitly state the perspective, but it implicitly appears

to be a Zimbabwean payer’s perspective. The models have considered a healthcare payer

perspective at least [11, 14, 19], a modified societal perspective (Weinstein et al. 2001;

Levison et al. 2013) and a societal perspective at most (Sendi et al. 2007). Consistent use of

the same perspective would improve the comparability of studies. There is no right or wrong

perspective, however the perspective should be kept in mind when comparing model results.

4.9 Prevalence of WT

Prevalence of wild type virus (no resistance) varies between countries and is changing over

time. Prevalence of resistance influences the cost effectiveness of GART. Levison uses a base

case wild type prevalence of 20%. Weinstein references the prevalence of primary (not

secondary) resistance. Corzillius, Sendi and Phillips do not report prevalence of resistance,

however Corzillius acknowledges that the cost effectiveness of GART inherently depends on

the prevalence of drug resistance.

4.10 Modelled population

Having different starting CD4 and VL values means that patients begin the model in a

“more” or “less” sick state. Weinstein starts with treatment naïve patients, 80% of which are

male, mean age 33 years with CD4 250/μl and HIV-RNA (copies/mL) distribution as follows

7.71% < 500, 16.33% 501 – 3000; 25.21% 3001 - 10 000; 25.02% 10 001 - 30 000; 25.73%

30 001 - 100 000 based on the Multicenter AIDS Cohort Study (MACS). Corzillius starts

with treatment naïve male patients mean age 35 years and CD4 350/μl based on the SHCS

and HIV RNA in the range of 10,000 to 30,000 copies/ml. Sendi starts with patients on

treatment with 1L failure, 80% if which are male mean age 33 years based on the SHCS

study. Starting HIV/RNA cannot be identified from the paper. Levison starts with treatment

naïve patients with starting CD4 173/μL and median HIV RNA 49 copies/mL and HIV RNA

median HIV RNA 79,432 copies/Ml based on published reports for South Africa [32-34].

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Phillips focuses on a high prevalence heterosexual population. Initial mean CD4 count at

infection was 756 cells/uL (supplementary appendix). The critical question is whether the

difference in CD4/VL levels at model start will result in a difference in cost and outcomes.

What are the clinically meaningful differences between patients with CD4 250 μl (Weinstein)

HIV RNA, 350 μl (Corzillius), 173 μl (Levison) and 49 μl (Sendi)? From a clinical

perspective CD4 has a greater impact on how patients are feeling and therefore their QoL in

comparison to VL. It seems reasonable to differentiate starting population according to a

combination of VL suppression threshold (1,000) and CD4 count threshold (200). By using

these thresholds, it seems possible to capture the differences in costs and quality of life

between modelled subgroups and should be sufficient for future modelling.

4.11 Viral load threshold determining failure

The viral load threshold will determine whether the patients are classified as failing or not

failing 1L therapy. This influences the number of patients who potentially benefit from

GART. Weinstein and Levison consider virologic failure as two consecutive clinical visits

with >1 log increase in HIV-RNA. Weinstein uses two consecutive months, whereas Levison

says two consecutive visits. Corzillius and Sendi consider virologic failure as ≥ 500

copies/mL. Levison considers failure as ≥400 copies/ml @ 24 weeks. Phillips consider

virologic failure as >1000 copies/mL (>log increase). The threshold will also direct

differential proportions of patients in a cohort model through the different strategies. The

threshold we use impacts on the proportion of patients eligible for GART.

4.12 ART

In Weinstein, all patients received indinavir, zidovudine or stavudine, and lamivudine as

initial therapy from the AIDS Clinical Trials Group (ACTG) Protocol 320. Corzillius

modelled the following drug regimens: NRTI: Zidovudine, Didanosine, Zalcitabine,

Stavudine, Lamivudine, Abacavir. NNRTI Nevirapine, Delavirdine, Efavirenz. PI Saquinavir,

Ritonavir, Indinavir, Nelfinavir, Amprenavir, Lopinavir [12]. Probabilities for primary

treatment failure were based on SHCS [11]. Sendi models the treatment regimens in the

SCHS study. According to Lederberger 43% of patients started ART with Zidovudine (AZT)

and 10% with Didanosine (ddI) [35]. Levison modelled the two “lines” of ART available at

that time, consistent with World Health Organization (WHO) guidelines which recommended

that after failure of first-line, nonnucleoside reverse transcriptase inhibitor (NNRTI)–based

ART, individuals switch to protease inhibitor (PI)–based second-line ART. In Phillips the

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regimens are not explicitly stated, but reference is made to WHO guidelines of the time

(2013) which recommend a first line regimen consisting of NNRTI efavirenz plus two NRTIs

tenofovir and 3TC/FTC, with a second line regimen consisting of a ritonavir boosted protease

inhibitor (lopinavir or atazanair) plus two NRTIs (most commonly zidovudine and

3TC/FTC). All five studies model first line HIV treatment, but due to the publication dates,

the standard HIV therapy regimens differ.

4.13 Opportunistic Infection

Weinstein includes the risk of developing OIs. Sendi has two related publications which may

have costs of OI. Corzillius and Phillips do not mention OI. Levison includes monthly risk of

developing mild and severe OI.

4.14 Modelling HIV-related and non-HIV-related mortality

The use of life tables for non-HIV related mortailty is commonly accepted practice in

modelling. Previous comparisons have shown that different assumptions about mortality do

not have substantial impact on cost effectiveness results (Drummond 2005). Weinstein uses

CD4 count and HIV/RNA to predict HIV-related mortality. From the paper it cannot be

identified whether non-HIV related mortality is included in the model, however patients are

followed until death therefore it is assumed that it is included. Corzillius includes health

states for HIV-related and non-HIV related mortality. HIV-related mortality is modelled as

24 months taken from Robert Koch Institute Infectious Disease estimates in Germany

(http://www.rki.de/). Non-HIV mortality is taken from life tables of the Federal Agency for

Statistics, Wiesbaden, Germany. Sendi includes HIV-related mortality assumed to be taken

from SHCS and non-HIV related mortality from Swiss life tables (www.statistik.admin.ch).

Levison includes monthly HIV-related mortality in patients with no history of OI as 0.11 -

4.0 and 7.9 - 9.5 with history of OI sourced from a study by Holmes in South Africa (Holmes

2006). It is not clear if non-HIV related mortality is included in this model adaptation.

Phillips includes monthly risk of HIV-related mortality based on CD4 (VL and age) (Phillips

2014) and in the adaptation it is not clear but the original HIV synthesis model bases non-

HIV related mortality on lifetables for UK (Phillips 2007) therefore it is assumed that local

life tables (Zimbabwe) were used for the 2014 study.

4.15 Resource use and valuation

1.1 Resource use and valuation

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When including the resource use in the model – the main driver is predicted to be the

distinction between the resource use for AIDS versus non-AIDS patients. Again this is an

important aspect which could be potentially improved on in a new model. The type of costs

which will be included in a model are determined by the perspective chosen for the analysis.

To compare costs an exhaustive table of resource use and cost data was compiled across all

models.

Resource Use and Cost Data: The country of focus in the studies are USA (Weinstein),

Germany (Corzillius), Switzerland (Sendi), South Africa (Levison) and Zimbabwe (Phillips).

It is difficult to directly compare resource use and cost data across models because different

models group resources differently, for example Corzillius applies 6 monthly cost according

to AIDS status whereas Sendi assigns costs to patients according to 9 combinations of CD4-

HIV strata and Phillips assigns costs according to WHO classification. Comparability is also

difficult due to values not being reported, possibly due to journal word count restrictions.

Only one paper reports and describes inclusion of productivity costs in the societal

perspective analysis (Sendi). The cost distinction that will impact the modelling results is that

between AIDS and non-AIDS.GART cost is not a big driver for results shown in the

sensitivity analysis. None of the models explicitly describe the inclusion of cost of OI in the

models. Corzillius alludes to this when he assigns a 6 monthly cost of treatment to AIDS and

non-AIDS patients. In sensitivity analysis he identifies this as a major determinant in driving

the cost effectiveness results.

Diagnostic test costs: Comparable GART cost per test are reported as $400 (1998) (Weinstein

et al. 2001); €307 (1999) (Corzillius et al. 2004); $625 (2005) (Sendi et al. 2007); $300

(2010)(Levison et al. 2013). Phillips reports a cost of $30 (2014) (assumed to be per test cost)

for their base case calculation (Phillips et al. 2014). CD4 count test cost is $83 (1998)

(Weinstein et al. 2001); $12(1998) (Levison et al. 2013) and $8 (2014)(Phillips et al. 2014).

Viral load testing is reported as $110 (1998)(Weinstein et al. 2001); $62 (2010) (Levison et

al. 2013) and $15 (2014) (Phillips et al. 2014). The cost of viral load testing and CD4 count

cannot be located for Corzillius (Corzillius et al. 2004; Corzillius 2003) and Sendi (Sendi et

al. 2007).

ARV drug treatment cost: For costing purposes, Weinstein assumed that patients received an

average of 2 NRTIs, 1 PI and 0.6 or 1 NNRTI. Weinstein used $2700 for NRTI based ART,

$3300 for NNRTI based ART and $6800 PI based ART per patient per year (1998). Levison

20

reports $124 for NNRTI based ART and $613 for PI based ART per patient per month

(2010). ARV drug treatment costs cannot be located in Sendi , Phillips and Corzillius.

Health care costs: Corzillius reports a 6 monthly cost for HIV care in patients with AIDS and

AIDS-free as €13,549 and €10,482 (1999). Sendi assigns six monthly costs to health states as

follows: undetectable viral load first two years $18,427; detectable viral load first two years

$16,870; No AIDS, CD4 0–200 cells/mm3, <1000 HIV RNA copies/ml $23,504; No AIDS,

CD4 0–200 cells/mm3, ≥1000 HIV RNA copies/ml $19,264; No AIDS, CD4 201–500

cells/mm3, <1000 HIV RNA copies/ml $16,519; No AIDS, CD4 201–500 cells/mm3, ≥1000

HIV RNA copies/ml $14,898; No AIDS, CD4 ≥500 cells/mm3, <1000 HIV RNA copies/ml

$17,207; No AIDS, CD4 ≥500 cells/mm3,≥1000 HIV RNA copies/ml $15,127; AIDS, CD4

0–200 cells/mm3 $44,736; AIDS, CD4 201–500 cells/mm3 $29,402; AIDS, CD4 ≥500

cells/mm3 $17,207 (2005). Phillips asigned a mean treatment cost according to patient WHO

status for WHO 3 $20; WHO 4 $200; and TB $50 with the ‚cost incurred for 3 months‘

(2014).

No costs of opportunistic infections can be identified in any of papers.

4.16 Model output measure:

From a decision makers viewpoint the QALY is the best outcome measure, it allows us to

compare cost effectiveness across disease spectrums. Although not an exact science it allows

approximation of the impact of clinical treatment on patients. It seems unnecessary to use

other outcome measures, it renders studies difficult to compare. Weinstein reports quality

adjusted life expectancy (QALE) (months and years) and quality adjusted life years

(QALYs). Corzillius reports cost per life years gained (LYG). Sendi reports cost per life year

gained, cost per quality adjusted life years and quality adjusted life months (QALMs).

Levison reports years of life saved (YLS), it is unclear if this is the same as life years saved.

Levison reports cohort results for each of the arms illustrated in their tree diagram – it is

unclear why these results are presented . Phillips reports cost per disability adjusted life years

(DALY). Therefore, it is possible to compare Corzillius and Sendi’s LYG and Weinstein and

Sendi’s QALYs (with caution considering Weinstein uses discount rates of 3% for costs and

outcomes and Sendi uses 2 % and 4% for costs and outcomes respectively and that Weinstein

uses a modified societal perspective and sendi uses healthcare and societal perspectives). It

does not seem possible to accurately compare Phillips results to the other studies due to the

21

use of DALYS. Standardising model outputs according to a reference case make it easier to

compare model results. The models essentially measure life expectancy, years of life saved.

4.17 Quality of life (continued from main paper)

In Phillips main paper there is no mention of quality of life since the health outcome measure

is DALYs. However in the supplementary appendix (2014) there is a section called “utilities”

and it is stated that “values are 1 except for the following” where the following are listed:

drug toxicity in current 3 month period (0.95), WHO 3 condition (except TB) in (0.78) and

current TB (0.60) and WHO 4 condition (0.46) all in “current 3 month period”. It is presumed

that these values refer to another version of the model. Salomen’s 2010 study of DALYs is

referenced for this section which reports DALYs for HIV symptomatic pre-AIDS patients

(0.221); HIV/AIDS receiving ART (0.053) and AIDS not receiving ART (0.547) [36]. It is

unclear whether/how these DALYs have been used in the model (Philips 2014).

4.18 Raw results

Raw results: Weinstein and Corzillius found GART resulted in an increased cost (discounted

$17900 vs undiscounted €16406) with increased life expectancy (LE) (3 mo vs 9 mo)

respectively. Sendi found GART resulted in an increased cost (??discounted $2,000) with an

increase LE (3 and 2 wks quality adjusted LE). Levison found GART increased cost

(discounted lifetime cost $180 per person) with a gain in health outcomes (2.2 months).

Phillips found that GART strategy resulted in reduction in cost and no gain in health

outcomes.

4.19 Willingness to pay threshold: Reported results and standardization to 2017 USD

WTP thresholds are specific to the context of the decision question and cannot be criticized

but readers should keep in mind that the final conclusion of the study on whether or not

GART is cost effective depends on the CE threshold used in the analysis. Weinstein does not

state which willingness to pay threshold is considered to determine cost effectiveness.

Corzillius referred to a willingness to pay threshold of 50,000 €/LY; Sendi to 50,000

USD/QALY, Phillips mentions 1,000 USD/DALY and Levison considered a strategy “very

cost-effective” “if its ICER was <1 times the per capita gross domestic product (GDP =

US$7100 for South Africa in 2010), and “cost effective” if <3 times the GDP”.

22

Table 2: Standardised results

Interpreted

perspective

Author/study Reported

component

Discounted non-standardised reported results Standardised 2017 USD, assume no change

in benefit

No

GART

GART cost per

outcome

comment No

GART

GART cost per

outcome

comment

Modified

societal

Weinstein

CPCRA046

cost USD

1998 (dr 3%)

$90

360

$93

650

reported

result

$129

962

$134

694

Modified

societal

Weinstein

CPCRA046

QALE (mo)

(dr 3%)

60,9 63,1 reported

result

60,9 63,1

Modified

societal

Weinstein

CPCRA046

QALE (yrs)

(dr 3%)

5,1 5,3 not reported,

calculated

5,1 5,3

Modified

societal

Weinstein

CPCRA046

cost per

outcome

[USD

1998/QALE

(yrs)]

$17 945 reported

value Table

3 $17,900

discrepancy

assumed to

be due to

rounding.

$25 811 USD

2017/QALE

(yrs)

Modified

societal

Weinstein

VIRADAPT

cost USD

1998 (dr 3%)

$91

980

$97

790

reported

result

$132

292

$140

649

23

Interpreted

perspective


component


in benefit

Modified

societal

Weinstein

VIRADAPT

QALE (mo)

(dr 3%)

62,2 66,4 not reported,

calculated

62,2 66,4

Modified

societal

Weinstein

VIRADAPT

QALE (yrs)

(dr 3%)

5,2 5,5 reported

value

$17,900

discrepancy

assumed to

be due to

rounding.

5,2 5,5

Modified

societal

Weinstein

VIRADAPT

cost per

outcome

[USD

1998/QALE

(yrs)]

$16 600 reported

value Table

3 $16,300

discrepancy

assumed to

be due to

rounding.

$23 877 USD

2017/QALE

(yrs)

Public health

payer

Corzillius cost Euro1999

(dr 5%)

223

688 €

230

441 €

6 753 € $243

289

$250

633

24

Interpreted

perspective


component


in benefit

Public health

payer

Corzillius LYG (dr 5%) 10,4 10,7 0,3 10,4 10,7

Public health

payer

Corzillius cost per

outcome [€

1999/LYG]

$22 510 reported

Table 2

$24 480 USD

2017/LYG

Healthcare Sendi cost USD

2005 (dr 4%)

$419

200

$420

900

$1 700 Note that

there is a

discrepancy

between the

values

reported in

the text and

Table 3, for

the purpose

of this

comparison,

the latter

have been

$548

237

$550

723

25

Interpreted

perspective


component


in benefit

used.

Healthcare Sendi LYG (dr 4%) 15,43 15,49 15,43 15,49

Healthcare Sendi QALY (dr

4%)

11,07 11,12 0,05 11,07 11,12

Healthcare Sendi cost per

outcome

USD

2005/LYG]

$28 333 $41 434 USD

2017/LYG


outcome

[USD

2005/QALY]

$34 000 Reported

value in

Table 4 is

$35,000

discrepancy

assumed to

be due to

rounding of

incremental

cost $1800

reported

$49 721 USD

2017/QALY

26

Interpreted

perspective


component


in benefit

versus $1700

calculated

Societal (inc

productivity

costs)

Sendi cost USD

2005 (dr 4%)

$310

200

$311

600

Note that

there is a

discrepancy

between the

values

reported in

the text and

Table 3, for

the purpose

of this

comparison,

the latter

have been

used.

$405

878

$407

710

Societal (inc

productivity

Sendi LYG (dr 4%) 15,43 15,49 15,43 15,49

27

Interpreted

perspective


component


in benefit

costs)

Societal (inc

productivity

costs)

Sendi QALY (dr

4%)

11,07 11,12 11,07 11,12

Societal (inc

productivity

costs)

Sendi cost per

outcome

[USD

2005/LYG]

$23 333 $30 533 USD

2017/LYG

Societal (inc

productivity

costs)

Sendi cost per

outcome

[USD

2005/QALY]

$28 000 $36 640 USD

2017/QALY

Healthcare Sendi cost USD

2005 (dr 2%)

$548

600

$550

500

Note that

there is a

discrepancy

between the

values

reported in

$717

811

$720

297

28

Interpreted

perspective


component


in benefit

the text and

Table 3, for

the purpose

of this

comparison,

the latter

have been

used.

Healthcare Sendi LYG (dr 2%) 19,35 19,4 19,35 19,4

Healthcare Sendi QALY (dr

2%)

14,19 14,27 14,19 14,27


outcome

[USD

2005/LYG]

$38 000 $49 720 USD

2017/LYG


outcome

[USD

2005/QALY]

$23 750 $31 075 USD

2017/QALY

29

Interpreted

perspective


component


in benefit

Societal (inc

productivity

costs)

Sendi cost USD

2005 (dr 2%)

$398

500

$400

600

$2 100 Note that in

Table 3

GART is

more costly

($400,600)

than expert

opinion

($398,500),

however in

Table 4 this

change in

incremental

cost is

reported as -

$200 costs,

however this

does not

correspond

$521

414

$524

162

30

Interpreted

perspective


component


in benefit

to the

calculated

incremental

difference

$1200 of

GART vs no

GART and

the origin of

the originally

reported

value is

unclear.

Societal (inc

productivity

costs)

Sendi LYG (dr 2%) 19,35 19,4 0,05 19,35 19,4

Societal (inc

productivity

costs)

Sendi QALY (dr

2%)

14,19 14,27 0,08 14,19 14,27

Societal (inc

productivity

Sendi cost per

outcome

$42 000 $54 960 USD

2017/LYG

31

Interpreted

perspective


component


in benefit

costs) [USD

2005/LYG]

Societal (inc

productivity

costs)

Sendi cost per

outcome

[USD

2005/QALY]

$26 250 $34 350 USD

2017/QALY

Modified

societal

Levison Cost USD

2010 (dr 3%)

$16

360

$16

540

$81

477

$82

373

Modified

societal

Levison LE (mo) (dr

3%)

106,1 108,3 106,1 108,3

Modified

societal

Levison $/YLS (dr

3%)

2,04 2,08 2,04 2,08

Modified

societal

Levison cost/LE (mo)

calculated

$82 $407 USD

2017/LE

(mo)

Modified

societal

Levison USD 2010

/LE (yrs)

calculated

$4 255 $21 178 USD

2017/LE

(yrs)

32

Interpreted

perspective


component


in benefit

Modified

societal

Levison USD 2010

/YLS (yrs)

reported

$900 The cost per

YLS

reported in

Table 2 is

$900 but it is

unclear how

this value

has been

calculated

according to

the

information

provided in

Table 2.

Unsure how

to calculate

this

33

4.20 Types of SA performed

Comparative analysis of sensitivity analysis indicates what impact the model parameters have

on the model results. PSA is the gold standard and is a good way to determine the overall

robustness of the model results. Univariate and multiway are complementary to PSA and

their importance to understanding the key cost drivers should not be underestimated

(Rautenberg, Zerwes, and Lee 2018). The impact which the parameters and assumptions have

on the model results is partially dependant on the structure of the model. For example cohort

models which use decision tree frameworks are usually influenced by variables which

determine the proportions through which the patients move through the arms (Rautenberg,

Zerwes, and Lee 2018). Weinstein describes one-way SA, it is not clear if multiway SA has

been undertaken. It is unclear whether PSA is not performed or not reported. If this is the

CEPAC model, the CEPAC model user’s guide describes a sensitivity analysis tool as an

additional model tool which allows the user to test 1-3 way SA and PSA (Weinstein 2001,

CEPAC users guide). Corzillius performed one-way sensitivity analysis for plausible ranges

of all model parameters, multivariate scenario analysis and probabilistic sensitivity analysis

(Corzillius 2004).

Sendi performed PSA, no univariate or multivariate SA reported (Sendi 2007). Levison

performed ‘broad univariate analyses and multiway sensitivity analyses’. Report results of

top six parameters which affect the clinical outcomes and top eight parameters that affect cost

effectiveness outcomes (table below) (Levison 2013). Phillips conducted several one-way

SA. Model results were insensitive to changes in adherence, time horizon of twenty years

(AOT 10 years); cost of GART; Starting treatment CD4 count, cost of PI based therapy

(Philips 2014). Both models with PSAs (Sendi and Corzillius) show high probability of cost

effectiveness. Corzillius PSA varying all model parameters simultaneously yielded a median

ICER of approximately 22,200 €/LY (95% CI: 16,900–34,600 €/LY). In 98.96% of the

simulated cases, ICER was below 50,000 €/LY. Sendi showed that from a healthcare

perspective, at a WTP threshold of ≥ $35,000 GART has a higher probability of being cost

effective than no GART. From a societal perspective at a WTP threshold ≥ $850 GART has a

higher probability of being cost effective than expert opinion. This seems to be confirmed for

the Levison model.

34

4.21 Variables explored

For the purpose of this comparative analysis the parameters were classified as having a low

impact if the cost effectiveness status was not affected by the variation in the parameter

explored. The parameter was classified as having a high impact if the cost effectiveness

results were overturned ie GART become not cost effective.

35

Table 3: Model parameters explored in the SA

Model parameter Classified impact Model - Variation Model - Change in result

Healthcare costs - before AIDS High Corzillius Healthcare costs - before

AIDS

Corzillius – “Major determinant of cost

effectiveness”

Healthcare costs - AIDS High Corzillius - Healthcare costs - AIDS Corzillius - “Major determinant of cost

effectiveness”

Genotype associated

delays in ART switching

High Levison (base case = 3 months) varied

to <5 months,

was ≥5 months,

Levison - GART very cost-effective <5

months,

was ≥5 months, no GART

preferred strategy

GART test cost Low Weinstein - with test cost $0-$1,000

Corzillius – not specified

Levison base case $300 reduced to <

$100

Weinstein - GART remains cost effective

Corzillius – little impact

Levison- GART was cost-saving when the test

cost was <$100, and very cost-effective at

36


Phillips

Variation not reported

costs greater than this

Phillips GART remains cost effective

Relative effectiveness of

GART

Low Weinstein - relative effectiveness of

GART in reducing

the failure rate of subsequent ART (all

other parameters constant).

Corzillius - effectiveness

of GART (relative risk of treatment

Weinstein GART remains cost effective

<25000 USD even if GART reduced failure

rates by as little as 5%.

Corzillius GART remains 95% probability of

37


failure) most conservative estimate

GART effectiveness (relative risk

0.88; NARVAL trial), and the

most extreme 95% CI upper limit

(relative risk 0.97; VIRADAPT trial).

cost effective at a WTP threshold of €50,000

ART efficacy Low Levison - efficacy of continued

NNRTI-based ART (cohort

Geno WT, base case = 45%):

Levison - genotype was cost-effective when

this efficacy was >15%, and very cost-effective

at efficacies

>17%

Health related quality of life

(not utility)

Low Weinstein – varied over a plausible

range (not reported).

Weinstein remains cost effective

ART 2L efficacy Low Weinstein - Weinstein - GART remains cost effective

when increased suppression rate to 68%

(GART) & 44% (no GART) @ 16wks YES

38


Levison

efficacy of PI-based second-line ART

(cohort Geno

WT, base case = 60%):

Phillips - assuming PI drugs have

same (not superior) efficacy to other

drugs

Levison -

very cost-effective if the efficacy of second-

line ART was >38%; (6) third-line ART (base

case = $254 per month):

Phillips –

GART remains cost effective

Ratio

of cost to charge

Low Weinstein across a plausible range

not reported

Weinstein GART - Remains cost effective

Discount rate Low Weinstein – across a plausible range

not reported

Weinstein - GART remains cost effective

39


Levison – discount rate varied

Levison – GART remains cost effective

Prevalence of WT virus at first-

line ART failure

Low Levison when WT virus was ≥12%, Levison GART was very

cost-effective compared with no genotype;

Costs of second-line ART,

routine care, and an adherence

intervention for individuals

with WT virus (Geno WT)

Low Levison – plausible variations not

reported

Levison - GART remained very cost-effective

Time horizon Low Phillips - increasing the time horizon

(presumably 10 years in base case to

20 years).

Phillips -

GART remains cost effective

CD4 count at 1L failure: Low Levison when CD4 count was >80/μL,

Phillips - CD4 count at ART initiation

variation not reported

Levison GART was very cost-effective;

40


Philips GART remains cost effective

Monthly probability of “late”

ART

failure

Low Levison base case = 1.3% variation

≥1%, 0.25% and 0.9%.

Levison – GART remains very cost-effective if

the

probability was ≥1% and cost-saving between

0.25% and 0.9%.

Adherence Low Phillips adherence Phillips GART remains cost effective

All parameters (PSA) Low Corzillius – all Corzillius a median ICER of approximately

22,200 €/LY (95% CI: 16,900–34,600 €/LY).

In 98.96% of the simulated cases, ICER was

below 50,000 €/LY.

Sendi - From a healthcare perspective, at a

WTP threshold of ≥ $35,000 GART has a

higher probability of being cost effective than

no GART. From a societal perspective at a

WTP threshold ≥ $850 GART has a higher

41


Sendi – all

probability of being cost effective than expert

opinion

42

Other parameters varied by Phillips resistance accumulation is similar to NNRTI drugs

(rather than lower, as in base case); PI halved; The supplementary appendix describes the

following additional SA “people starting second line on average have a decrease in

adherence, due to gastrointestinal adverse effects of boosted PI (b) people starting second

line on average have an increase in adherence, due to greater motivation to adhere (c)

decreased probabiity of switch once failure criteria fulfilled (d) immediate switch once

failure criteria fulfilled (e) decreased death rate in people with current WHO stage 4 disease

or TB (f) decreased underlying variability in CD4 count (g) Increased underlying variability

in CD4 count (h) decreased extent to which people interrupt ART with the clinic unaware

(and so are considered on ART but have no resistance mutations) (i) increased extent to

which people interrupt ART with the clinic unaware (j) decreased rate of treatment

interruption (k) increased rate of treatment interruption (l) situation in which boosted PI

drugs had half potency of other drugs (so preserving activity of nucleosides more

important).“ Phillips 2014 supplement Levison also simultaneously explore the effect of the

following combinations of parameters: i) efficacy of continued NNRTI based ART+

prevalence WT virus ii) CD4 count and GART associated delay in ART switching iii) GART

test cost+1L failure. Corzilius states that they are major determinates but does not report what

plausible values were explored and the magnitude or direction of change of results. For the

purpose of this review, these two paramters have been assumed to overturn the results.

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s3-eu-west-1.amazonaws.com€¦ · Web viewComparative Analyses of Published Cost Effectiveness Models Highlight Critical Considerations Which Are Useful to Inform Development of