2013 Special Topics Conference

Peaks and Pitfalls in Longitudinal Analysis of Symptom Outcome Data

Terri S. Armstrong, PhD, ANP-BC, FAANPUniversity of Texas health Science Center at Houston

Special Thanks:AAN: Planning Committee, Laura SmothersUTHSC-SON: Nancy Bergstrom, Dean Patricia Starck

COI: Research SupportSchering PloughMerckGenentech

RESEARCH SUPPORT:RTOG 0525/0825: NCI U10CA 21661, U10CA37422, ABC2, MERCK PHARMACEUTICALS, GENENTECHFATIGUE PILOT STUDY: CERN RESEARCH FOUNDATION

INNOVATIONS IN LONGITUDINAL DATA COLLECTION AND ANALYSIS

Success consists of going from failure to failure without loss of enthusiasm

-Winston Churchill

Armstrong, T.S. (2003), ONF; Armstrong et al, (2004) Journal of Nursing Scholarship

SYMPTOMS EXPERIENCE MODEL

The Science of Symptom Management

Exposure Genetic Susceptibility

Biologic Trigger or Process

Symptom or Toxicity

I

I

I

I

Targeted InterventionS

ympt

om S

core

-14 -7 0 7 14 21 28 35 420123456789

10

06

121824303642485460

WB

C (

x10^

9/L

)

PainFatigueNauseaSleepDistressShortness of BreathLack of AppetiteSadnessAttentionWBC

TreatmentTargetIntervention

Biologic Correlate

The Science Behind Symptom Management: INTERLOCKING IDEAS

Develop Biologically Based & Practical

Interventions

Predict whose at Risk

(Clinical and Genomic)

Recognize importance and

Accurately Measure

•

PROs Used in the Analysis

Symptom Burden: MDASI-BT

Overall Scores

• Global Symptom Burden- Mean of all 22 symptoms• Interference (6 items)- Activity Related

Interference- Mood Related Interference:

Factor Grouping

6 multi-item Groupings1.Affective Factor2.Cognitive Factor3. Neurologic Factor4. Treatment-related Factor5. Generalized Disease Factor 6. GI Factor

Longitudinal Data

Course before the

index episode

Prodrome

Index episode

Course between the

index episode and

follow-up

Follow-up or Outcome

Assessment

Adamis, 2009

Causality

Causality

Advantages/Disadvantages of Longitudinal Data

• ADVANTAGES:– Often provide more informative

data– They allow the study of individual

dynamics (such as age and cohort effects)

– They allow assessment of the time order of events

• DISADVANTAGES:– Attrition and missingness– Need for special statistical analysis

(individual versus time)

Diggle, Heagerty, Liange, & Zeger, 2002;Meard, 1991; & Adamis, 2009

‘It isn’t the mountain ahead that wears you out; It’s the grain of sand in your shoe’

Brain Tumor Background • Tumors that arise from the constituent

elements of the CNS & primarily stay within the CNS

• An estimated 51,410 new cases of primary nonmalignant and malignant brain tumors estimated for 2012 (21,810 malignant)1

• Above represents 1.35% of cancers1

• An estimated 12,760 deaths will be attributed to primary malignant brain tumors in the U.S. in 20051; this represents 2.4% of all cancer deaths2

1. CBTRUS: Statistical Report on Primary Brain Tumors in the United States,. www.cbtrus.org/factsheet.htm

2. SEER.cancer.gov/CSR

Rationale for Program of Research Patients with CNS tumors often suffer devastating effects as a consequence of the

tumor and/or treatment Often unable to return to work from the time of diagnosis and studies report

patients spend the majority of their lives feeling ill and unable to perform usual activities (Fobair et al, 1990; Salander et al, 2000; Strang & Strang, 2001)

Limitations of current outcomes assessment CNS tumor treatments are often similar in efficacy and survival (Stupp et al, 2005) Current imaging is limited by technique, interpretation, and changing impact of

targeted agents and ‘The Avastin Effect’ (Chamberlain et al, 2006; Norden et al, 2008); and pseudoprogression (Chamberlain et al, 2007)

Tumor related Symptoms and Toxicity associate with therapy has been widely reported, but not collected in a systematic or rigorous way. (Armstrong et al, 2005; Scheibel, et al, 1996; Correa et al, 2007) Traditional endpoints do not necessarily reflect clinical benefit

Standard Treatment

SurgeryConcurrent

chemoradiation(6 weeks)

Adjuvant chemotherapy

(12 months)

It’s like deja-vu, all over again

-Yogi Berra

Comparative Impact of Treatment on Patient Reported

Outcomes (PROs) in Patients with Glioblastoma (GBM)

Enrolled in RTOG 0825

Won, M., Wefel, J.S., Gilbert, M.R., Pugh, S.L., Wendland, M., Brachman, D., Komaki, R., Crocker I. , J., Robins, H.I. ., Lee, R., & Mehta, M.

Jeffrey S. Wefel, PhD , Meihua Wang, PhD Minhee Won, MA, Andrew Bottomley, PhD, Tito R. Mendoza, PhD, Corneel Coens, MSc, Maria Werner-Wasik, MD, David G. Brachman, MD, Ali K. Choucair, MD, Mark R. Gilbert, MD, Minesh Mehta, MD

Net Clinical Benefit AnalysisOf Radiation Therapy Oncology Group 0525: A phase III Trial comparing conventional Adjuvant Temozolomide with Dose-Intensive TemozolomideIn Patinets with newly iagnosed GBM

Biologic Correlates of Fatigue in GBM Patients Undergoing Radiation Therapy: A Pilot Study

Alvina Acquaye, MS, David Balachandran, MD, Elizabeth Vera-Bolanos, MS, Mark R. Gilbert, MD, Duck-Hee Kang, PhD, Anita Mahajan, MD

Top 5 List

• Study Planning• Study Design• Conduct of the Study• Data Analysis• Data Reporting

'The only thing you'll find on the summit of Mount Everest is a divine view. The things that really matter lie far below.’

-Roland Smith

‘I AM THANKFUL TO ALL THOSE WHO SAID NO- IT IS BECAUSE OF THEM I DID IT MYSELF’

-ALBERT EINSTEIN

Study Planning

Steps in Planning Use of Pros in Longitudinal Studies

Identify the relevant domains to measure:

What are the areas that the particular therapy are known or hypothesized to impact?

Development of a conceptual framework:

Outline the proposed relationships among the disease, treatment and PRO domains.

Identify candidate approaches to measuring the domains:

Is there an existing instrument that is psychometrically validated and feasible for use?

Synthesize the information to design the final measurement strategy:

Develop hypotheses and measureable outcomes based on the identified relationships

between primary outcome and PRO domains. Identify timepoints that are important to

capture, considering feasibility and completion of data.

Conducting the Study: The PITFALLSWhat I have learned• Seek input from others

• Be active in the data collection

• Feasability & Practicality are important

• Something will go wrong – be prepared

And Yogi Says:‘IF YOU DON’T KNOW WHERE YOUARE GOING, YOU MIGHT WIND UP

SOMEPLACE ELSE’

YOU CAN OBSERVE A LOT BY WATCHING

‘WE MADE TOO MANY WRONG MISTAKES’

‘IN THEORY THERE IS NO DIFFERENCE BETWEEN THEORY AND PRACTICE. IN PRACTICE THERE IS’

‘IF YOU CAN’T EXPLAIN IT SIMPLY- YOU DON’T UNDERSTAND IT WELL’ ENOUGH’

-ALBERT EINSTEIN

Data Analysis

Analytic Methods

• Summarized Data– Ex. Mean, median – Treat as a single response then analyze with ANOVA,

regression, etc– Simplest – Controversy over how to handle missing data

• Slope– Single summary measure (variable over time)– May miss nuances/can’t adjust for other variables

Analytic Methods

• Paired T-test– Limited to two observations

• (second – first or vis- versa)

• Other Summary Measures:– Area under the curve (AUC), maximum values

• Disadvantages:– Missingness can make unreliable– Reduced statistical power– If non-linear-difficult to interpret results

Summarized Data• WK 6 Fatigue severity correlated with:

radiation dose to the pineal gland (r = 0.86, p = .07), and altered sleep, including self report sleep (r= 0.849, p =.016), and as determined by ACT (r = 0.70, p =.07).

• Change in melatonin (MLT) levels strongly correlated with the change in fatigue score (r = 0.90, p = .036), and change in wake time after sleep onset (WASO) by ACT (r = 0.97, p = .033). Fatigue severity at WK 6 was also correlated with the severity of reported neurologic (r = 0.72, p = .043) and cognitive symptoms (r = 0.94, p = .01) at WK 6.

• Pilot study characterizing change in circadian pattern of melatonin production demonstrated ‘shift in melatonin to earlier in the day & excess production

Mean dose pineal gland (Gy)

Total18 28 50 52 60

BFI worst

fatigue right now

at week 6

2 11 0 0 0 0 1

4 0 1 0 0 0 1

7 0 0 1 1 0 2

10 0 0 0 0 1 1

Total 1 1 1 1 1 5

Model of Radiation-Induced Fatigue(Armstrong & Gilbert, 2012)

‘The most important thing is not to stop questioning. Curiosity has its own reason for existing’

-Albert Einstein

Analytic Methods

• Time-by-Time Analysis– Single or several time points while ignoring the

others– Useful if finding what timepoint is significantly

different– Advantage: Missing at other time points do not

impact data; simple– Disadvantage: increased chance of Type 1 error,

must exclude if missing at needed time point; complicated analysis (may need to summarize)

RTOG 0525Testing of Deterioration Status from Baseline

to prior to cycle 4 in MDASI-BT using MID

Set Minimally Important Difference

Classify patients as ‘deteriorated’ or ‘not’

Assess Difference in Proportion in each group

Arm 1 Arm 2

Deterioration Deterioration

Component n % n % p-value*

Symptom 5 10 11 27 0.03

Interference 7 14 13 32 0.03

--Activity related

8 16 15 39 0.01

-- Mood related

12 24 12 30 0.49

Median and range in Arm 2 Deterioration:

Overall Symptom change(1.6; range 1-2.8),

Overall Interference (2.5; range 1.5-7.7)

Activity Interference(1.5; range 1.0-8.0)

Example Symptom Burden on RTOG 0825Using Grouped Data Improved Deteriorated or No Change Baseline to Specific Time Points

Wk 10

No DifferenceWk 22

Treatment Factor (p=0.05)

Wk 34

Treatment (p=0.008)Affective (p=0.04)Generalized (p=0.02)Cognitive (p=0.05)

Significant Less Improvement/More Deterioration in Bev Arm

RTOG 0825MDASI-BT Baseline to Week 34

More Deteriorated on Bevacizumab More Improved on Placebo

Analytic Methods

• Mixed or Random Effects– Types of Analysis:

• Linear mixed effect model• Mixed effects approach for binary outcome data• Generalized estimating equations (GEE) approach

• Pro: – allows evaluation of trends over time using all data points– Allows evaluation of other variables

• Cons:– Degree of missingness can impact analysis– Complicated analysis

MDASI-BT and OSCox Proportional Hazards Model for Overall Survival (RPA & MGMT included)

p-valueHazard Ratio

(95%CI)Methylation Status (Methylated vs. Not) <.001 2.40 (1.81, 3.18)

RPA (IV vs. III) 0.002 1.83 (1.25, 2.66)RPA (V vs. III) <.001 3.18 (2.07, 4.88)Baseline Neurologic Factor 0.005 1.12 (1.04, 1.21)

Methylation Status (Methylated vs. Not) <.001 2.22 (1.58, 3.12)

RPA (IV vs. III)* 0.121 1.38 (0.92, 2.09)RPA (V vs. III) 0.002 2.19 (1.33, 3.60)Cognitive Factor 0.002 1.66 (1.20, 2.29)

Ba

selin

eE

arly

Comparative Impact of Treatment on RTOG 0825

MDASI-BT Longitudinal Trends – P-valuesStudy Duration (weeks 0-46)

Week

Effect*Treatment

Effect*

Week/TreatmentInteraction

Effect*MGMTEffect*

RPAEffect*

Symptom 0.029 0.180 0.017 0.300 <0.001

Inference 0.758 0.601 <0.001 0.891 <0.001

WAW 0.443 0.732 0.004 0.747 <0.001

REM 0.664 0.509 <0.001 0.426 <0.001

Affective Factor 0.508 0.525 0.038 0.810 <0.001

Cognitive Factor <0.001 0.143 0.014 0.372 <0.001

Neurologic Factor 0.082 0.017 0.135 0.719 0.003

Treatment Factor 0.014 0.890 0.029 0.021 <0.001

Generalized/disease Factor

0.865 0.199 0.011 0.353 <0.001

GI Factor <0.001 0.124 0.889 0.710 0.041

*Type III test of fixed effects, general linear model (repeated measure), linear trend

Global Symptom Burden, Interference & Multiple Factor groups significantly worse with Bevacizumab compared to Placebo

Cognitive Factor Overall Interference

Weeks from Randomization

MD

AS

I Sco

re

P = 0.040

MDASI-BT Longitudinal Analysis from RTOG 0825

0 6 10 22 34 46

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

Weeks from Randomization

Infe

ren

ce S

core

0 6 10 22 34 46

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

P < 0.001

Placebo

Bevacizumab

Placebo

Bevacizumab

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From:Lancet Oncol. Author manuscript; available in PMC 2012 July 6.

Published in final edited form as:Lancet Oncol. 2008 August; 9(8): 777–785. doi: 10.1016/S1470-2045(08)70197-9Copyright/License ►Request permission to reuse

Figure 2Click on image to zoom

Molecular epidemiology approach to cancer-related symptoms

Images in this article•

•

Click on the image to see a larger version.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3390774/figure/F2/

Published in final edited form as:Lancet Oncol. 2008 August; 9(8): 777–785. doi: 10.1016/S1470-2045(08)70197-9

Molecular epidemiology approach to cancer-related symptoms

When you come to a fork in the road – take it

-Yogi Berra

Upcoming PeaksGrant# 1 R01 NR013707-01A1; Symptoms-Toxicity-Response Electronic Data Capture

www.cern-foundation.org

‘IT AIN’T OVER TIL IT’S OVER’ -YOGI BERRA

Study Publication

Summary

• Planning is key• Seek input • Analysis plan dependent on question of

interest• Integrated analysis to fully understand the

symptom (molecular epidemiologic approach)• Publication of results!

Special Thanks to the patients and familiesWho participated in these trials

Success is not final, failure is not fatal – it is the courage to continue that matters

-Winston Churchill