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2013 Special Topics Conference Peaks and Pitfalls in Longitudinal Analysis of Symptom Outcome Data Terri S. Armstrong, PhD, ANP-BC, FAANP University of.

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Presentation on theme: "2013 Special Topics Conference Peaks and Pitfalls in Longitudinal Analysis of Symptom Outcome Data Terri S. Armstrong, PhD, ANP-BC, FAANP University of."— Presentation transcript:

1 2013 Special Topics Conference Peaks and Pitfalls in Longitudinal Analysis of Symptom Outcome Data Terri S. Armstrong, PhD, ANP-BC, FAANP University of Texas health Science Center at Houston

2 Special Thanks: AAN: Planning Committee, Laura Smothers UTHSC-SON: Nancy Bergstrom, Dean Patricia Starck COI: Research Support Schering Plough Merck Genentech RESEARCH SUPPORT: RTOG 0525/0825: NCI U10CA 21661, U10CA37422, ABC 2, MERCK PHARMACEUTICALS, GENENTECH FATIGUE PILOT STUDY: CERN RESEARCH FOUNDATION

3 INNOVATIONS IN LONGITUDINAL DATA COLLECTION AND ANALYSIS Success consists of going from failure to failure without loss of enthusiasm - Winston Churchill

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

5 The Science of Symptom Management Exposure Genetic Susceptibility Biologic Trigger or Process Symptom or Toxicity I I I I

6 Targeted Intervention Treatment Target Intervention Biologic Correlate

7 The Science Behind Symptom Management: INTERLOCKING IDEAS Develop Biologically Based & Practical Interventions Predict whose at Risk (Clinical and Genomic) Recognize importance and Accurately Measure

8 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 Groupings 1.Affective Factor 2.Cognitive Factor 3. Neurologic Factor 4. Treatment-related Factor 5. Generalized Disease Factor 6. GI Factor

9 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

10 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’

11 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 cancers 1 An estimated 12,760 deaths will be attributed to primary malignant brain tumors in the U.S. in 2005 1 ; this represents 2.4% of all cancer deaths 2 1. CBTRUS: Statistical Report on Primary Brain Tumors in the United States,. 2.

12 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

13 Standard Treatment Surgery Concurrent chemoradiation (6 weeks) Adjuvant chemotherapy (12 months) It’s like deja-vu, all over again -Yogi Berra

14 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 Analysis Of Radiation Therapy Oncology Group 0525: A phase III Trial comparing conventional Adjuvant Temozolomide with Dose-Intensive Temozolomide In 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

15 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


17 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.

18 Conducting the Study: The PITFALLS What 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 YOU ARE 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’


20 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

21 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

22 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) Total 1828505260 BFI worst fatigue right now at week 6 21 00001 40 1 0001 700 11 02 100000 1 1 Total111115

23 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

24 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)

25 RTOG 0525 Testing 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 1Arm 2 Deterioration Componentn%n%p-value* Symptom51011270.03 Interference71413320.03 --Activity related 81615390.01 -- Mood related 122412300.49 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)

26 Example Symptom Burden on RTOG 0825 Using Grouped Data Improved Deteriorated or No Change Baseline to Specific Time Points Wk 10 No Difference Wk 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

27 RTOG 0825 MDASI-BT Baseline to Week 34 More Deteriorated on Bevacizumab More Improved on Placebo

28 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

29 MDASI-BT and OS Cox Proportional Hazards Model for Overall Survival (RPA & MGMT included) p-value Hazard Ratio (95%CI) Methylation Status (Methylated vs. Not)<.0012.40 (1.81, 3.18) RPA (IV vs. III)0.0021.83 (1.25, 2.66) RPA (V vs. III)<.0013.18 (2.07, 4.88) Baseline Neurologic Factor0.0051.12 (1.04, 1.21) Methylation Status (Methylated vs. Not)<.0012.22 (1.58, 3.12) RPA (IV vs. III)*0.1211.38 (0.92, 2.09) RPA (V vs. III)0.0022.19 (1.33, 3.60) Cognitive Factor0.0021.66 (1.20, 2.29) Baseline Early 

30 Comparative Impact of Treatment on RTOG 0825 MDASI-BT Longitudinal Trends – P-values Study Duration (weeks 0-46) Week Effect* Treatment Effect* Week/Treatment Interaction Effect* MGMT Effect* RPA Effect* Symptom 0.0290.1800.0170.300<0.001 Inference 0.7580.601<0.0010.891<0.001 WAW 0.4430.7320.0040.747<0.001 REM 0.6640.509<0.0010.426<0.001 Affective Factor 0.5080.5250.0380.810<0.001 Cognitive Factor <0.0010.1430.0140.372<0.001 Neurologic Factor 0.0820.0170.1350.7190.003 Treatment Factor 0.0140.8900.0290.021<0.001 Generalized/disease Factor 0.8650.1990.0110.353<0.001 GI Factor <0.0010.1240.8890.7100.041 *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

31 Cognitive Factor Overall Interference Weeks from Randomization MDASI Score 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 Inference Score 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

32 < P r e v > F r o m : L a n c e t O n c o l. A u t h o r m a n u s c r i p t ; a v a i l a b l e i n P M C 2 0 1 2 J u l y 6. L a n c e t O n c o l. A u t h o r m a n u s c r i p t ; a v a i l a b l e i n P M C 2 0 1 2 J u l y 6. P u b l i s h e d i n f i n a l e d i t e d f o r m a s : L a n c e t O n c o l. 2 0 0 8 A u g u s t ; 9 ( 8 ) : 7 7 7 – 7 8 5. d o i : 1 0. 1 0 1 6 / S 1 4 7 0 - 2 0 4 5 ( 0 8 ) 7 0 1 9 7 - 9 C o p y r i g h t / L i c e n s e ► R e q u e s t p e r m i s s i o n t o r e u s e R e q u e s t p e r m i s i o n t o r e u s e F i g u r e 2 C l i c k o n i m a g e t o z o m M o l e c u l a r e p i d e m i o l o g y a p p r o a c h t o c a n c e r - r e l a t e d s y m p t o m s I m a g e s i n t h i s a r t i c l e C l i c k o n t h e i m a g e t o s e e a l a r g e r v e r s i o n. 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

33 Upcoming Peaks Grant# 1 R01 NR013707-01A1; Symptoms-Toxicity-Response Electronic Data Capture


35 ‘IT AIN’T OVER TIL IT’S OVER’ -YOGI BERRA Study Publication

36 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!

37 Special Thanks to the patients and families Who participated in these trials Success is not final, failure is not fatal – it is the courage to continue that matters - Winston Churchill

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