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
<< PrevNext >>
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