CCEB Modeling Quality of Life Data with Missing Values Andrea B. Troxel, Sc.D. Assistant Professor of Biostatistics Center for Clinical Epidemiology and Biostatistics University of Pennsylvania School of Medicine

CCEB Outline Why measure QOL in oncology? Types of missing data Possible modeling approaches Example: SWOG study of QOL in colorectal cancer

CCEB QOL in Oncology Potentially debilitating effects of treatment Tradeoff between quantity and quality of life An increasingly chronic disease Important focus on survivorship Longitudinal measurements

CCEB Missing Data - Examples Subject moves out of town Researcher forgets to administer questionnaire Subject returns incomplete questionnaire Subject’s family refuses questionnaire Subject is too sick to fill out questionnaire Subject dies

CCEB Missing Data - Definitions Missing completely at random Missing at random Nonignorable

CCEB Modeling Approaches Complete case approaches Models for MAR data Models for NI data Sensitivity analyses Extensions of failure-time models Imputation methods

CCEB Models for MAR data Generalized linear models Generalized estimating equations Weighted methods

CCEB Models for NI data Fully parametric models –Directly model the missingness mechanism –Estimate a nonignorability parameter –Computationally difficult –Untestable assumptions

CCEB Sensitivity Analyses Vary aspects of model and determine effects on inference Local sensitivity analysis –ISNI (Troxel, Ma, and Heitjan, 2005) –Assess sensitivity in the neighborhood of the MAR assumption –Easy to compute and interpret

CCEB Failure-time Models Take advantage of bivariate survival methods Integrate clinical and QOL data Avoid primacy of one outcome over the other Partially handle missing data due to death

CCEB Multiple Imputation Use an appropriate method to create a series of “complete” data sets Use any appropriate method of analysis on each data set Combine the analyses to achieve one reportable result

CCEB SWOG 9045 Companion study to SWOG 8905 –599 subjects with advanced colorectal cancer –Seven arms (!) assessing effectiveness of 5-FU

CCEB SWOG 8905 Variations in –Route of administration »Bolus injection (arms 1-3) »Protracted 28-day continuous infusion (arms 4-5) »Four weekly 24-hour infusions (arms 6-7) –Biochemical modulation »None (arms 1, 4, 6) »Low dose leucovorin (arms 2, 5) »High dose leucovorin (arm 3) »PALA (arm 7)

CCEB SWOG 9045 Five primary outcomes –Mouth pain –Diarrhea –Hand/foot sensitivity –Emotional functioning (SF-36) –Physical functioning (SF-36) Secondary outcome –Symptom distress scale (high scores = more distress)

CCEB SWOG assessments –Randomization –6 weeks –11 weeks –21 weeks 287 patients registered 272 (95%) submitted baseline questionnaire

CCEB QOL Submission Rates Week n % of total % of % of alive

CCEB Missing Data Patterns and Reasons

CCEB Submission Rates Restrict analysis to subjects who survived for 21 weeks N=227 Week N %

CCEB Missing Data Patterns Time Pattern ( 1=submitted, 0=missing)Total n

CCEB Models - SDS Normal GLM –Complete cases –All available data, unweighted –All available data, weighted NI model –Normal component for SDS data –Logistic model for missingness probs.

CCEB Results - SDS

CCEB Sensitivity Analysis Assess sensitivity to nonignorability in the neighborhood of the MAR model Sensitivity of parameters depends on how the model is parameterized

CCEB Sensitivity - SDS EstimateSEISNI* T0(single) T6(single) T11(single) T21(single) T0(comb) T6(comb) T11(comb) T21(comb)

CCEB Frailty Model - SDS SDS>24  SDS “event” Jointly assess survival and SDS events Estimate correlation Estimate covariate effects No special programming required

CCEB Frailty Model – SDS No significant effect of combination therapy Frailty variance estimated to be %CI (0.28, 0.92) Significant random subject effect (p <.0001)

CCEB Models – Hand/Foot Sensitivity Y it is a binary indicator of bothersome or worse symptoms X i is an indicator of continuous infusion vs bolus injection (arms 4,5 vs arms 1-3) N=154 (arms 1-5, alive for 21 weeks)

CCEB Results – Hand/Foot Sensitivity

CCEB Models – Hand/Foot Sensitivity Treatment effect OR estimates –CC:3.1 (1.4 – 7.0) –MAR:2.5 (1.2 – 5.3) –Wtd MAR:2.5 (1.2 – 4.8)

CCEB Conclusions Missing data is a pervasive problem Standard approaches can lead to misleading inferences Sensitivity analysis is a key component Certain comparisons are more susceptible than others