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Modeling Quality of Life Data with Missing Values

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. Outline. Why measure QOL in oncology? Types of missing data

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Modeling Quality of Life Data with Missing Values

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

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

  3. 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

  4. 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

  5. Missing Data - Definitions • Missing completely at random • Missing at random • Nonignorable

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

  7. Models for MAR data • Generalized linear models • Generalized estimating equations • Weighted methods

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

  9. 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

  10. 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

  11. 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

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

  13. 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)

  14. 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)

  15. SWOG 9045 • 4 assessments • Randomization • 6 weeks • 11 weeks • 21 weeks • 287 patients registered • 272 (95%) submitted baseline questionnaire

  16. QOL Submission Rates

  17. Missing Data Patternsand Reasons

  18. Restrict analysis to subjects who survived for 21 weeks N=227 Submission Rates

  19. Missing Data Patterns

  20. 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.

  21. Results - SDS

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

  23. Sensitivity - SDS

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

  25. Frailty Model – SDS • No significant effect of combination therapy • Frailty variance estimated to be 0.54 • 95%CI (0.28, 0.92) • Significant random subject effect (p < .0001)

  26. Models – Hand/Foot Sensitivity • Yit is a binary indicator of bothersome or worse symptoms • Xi 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)

  27. Results – Hand/Foot Sensitivity

  28. 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)

  29. 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

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