Analysis of Longitudinal Quality of Life Data with Informative Dropout
An important objective of many clinical trials is the estimation and comparison of longitudinal changes in quality-of-life measurements. One difficulty encountered in these trials is that participants may drop out of the study due to worsening disease and miss follow-up evaluations after the time of dropout. When the dropout process depends on the true quality-of-life measurement, it is called informative dropout. If the analysis of the data ignores information about the informative dropout process, the estimates of changes in quality-of-life measurements may be biased. In this chapter we review several approaches for adjusting for informative dropout. We consider the situations where the probability of dropout depends on only the last observed measurement prior to dropout, on only the unobserved measurement at the time of dropout, and on both of these measurements. We illustrate these concepts with an analysis of a clinical trial for the treatment of asthma and provide examples of emphysema and prostate cancer treatment trials in which these approaches are also applicable.
KeywordsMean Square Error Lung Volume Reduction Surgery Observe Response Simulated Clinical Trial Asthma Symptom Score
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