Abstract
BackgroundHealth-related quality of life (HRQoL) has become a major endpoint to assess the clinical benefit of new therapeutic strategies in oncology clinical trials. Typically, HRQoL outcomes are analyzed using linear mixed models (LMMs). However, longitudinal analysis of HRQoL in the presence of missing data remains complex and unstandardized. Our objective was to compare the modeling alternatives that account for informative dropout.MethodsWe investigated three alternative methods—the selection model (SM), pattern-mixture model (PMM), and shared-parameters model (SPM)—in relation to the LMM. We first compared them on the basis of methodological arguments highlighting their advantages and drawbacks. Then, we applied them to data from a randomized clinical trial that included 267 patients with advanced esophageal cancer for the analysis of four HRQoL dimensions evaluated using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire.ResultsWe highlighted differences in terms of outputs, interpretation, and underlying modeling assumptions; this methodological comparison could guide the choice of method according to the context. In the application, none of the four models detected a significant difference between the two treatment arms. The estimated effect of time on HRQoL varied according to the method: for all analyzed dimensions, the PMM estimated an effect that contrasted with those estimated by the SM and SPM; the LMM estimated effects were confirmed by the SM (on two of four HRQoL dimensions) and SPM (on three of four HRQoL dimensions).ConclusionsThe PMM, SM, or SPM should be used to confirm or invalidate the results of LMM analysis when informative dropout is suspected. Of these three alternative methods, the SPM appears to be the most interesting from both theoretical and practical viewpoints.Trial registrationThis study is registered with ClinicalTrials.gov, number NCT00861094.
Highlights
Health-related quality of life (HRQoL) has become a major endpoint to assess the clinical benefit of new therapeutic strategies in oncology clinical trials
We modeled the joint distribution of the longitudinal outcome and the dropout process using three models that are valid under the missing not at random (MNAR) assumption: the selection model (SM) and the pattern-mixture model (PMM), which are based on the two existing and converse factorizations of the joint distribution, and the shared-parameters model (SPM), where the longitudinal outcome and the time-to-dropout are linked through a function of the random effects
The SM explains the probability of dropout by a logistic regression; the PMM estimates the probability of belonging to a certain pattern of dropout with a multinomial distribution; the SPM uses a survival model for the time-to-dropout
Summary
Health-related quality of life (HRQoL) has become a major endpoint to assess the clinical benefit of new therapeutic strategies in oncology clinical trials. Health-related quality of life (HRQoL) is often a secondary endpoint in cancer clinical trials. It is increasingly being used as a primary or co-primary endpoint [1]. The HRQoL outcome to be analyzed consists of longitudinal dimension-specific score data. The rate of completed questionnaires generally decreases over time and, in addition, some items may be missing among available questionnaires. This leads to missing data that are said to be monotone if the score is not available from a certain time point until the end of the study, and intermittent otherwise. The nature of the missing data mechanism depends on how the missingness is related to the HRQoL outcome
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