Handling informative dropout in longitudinal analysis of health-related quality of life: application of three approaches to data from the esophageal cancer clinical trial PRODIGE 5/ACCORD 17

Abstract

Background Health-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. Methods We 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. Results We 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). Conclusions The 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 registration This study is registered with ClinicalTrials.gov , number NCT00861094 .

Joint modelling with competing risks of dropout for longitudinal analysis of health-related quality of life in cancer clinical trials

“…As in previous analyses using linear mixed models, no significant difference between the HRQoL score trajectories of the two treatment arms was detected [6,16]. Note that we assumed a linear trend for the HRQoL trajectories for readability and comparability with the simulation study.…”

“…This form of dropout can be informative, leading to a biased estimate of the treatment effect [3]. In order to produce valid results in the analysis of the longitudinal outcome, it is crucial to consider the missing data mechanism [4][5][6].…”

Multiply robust subgroup analysis based on a single‐index threshold linear marginal model for longitudinal data with dropouts

“…In order to correct the selection bias caused by missingness, the proposed IPW method specifies a missingness model, which models the conditional probability of dropout at each visit. Some other methods such as the pattern‐mixture model and the shared‐parameters model 50 can also be used for handling dropouts in longitudinal data analysis, but there are some challenges in applying them to our setting. For the pattern‐mixture model, it may be difficult in practice to define the pattern of missing data.…”

A generalized single‐index linear threshold model for identifying treatment‐sensitive subsets based on multiple covariates and longitudinal measurements

“…In practice, longitudinal data may be missing not at random. Recently, Cuer et al (2020) considered the modelling alternatives that account for informative dropout in the analysis of QoL data. It may also be of interest to consider these alternatives in the context of this article.…”