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.

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 .

Time to deterioration in cancer randomized clinical trials for patient-reported outcomes data: a systematic review.

PURPOSE: The time to deterioration (TTD) approach has been proposed as a modality of longitudinal analysis of patient-reported outcomes (PROs) in cancer randomized clinical trials (RCTs). The objective of this study was to perform a systematic review of how the TTD approach has been used in phase III RCTs to analyze longitudinal PRO data. METHODS: A systematic literature search was conducted in PubMed/MEDLINE, the Cochrane Library and through manual search to identify studies published between January 2014 and June 2018. All phase III cancer RCTs including a PRO endpoint using the TTD approach were considered. We collected general information about the study, PRO assessment and the TTD approach, such as the event definition, the choice of reference score and whether the deterioration was definitive or not. RESULTS: A total of 1549 articles were screened, and 39 studies were finally identified as relevant according to predefined criteria. Among these 39 studies, 36 (92.3%) were in advanced and/or metastatic cancer. Several different deterioration definitions were used in RCTs, 10 studies (25.6%) defined the deterioration as "definitive", corresponding to a deterioration maintained over time until the last PRO assessment available for each patient. The baseline score was explicitly stated as the reference score to qualify the deterioration for most studies (n = 31, 79.5%). CONCLUSION: This review highlights the lack of standardization of the TTD approach for the analysis of PRO data in RCTs. Special attention should be paid to the definition of "deterioration", and this should be based on the specific cancer setting.