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

Cuer et al. BMC Medical Research Methodology https://doi.org/10.1186/s12874-020-01104-w (2020) 20:223 RESEARCH ARTICLE Open Access 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 B. Cuer1,2,3* , C. Mollevi1,2,3, A. Anota2,4,5, E. Charton2,4,5, B. Juzyna6, T. Conroy7,8 and C. Touraine1,2 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. (Continued on next page) * Correspondence: 1 Biometrics Unit, Montpellier Cancer Institute (ICM), University of Montpellier, 208, avenue des Apothicaires, 34298 Montpellier, France 2 French National Platform Quality of Life and Cancer, Montpellier, France Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Cuer et al. BMC Medical Research Methodology (2020) 20:223 Page 2 of 13 (Continued from previous page) Trial registration: This study is registered with ClinicalTrials.gov, number NCT00861094. Keywords: Pattern-mixture model, Selection model, Shared-parameters model, Joint modeling, Health-related quality of life, Informative dropout, Cancer clinical trial Background Health-related quality of life (HRQoL) is often a secondary endpoint in cancer clinical trials. It is also increasingly being used as a primary or co-primary endpoint [1]. HRQoL is assessed at different time points throughout the care process (at baseline, during treatment, and during follow-up) by self-administered questionnaires composed of items assessing different HRQoL dimensions. The HRQoL outcome to be analyzed consists of longitudinal dimension-specific score data. However, 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. Missing data are classified as missing completely at random (MCAR) if missingness is independent of the (observed or unobserved) HRQoL outcome or depends only on observed characteristics, as missing at random (MAR) if missingness additionally depends on the observed HRQoL outcome, and as missing not at random (MNAR) if missingness is dependent of the unobserved HRQoL outcome [2, 3]. The terms informative or nonignorable are also used to refer to MNAR data. In the presence of incomplete longitudinal outcome data, the strategy of analysis should be adapted to the nature of the missing data mechanism in order to avoid biased or inaccurate results. In most studies, the missing data mechanism is not characterized, so methods used to analyze longitudinal HRQoL data in randomized clinical trials [4] are potentially inadequate. Linear mixed models (LMMs) are powerful and flexible models for the analysis of repeated measures of a continuous outcome. This class of model is classically used to compare changes in HRQoL over time between experimental and control arms in cancer clinical trials [5, 6]. However, the occurrence of intermittent or monotone missing data could compromise the longitudinal analysis of HRQoL data, leading to a loss of statistical power at best, and, at worse, biased estimates; for instance, in palliative or advanced disease situations, where missing data could be related to the health status of patients too ill to complete their HRQoL questionnaires [7, 8]. Likelihood-based methods that use all the observed information (as in LMMs) are valid when the missing data are MAR [9]. However, in the presence of informative missing data (i.e., MNAR), the two processes that are the longitudinal HRQoL outcome and the missing data mechanism have to be jointly modeled to prevent a biased estimation [10, 11]. Since the end of the 1980s, different models have been proposed for the joint distribution of the longitudinal outcome and the missingness proc (...truncated)