An alternative characterization of MAR in shared parameter models for incomplete longitudinal data and its utilization for sensitivity analysis. (February 2021)

• Record Type: Journal Article
• Authors: Papageorgiou, Grigorios; Rizopoulos, Dimitris
• Other Names: Armero Carmen guest-editor.; Gómez-Rubio Virgilio guest-editor.
• Is Part Of: Statistical modelling. Volume 21:Number 1/2(2021)
• Page Start: 95
• Page End: 114
• Publication Date: 2021-02
• Subjects: missing data -- Sensitivity analysis -- shared parameter models -- dropout -- MNAR; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X20927114
• Languages: English
• ISSNs: 1471-082X

Abstract

Dropout is a common complication in longitudinal studies, especially since the distinction between missing not at random (MNAR) and missing at random (MAR) dropout is intractable. Consequently, one starts with an analysis that is valid under MAR and then performs a sensitivity analysis by considering MNAR departures from it. To this end, specific classes of joint models, such as pattern-mixture models (PMMs) and selection models (SeMs), have been proposed. On the contrary, shared-parameter models (SPMs) have received less attention, possibly because they do not embody a characterization of MAR. A few approaches to achieve MAR in SPMs exist, but are difficult to implement in existing software. In this article, we focus on SPMs for incomplete longitudinal and time-to-dropout data and propose an alternative characterization of MAR by exploiting the conditional independence assumption, under which outcome and missingness are independent given a set of random effects. By doing so, the censoring distribution can be utilized to cover a wide range of assumptions for the missing data mechanism on the subject-specific level. This approach offers substantial advantages over its counterparts and can be easily implemented in existing software. More specifically, it offers flexibility over the assumption for the missing data generating mechanism that governs dropout by allowing subject-specific perturbations of the censoring distribution, whereas in PMMs and SeMs dropout is considered MNAR strictly.