Shared random parameter models: A legacy of the biostatistics program at the National Heart, Lung, and Blood Institute

• Published in: Statistics in medicine Vol. 38; no. 4; pp. 501 - 511
• Main Author: Albert, Paul S.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.02.2019
• Subjects: Biostatistics; Health risk assessment; Humans; joint models; Longitudinal Studies; Missing data; Models, Statistical; National Heart, Lung, and Blood Institute (U.S.); nonignorable missing data; Patient Dropouts; random effects; repeated measures; risk prediction; United States; Within-subjects design; United States; random effects; joint models; risk prediction; nonignorable missing data; repeated measures
• DOI: 10.1002/sim.8011
• ISSN: 0277-6715; 1097-0258

Shared random parameter models (SRPMs) were first introduced by researchers at the National Heart Lung and Blood Institute (NHLBI) Biostatistics Branch for analyzing longitudinal data with informative dropout (Wu and Carroll, 1987; Wu and Bailey, 1988; Follmann and Wu, 1995; Albert and Follmann, 2000; Albert et al, 2002). This work was all focused on characterizing the longitudinal data process in the presence of an informative missing data mechanism that is treated as a nuisance. Shared random parameter modeling approaches have also been developed from the perspective of characterizing the relationship between longitudinal data and a subsequent outcome that may be an event time, a dichotomous measurement, or another longitudinal outcome. This article will review the early contributions of the NHLBI biostatisticians on SRPMs for analyzing longitudinal data with dropout and demonstrate how these ideas have, more recently, been applied in these other areas of biostatistics. Rather than focus on technical details or specific analyses, this article presents a conceptual framework for SRPMs within a historical context.