A1 Journal article (refereed)
Bayesian subcohort selection for longitudinal covariate measurements in follow‐up studies (2022)

Reinikainen, J., & Karvanen, J. (2022). Bayesian subcohort selection for longitudinal covariate measurements in follow‐up studies. Statistica Neerlandica, 76(4), 372-390. https://doi.org/10.1111/stan.12264

JYU authors or editors

Publication details

All authors or editors: Reinikainen, Jaakko; Karvanen, Juha

Journal or series: Statistica Neerlandica

ISSN: 0039-0402

eISSN: 1467-9574

Publication year: 2022

Publication date: 23/01/2022

Volume: 76

Issue number: 4

Pages range: 372-390

Publisher: Wiley-Blackwell

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1111/stan.12264

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/83480

Web address of parallel published publication (pre-print): https://arxiv.org/abs/1609.01547


We propose an approach for the planning of longitudinal covariate measurements in follow-up studies where covariates are time-varying. We assume that the entire cohort cannot be selected for longitudinal measurements due to financial limitations, and study how a subset of the cohort should be selected optimally, in order to obtain precise estimates of covariate effects in a survival model. In our approach, the study will be designed sequentially utilizing the data collected in previous measurements of the individuals as prior information. We propose using a Bayesian optimality criterion in the subcohort selections, which is compared with simple random sampling using simulated and real follow-up data. Our work improves the computational approach compared to the previous research on the topic so that designs with several covariates and measurement points can be implemented. As an example we derive the optimal design for studying the effect of body mass index and smoking on all-cause mortality in a Finnish longitudinal study. Our results support the conclusion that the precision of the estimates can be clearly improved by optimal design.

Keywords: follow-up study; longitudinal research; cohort study; sampling (statistical methods); Bayesian analysis

Free keywords: Bayesian optimal design; data collection; follow-up study; longitudinal measurements; study design

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1

Last updated on 2023-10-01 at 15:10