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 editorsReinikainen, Jaakko; Karvanen, Juha

Journal or seriesStatistica Neerlandica

ISSN0039-0402

eISSN1467-9574

Publication year2022

Publication date23/01/2022

Volume76

Issue number4

Pages range372-390

PublisherWiley-Blackwell

Publication countryUnited Kingdom

Publication languageEnglish

DOIhttps://doi.org/10.1111/stan.12264

Publication open accessOpenly available

Publication channel open accessPartially 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


Abstract

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.


Keywordsfollow-up studylongitudinal researchcohort studysampling (statistical methods)Bayesian analysis

Free keywordsBayesian optimal design; data collection; follow-up study; longitudinal measurements; study design


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-03-04 at 17:07