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
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.
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
JUFO rating: 1