Design and analysis of two-phase studies with multivariate longitudinal data.

Publisher: Oxford University Press Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE

Publication: March 2024- : [Oxford] : Oxford University Press
Original Publication: Alexandria Va : Biometric Society

Models, Statistical* ; Research Design*; Humans ; Cohort Studies ; Longitudinal Studies ; Retrospective Studies

Two-phase studies are crucial when outcome and covariate data are available in a first-phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the second-phase sample, in whom the exposure is collected. For longitudinal outcomes, one class of two-phase studies stratifies subjects based on an outcome vector summary (e.g., an average or a slope over time) and oversamples subjects in the extreme value strata while undersampling subjects in the medium-value stratum. Based on the choice of the summary, two-phase studies for longitudinal data can increase efficiency of time-varying and/or time-fixed exposure parameter estimates. In this manuscript, we extend efficient, two-phase study designs to multivariate longitudinal continuous outcomes, and we detail two analysis approaches. The first approach is a multiple imputation analysis that combines complete data from subjects selected for phase two with the incomplete data from those not selected. The second approach is a conditional maximum likelihood analysis that is intended for applications where only data from subjects selected for phase two are available. Importantly, we show that both approaches can be applied to secondary analyses of previously conducted two-phase studies. We examine finite sample operating characteristics of the two approaches and use the Lung Health Study (Connett et al. (1993), Controlled Clinical Trials, 14, 3S-19S) to examine genetic associations with lung function decline over time.
(© 2022 The International Biometric Society.)

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R01 HL094786 United States HL NHLBI NIH HHS

Keywords: Lung Health Study; ascertainment corrected maximum likelihood; missing data; multiple imputation; outcome-dependent sampling; secondary outcome analysis

Date Created: 20220111 Date Completed: 20230621 Latest Revision: 20230701

20231215

PMC9392467

10.1111/biom.13616

35014029