Multiplicative rates model for recurrent events in case-cohort studies

Poulami Maitra (), Leila D. A. F. Amorim () and Jianwen Cai ()
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Poulami Maitra: University of North Carolina at Chapel Hill
Leila D. A. F. Amorim: Federal University of Bahia
Jianwen Cai: University of North Carolina at Chapel Hill

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 1, No 7, 134-157

Abstract: Abstract In large prospective cohort studies, accumulation of covariate information and follow-up data make up the majority of the cost involved in the study. This might lead to the study being infeasible when there are some expensive variables and/or the event is rare. Prentice (Biometrika 73(1):1–11, 1986) proposed the case-cohort study for time to event data to tackle this problem. There has been extensive research on the analysis of univariate and clustered failure time data, where the clusters are formed among different individuals under case-cohort sampling scheme. However, recurrent event data are quite common in biomedical and public health research. In this paper, we propose case-cohort sampling schemes for recurrent events. We consider a multiplicative rates model for the recurrent events and propose a weighted estimating equations approach for parameter estimation. We show that the estimators are consistent and asymptotically normally distributed. The proposed estimator performed well in finite samples in our simulation studies. For illustration purposes, we examined the association between prior occurrence of measles on acute lower respiratory tract infections (ALRI) among young children in Brazil.

Keywords: Generalized case-cohort design; Recurrent events; Correlated data; Acute lower respiratory tract infections (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-019-09466-0

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