Optimal generalized case-cohort sampling design under the additive hazard model

Generalized case-cohort designs have been proved to be a cost-effective way to enhance effectiveness in large epidemiological cohort. In generalized case-cohort design, we first select a subcohort from the underlying cohort by simple random sampling, and then sample a subset of the failures in the r...

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Published inCommunications in statistics. Theory and methods Vol. 46; no. 9; pp. 4484 - 4493
Main Authors Cao, Yongxiu, Yu, Jichang
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.05.2017
Taylor & Francis Ltd
Subjects
Additives
Case-cohort
Economic models
Failure
generalized case-cohort
Hazards
Maximum power
optimal allocation
Optimization
Samples
Statistical analysis
Statistical methods
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Summary:Generalized case-cohort designs have been proved to be a cost-effective way to enhance effectiveness in large epidemiological cohort. In generalized case-cohort design, we first select a subcohort from the underlying cohort by simple random sampling, and then sample a subset of the failures in the remaining subjects. In this article, we propose the inference procedure for the unknown regression parameters in the additive hazards model and develop an optimal sample size allocations to achieve maximum power at a given budget in generalized case-cohort design. The finite sample performance of the proposed method is evaluated through simulation studies. The proposed method is applied to a real data set from the National Wilm's Tumor Study Group.
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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2015.1085563