Cost-Effective Extreme Case-Control Design Using a Resampling Method

Young Min Kim, Jongho Im

Research output: Contribution to journalArticlepeer-review

Abstract

Nested case-control sampling design is a popular method in a cohort study whose events are often rare. The controls are randomly selected with or without the matching variable fully observed across all cohort samples to control confounding factors. In this article, we propose a new nested case-control sampling design incorporating both extreme case-control design and a resampling technique. This new algorithm has two main advantages with respect to the conventional nested case-control design. First, it inherits the strength of extreme case-control design such that it does not require the risk sets in each event time to be specified. Second, the target number of controls can only be determined by the budget and time constraints and the resampling method allows an under sampling design, which means that the total number of sampled controls can be smaller than the number of cases. A simulation study demonstrated that the proposed algorithm performs well even when we have a smaller number of controls compared with the number of cases. The proposed sampling algorithm is applied to a public data collected for “Thorotrast Study.”

Original languageEnglish
JournalEvolutionary Bioinformatics
Volume15
DOIs
Publication statusPublished - 2019 Apr 1

Bibliographical note

Funding Information:
FUnDIng: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the National Research Foundation (NRF) of Korea, NRF-2016R1D1A1B03932212 for the author and NRF-2018R1D1A1B07045220 for the second author, respectively.

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Computer Science Applications

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