ArticleStatistics in medicine2012
Logistic regression analysis of biomarker data subject to pooling and dichotomization.
Article in Statistics in medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Group testing can improve the cost-efficiency of prospective-retrospective biomarker studies.BMC medical research methodology · 2021Article
- Determination of Varying Group Sizes for Pooling Procedure.Computational and mathematical methods in medicine · 2019Article
- A highly efficient design strategy for regression with outcome pooling.Statistics in medicine · 2014Article
- Prevalence estimation subject to misclassification: the mis-substitution bias and some remedies.Statistics in medicine · 2014Article
- Regression for skewed biomarker outcomes subject to pooling.Biometrics · 2014Article
- The biomarker revolution.Statistics in medicine · 2012Article
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Abstract
There is growing interest in pooling specimens across subjects in epidemiologic studies, especially those involving biomarkers. This paper is concerned with regression analysis of epidemiologic data where a binary exposure is subject to pooling and the pooled measurement is dichotomized to indicate either that no subjects in the pool are exposed or that some are exposed, without revealing further information about the exposed subjects in the latter case. The pooling process may be stratified on the disease status (a binary outcome) and possibly other variables but is otherwise assumed random. We propose methods for estimating parameters in a prospective logistic regression model and illustrate these with data from a population-based case-control study of colorectal cancer. Simulation results show that the proposed methods perform reasonably well in realistic settings and that pooling can lead to sizable gains in cost efficiency. We make recommendations with regard to the choice of design for pooled epidemiologic studies.
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