ArticleStatistics in medicine2017
A general framework for the regression analysis of pooled biomarker assessments.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Additive partially linear model for pooled biomonitoring data.Computational statistics & data analysis · 2024Article
- Assessing disparities in Americans' exposure to PCBs and PBDEs based on NHANES pooled biomonitoring data.Journal of the American Statistical Association · 2023Article
- Varying-coefficient regression analysis for pooled biomonitoring.Biometrics · 2022Article
- Local polynomial regression for pooled response data.Journal of nonparametric statistics · 2020Article
- Logistic regression with a continuous exposure measured in pools and subject to errors.Statistics in medicine · 2018Article
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Authors and funding
3 authors.
Funding
Abstract
As a cost-efficient data collection mechanism, the process of assaying pooled biospecimens is becoming increasingly common in epidemiological research; for example, pooling has been proposed for the purpose of evaluating the diagnostic efficacy of biological markers (biomarkers). To this end, several authors have proposed techniques that allow for the analysis of continuous pooled biomarker assessments. Regretfully, most of these techniques proceed under restrictive assumptions, are unable to account for the effects of measurement error, and fail to control for confounding variables. These limitations are understandably attributable to the complex structure that is inherent to measurements taken on pooled specimens. Consequently, in order to provide practitioners with the tools necessary to accurately and efficiently analyze pooled biomarker assessments, herein, a general Monte Carlo maximum likelihood-based procedure is presented. The proposed approach allows for the regression analysis of pooled data under practically all parametric models and can be used to directly account for the effects of measurement error. Through simulation, it is shown that the proposed approach can accurately and efficiently estimate all unknown parameters and is more computational efficient than existing techniques. This new methodology is further illustrated using monocyte chemotactic protein-1 data collected by the Collaborative Perinatal Project in an effort to assess the relationship between this chemokine and the risk of miscarriage. Copyright © 2017 John Wiley & Sons, Ltd.
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