ArticleBiometrics2014
Regression for skewed biomarker outcomes subject to pooling.
Article in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 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
- Prediction-driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda.Statistics in medicine · 2021Article
- Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.Biostatistics (Oxford, England) · 2021Article
- STATISTICAL METHODS FOR ANALYSIS OF COMBINED CATEGORICAL BIOMARKER DATA FROM MULTIPLE STUDIES.The annals of applied statistics · 2020Article
- 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
- Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents.Statistics in medicine · 2018Article
- Group testing case identification with biomarker information.Computational statistics & data analysis · 2018Article
- A general framework for the regression analysis of pooled biomarker assessments.Statistics in medicine · 2017Article
- Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments.Biometrical journal. Biometrische Zeitschrift · 2016Article
- Pooling Bio-Specimens in the Presence of Measurement Error and Non-Linearity in Dose-Response: Simulation Study in the Context of a Birth Cohort Investigating Risk Factors for Autism Spectrum Disorders.International journal of environmental research and public health · 2015Article
- A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples.International journal of environmental research and public health · 2015Article
- Positing, fitting, and selecting regression models for pooled biomarker data.Statistics in medicine · 2015Article
- Semiparametric regression models for a right-skewed outcome subject to pooling.American journal of epidemiology · 2015Article
- A highly efficient design strategy for regression with outcome pooling.Statistics in medicine · 2014Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Epidemiological studies involving biomarkers are often hindered by prohibitively expensive laboratory tests. Strategically pooling specimens prior to performing these lab assays has been shown to effectively reduce cost with minimal information loss in a logistic regression setting. When the goal is to perform regression with a continuous biomarker as the outcome, regression analysis of pooled specimens may not be straightforward, particularly if the outcome is right-skewed. In such cases, we demonstrate that a slight modification of a standard multiple linear regression model for poolwise data can provide valid and precise coefficient estimates when pools are formed by combining biospecimens from subjects with identical covariate values. When these x-homogeneous pools cannot be formed, we propose a Monte Carlo expectation maximization (MCEM) algorithm to compute maximum likelihood estimates (MLEs). Simulation studies demonstrate that these analytical methods provide essentially unbiased estimates of coefficient parameters as well as their standard errors when appropriate assumptions are met. Furthermore, we show how one can utilize the fully observed covariate data to inform the pooling strategy, yielding a high level of statistical efficiency at a fraction of the total lab cost.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.