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Van Domelen DR, Mitchell EM, Perkins NJ, Schisterman EF, Manatunga AK, Huang Y, Lyles RH. Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors. Biostatistics 2019; 22:250-265. [PMID: 31373355 DOI: 10.1093/biostatistics/kxz028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 04/05/2019] [Accepted: 06/23/2019] [Indexed: 11/14/2022] Open
Abstract
Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.
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Affiliation(s)
- Dane R Van Domelen
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., Atlanta, GA, USA
| | - Emily M Mitchell
- Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD, USA
| | - Neil J Perkins
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Epidemiology Branch, Division of Intramural Population Health Research, 6710B Rockledge Drive, Bethesda, MD, USA
| | - Enrique F Schisterman
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Epidemiology Branch, Division of Intramural Population Health Research, 6710B Rockledge Drive, Bethesda, MD, USA
| | - Amita K Manatunga
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., Atlanta, GA 30322, USA
| | - Yijian Huang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., Atlanta, GA 30322, USA
| | - Robert H Lyles
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., Atlanta, GA 30322, USA
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Van Domelen DR, Mitchell EM, Perkins NJ, Schisterman EF, Manatunga AK, Huang Y, Lyles RH. Logistic regression with a continuous exposure measured in pools and subject to errors. Stat Med 2018; 37:4007-4021. [PMID: 30022497 DOI: 10.1002/sim.7891] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 05/23/2018] [Accepted: 06/08/2018] [Indexed: 11/07/2022]
Abstract
In a multivariable logistic regression setting where measuring a continuous exposure requires an expensive assay, a design in which the biomarker is measured in pooled samples from multiple subjects can be very cost effective. A logistic regression model for poolwise data is available, but validity requires that the assay yields the precise mean exposure for members of each pool. To account for errors, we assume the assay returns the true mean exposure plus a measurement error (ME) and/or a processing error (PE). We pursue likelihood-based inference for a binary health-related outcome modeled by logistic regression coupled with a normal linear model relating individual-level exposure to covariates and assuming that the ME and PE components are independent and normally distributed regardless of pool size. We compare this approach with a discriminant function-based alternative, and we demonstrate the potential value of incorporating replicates into the study design. Applied to a reproductive health dataset with pools of size 2 along with individual samples and replicates, the model fit with both ME and PE had a lower AIC than a model accounting for ME only. Relative to ignoring errors, this model suggested a somewhat higher (though still nonsignificant) adjusted log-odds ratio associating the cytokine MCP-1 with risk of spontaneous abortion. Simulations modeled after these data confirm validity of the methods, demonstrate how ME and particularly PE can reduce the efficiency advantage of a pooling design, and highlight the value of replicates in improving stability when both errors are present.
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Affiliation(s)
- Dane R Van Domelen
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Emily M Mitchell
- The Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland
| | - Neil J Perkins
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Epidemiology Branch, Division of Intramural Population Health Research, Bethesda, Maryland
| | - Enrique F Schisterman
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Epidemiology Branch, Division of Intramural Population Health Research, Bethesda, Maryland
| | - Amita K Manatunga
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Yijian Huang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Robert H Lyles
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
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O'Brien KM, Upson K, Buckley JP. Lipid and Creatinine Adjustment to Evaluate Health Effects of Environmental Exposures. Curr Environ Health Rep 2018; 4:44-50. [PMID: 28097619 DOI: 10.1007/s40572-017-0122-7] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW Urine- and serum-based biomarkers are useful for assessing individuals' exposure to environmental factors. However, variations in urinary creatinine (a measure of dilution) or serum lipid levels, if not adequately corrected for, can directly impact biomarker concentrations and bias exposure-disease association measures. RECENT FINDINGS Recent methodological literature has considered the complex relationships between creatinine or serum lipid levels, exposure biomarkers, outcomes, and other potentially relevant factors using directed acyclic graphs and simulation studies. The optimal measures of urinary dilution and serum lipids have also been investigated. Existing evidence supports the use of covariate-adjusted standardization plus creatinine adjustment for urinary biomarkers and standardization plus serum lipid adjustment for lipophilic, serum-based biomarkers. It is unclear which urinary dilution measure is best, but all serum lipid measures performed similarly. Future research should assess methods for pooled biomarkers and for studying diseases and exposures that affect creatinine or serum lipids directly.
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Affiliation(s)
- Katie M O'Brien
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, Durham, NC, 27709, USA.
| | - Kristen Upson
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, 111 TW Alexander Dr, Research Triangle Park, Durham, NC, 27709, USA
| | - Jessie P Buckley
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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