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Bayesian adjustment for covariate measurement errors: a flexible parametric approach.
Stat Med. 2009 May 15; 28(11):1580-600.SM

Abstract

In most epidemiological investigations, the study units are people, the outcome variable (or the response) is a health-related event, and the explanatory variables are usually environmental and/or socio-demographic factors. The fundamental task in such investigations is to quantify the association between the explanatory variables (covariates/exposures) and the outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely the relevant covariates are measured. In many instances, we cannot measure some of the covariates accurately. Rather, we can measure noisy (mismeasured) versions of them. In statistical terminology, mismeasurement in continuous covariates is known as measurement errors or errors-in-variables. Regression analyses based on mismeasured covariates lead to biased inference about the true underlying response-covariate associations. In this paper, we suggest a flexible parametric approach for avoiding this bias when estimating the response-covariate relationship through a logistic regression model. More specifically, we consider the flexible generalized skew-normal and the flexible generalized skew-t distributions for modeling the unobserved true exposure. For inference and computational purposes, we use Bayesian Markov chain Monte Carlo techniques. We investigate the performance of the proposed flexible parametric approach in comparison with a common flexible parametric approach through extensive simulation studies. We also compare the proposed method with the competing flexible parametric method on a real-life data set. Though emphasis is put on the logistic regression model, the proposed method is unified and is applicable to the other generalized linear models, and to other types of non-linear regression models as well.

Authors+Show Affiliations

British Columbia Cancer Research Centre, Vancouver, Canada. shossain@bccrc.caNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article

Language

eng

PubMed ID

19226564

Citation

Hossain, Shahadut, and Paul Gustafson. "Bayesian Adjustment for Covariate Measurement Errors: a Flexible Parametric Approach." Statistics in Medicine, vol. 28, no. 11, 2009, pp. 1580-600.
Hossain S, Gustafson P. Bayesian adjustment for covariate measurement errors: a flexible parametric approach. Stat Med. 2009;28(11):1580-600.
Hossain, S., & Gustafson, P. (2009). Bayesian adjustment for covariate measurement errors: a flexible parametric approach. Statistics in Medicine, 28(11), 1580-600. https://doi.org/10.1002/sim.3552
Hossain S, Gustafson P. Bayesian Adjustment for Covariate Measurement Errors: a Flexible Parametric Approach. Stat Med. 2009 May 15;28(11):1580-600. PubMed PMID: 19226564.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Bayesian adjustment for covariate measurement errors: a flexible parametric approach. AU - Hossain,Shahadut, AU - Gustafson,Paul, PY - 2009/2/20/entrez PY - 2009/2/20/pubmed PY - 2009/7/11/medline SP - 1580 EP - 600 JF - Statistics in medicine JO - Stat Med VL - 28 IS - 11 N2 - In most epidemiological investigations, the study units are people, the outcome variable (or the response) is a health-related event, and the explanatory variables are usually environmental and/or socio-demographic factors. The fundamental task in such investigations is to quantify the association between the explanatory variables (covariates/exposures) and the outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely the relevant covariates are measured. In many instances, we cannot measure some of the covariates accurately. Rather, we can measure noisy (mismeasured) versions of them. In statistical terminology, mismeasurement in continuous covariates is known as measurement errors or errors-in-variables. Regression analyses based on mismeasured covariates lead to biased inference about the true underlying response-covariate associations. In this paper, we suggest a flexible parametric approach for avoiding this bias when estimating the response-covariate relationship through a logistic regression model. More specifically, we consider the flexible generalized skew-normal and the flexible generalized skew-t distributions for modeling the unobserved true exposure. For inference and computational purposes, we use Bayesian Markov chain Monte Carlo techniques. We investigate the performance of the proposed flexible parametric approach in comparison with a common flexible parametric approach through extensive simulation studies. We also compare the proposed method with the competing flexible parametric method on a real-life data set. Though emphasis is put on the logistic regression model, the proposed method is unified and is applicable to the other generalized linear models, and to other types of non-linear regression models as well. SN - 0277-6715 UR - https://www.unboundmedicine.com/medline/citation/19226564/Bayesian_adjustment_for_covariate_measurement_errors:_a_flexible_parametric_approach_ DB - PRIME DP - Unbound Medicine ER -