Statistics in Biosciences

, Volume 8, Issue 2, pp 234–252 | Cite as

Hypothesis Testing for an Exposure–Disease Association in Case–Control Studies Under Nondifferential Exposure Misclassification in the Presence of Validation Data: Bayesian and Frequentist Adjustments

Article

Abstract

In epidemiologic studies, measurement error in the exposure variable can have a detrimental effect on the power of hypothesis testing for detecting the impact of exposure in the development of a disease. To adjust for misclassification in the hypothesis testing procedure involving a misclassified binary exposure variable, we consider a retrospective case–control scenario under the assumption of nondifferential misclassification. We develop a test under Bayesian approach from a posterior distribution generated by a MCMC algorithm and a normal prior under realistic assumptions. We compared this test with an equivalent likelihood ratio test developed under the frequentist approach, using various simulated settings and in the presence or the absence of validation data. In our simulations, we considered varying degrees of sensitivity, specificity, sample sizes, exposure prevalence, and proportion of unvalidated and validated data. In these scenarios, our simulation study shows that the adjusted model (with-validation data model) is always better than the unadjusted model (without validation data model). However, we showed that exception is possible in the fixed budget scenario where collection of the validation data requires a much higher cost. We also showed that both Bayesian and frequentist hypothesis testing procedures reach the same conclusions for the scenarios under consideration. The Bayesian approach is, however, computationally more stable in rare exposure contexts. A real case–control study was used to show the application of the hypothesis testing procedures under consideration.

Keywords

Bayesian methods Case–control study Exposure misclassification Nondifferential assumption Hypothesis testing Validation data 

Supplementary material

12561_2015_9141_MOESM1_ESM.pdf (1.8 mb)
Supplementary material 1 (pdf 1808 KB)

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Copyright information

© International Chinese Statistical Association 2016

Authors and Affiliations

  1. 1.Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealCanada
  2. 2.Department of StatisticsUniversity of British ColumbiaVancouverCanada

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