Exposure misclassification: bias in category specific Poisson regression coefficients

2001 ◽  
Vol 20 (5) ◽  
pp. 771-784 ◽  
Author(s):  
Marit B. Veierød ◽  
Petter Laake
Keyword(s):  
Poisson Regression ◽  
Regression Coefficients ◽  
Exposure Misclassification ◽  
Misclassification Bias

scholarly journals Exposure misclassification bias in the estimation of vaccine effectiveness

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251622
Author(s):  
Ulrike Baum ◽  
Sangita Kulathinal ◽  
Kari Auranen
Keyword(s):  
Sensitivity And Specificity ◽  
Vaccination Coverage ◽  
Risk Ratio ◽  
Vaccine Effectiveness ◽  
Relative Reduction ◽  
Common Source ◽  
Exposure Misclassification ◽  
Misclassification Bias ◽  
Risk Of Disease ◽  
Differential Exposure

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naïve estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.

scholarly journals A simulation study of regression approaches for estimating risk ratios in the presence of multiple confounders

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Kanako Fuyama ◽  
Yasuhiro Hagiwara ◽  
Yutaka Matsuyama
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Risk Ratio ◽  
Simulation Study ◽  
Poisson Regression ◽  
Regression Coefficients ◽  
Confidence Interval Coverage ◽  
Standard Error Estimation ◽  
Unbiased Risk ◽  
Risk Ratios

Abstract Background Risk ratio is a popular effect measure in epidemiological research. Although previous research has suggested that logistic regression may provide biased odds ratio estimates when the number of events is small and there are multiple confounders, the performance of risk ratio estimation has yet to be examined in the presence of multiple confounders. Methods We conducted a simulation study to evaluate the statistical performance of three regression approaches for estimating risk ratios: (1) risk ratio interpretation of logistic regression coefficients, (2) modified Poisson regression, and (3) regression standardization using logistic regression. We simulated 270 scenarios with systematically varied sample size, the number of binary confounders, exposure proportion, risk ratio, and outcome proportion. Performance evaluation was based on convergence proportion, bias, standard error estimation, and confidence interval coverage. Results With a sample size of 2500 and an outcome proportion of 1%, both logistic regression and modified Poisson regression at times failed to converge, and the three approaches were comparably biased. As the outcome proportion or sample size increased, modified Poisson regression and regression standardization yielded unbiased risk ratio estimates with appropriate confidence intervals irrespective of the number of confounders. The risk ratio interpretation of logistic regression coefficients, by contrast, became substantially biased as the outcome proportion increased. Conclusions Regression approaches for estimating risk ratios should be cautiously used when the number of events is small. With an adequate number of events, risk ratios are validly estimated by modified Poisson regression and regression standardization, irrespective of the number of confounders.

The impact of periodontitis exposure misclassification bias from partial‐mouth measurements on association with diabetes and cardiovascular disease

2020 ◽  
Vol 47 (12) ◽  
pp. 1457-1465
Author(s):  
Talal S. Alshihayb ◽  
Elizabeth A. Kaye ◽  
Yihong Zhao ◽  
Cataldo W. Leone ◽  
Brenda Heaton
Keyword(s):  
Cardiovascular Disease ◽  
Exposure Misclassification ◽  
Misclassification Bias ◽  
The Impact
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