Bayesian Logistic Regression Modeling as a Flexible Alternative forEstimating Adjusted Risk Ratios in Studies with Common Outcomes

Journal of Biometrics & Biostatistics

article

Charles E Rose¹  Yi Pan¹  Andrew L Baughman² 

[1] Division of HIV/AIDS Prevention, Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention;Division of Global HIV/AIDS, Center for Global Health, Centers for Disease Control and Prevention

关键词: Bayesian logistic regression;    Log-binomial;    Poisson regression;    Prevalence ratio;    Risk ratio;   

DOI  :  10.4172/2155-6180.1000253

来源: Hilaris Publisher

【 摘 要 】

Background: For cohort and cross-sectional studies, the risk ratio (RR) is the preferred measure of effect rather than an odds ratio (OR), especially when the outcome is common (>10%). The log-binomial (LB) and Poisson models are commonly used to estimate the RR; the OR estimated using logistic regression is often used to approximate the RR when the outcome is rare. However, regardless of the prevalence of the outcome, logistic regression predicted exposed and unexposed risks may be used to estimate the RR. Because maximum likelihood estimation is used to fit the logistic model, estimation of the SE of the RR is difficult. Methods: To overcome difficulty in estimation of the SE of the RR and provide a flexible framework for modeling, we developed a Bayesian logistic regression (BLR) model to estimate the RR, with associated credible interval (CIB). We applied the BLR model to a large hypothetical cross-sectional study with categorical variables and to a small hypothetical clinical trial with a continuous variable for which the LB method did not converge. Results of the BLR model were compared to those from several commonly used RR modeling methods. Results: Our examples illustrate the Bayesian logistic regression model estimates adjusted RRs and 95% CIBs comparable to results from other methods. Adjusted risks and risk differences were easily obtained from the posterior distribution. Conclusions: The Bayesian logistic regression modeling approach compares favorably with existing RR modeling methods and provides a flexible framework for investigating confounding and effect modification on the risk scale.

【 授权许可】

Unknown