title: Complementary log-log regression for the estimation of covariate-adjusted prevalence ratios in the analysis of data from cross-sectional studies.
abstract: We assessed complementary log-log (CLL) regression as an alternative statistical model for estimating multivariable-adjusted prevalence ratios (PR) and their confidence intervals. Using the delta method, we derived an expression for approximating the variance of the PR estimated using CLL regression. Then, using simulated data, we examined the performance of CLL regression in terms of the accuracy of the PR estimates, the width of the confidence intervals, and the empirical coverage probability, and compared it with results obtained from log-binomial regression and stratified Mantel-Haenszel analysis. Within the range of values of our simulated data, CLL regression performed well, with only slight bias of point estimates of the PR and good confidence interval coverage. In addition, and importantly, the computational algorithm did not have the convergence problems occasionally exhibited by log-binomial regression. The technique is easy to implement in SAS (SAS Institute, Cary, NC), and it does not have the theoretical and practical issues associated with competing approaches. CLL regression is an alternative method of binomial regression that warrants further assessment.
authors: Department of Medicine, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216, USA. apenman@medicine.umsmed.edu
authors: Alan D
authors: AD
authors: Penman
authors: William D
authors: WD
authors: Johnson
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D015995:Prevalence; D012044:Regression Analysis