Combining Historical and Randomized Controls for Assessing Trends in Proportions

Abstract

Abstract A statistical method for incorporating historical control data in the analysis of proportions is proposed and illustrated. The method has as its extremes logistic regressions completely pooling and completely ignoring historical controls. The degree of pooling used is determined by the variability from experiment to experiment in the control incidences. The fit of historical control groups to an assumed normal logistic model is assessed using probability plotting techniques. Monte Carlo studies evaluate the adequacy of the asymptotic approximation used. Sensitivity analyses show that results are insensitive to alternative priors. The method is applied to several sets of tumor data from animal experiments.