Comparing methods for calculating confidence intervals for vaccine efficacy.
1996 Nov 15-30
AIDS Vaccines; Bayes Theorem; Confidence Intervals; Data Interpretation, Statistical; Humans; Incidence; Models, Statistical; Poisson Distribution; Probability; Randomized Controlled Trials as Topic; Research Design; Treatment Outcome
A method is introduced for computing a Bayesian 95 per cent posterior probability region for vaccine efficacy. This method assumes independent vague gamma prior distributions for the incidence rates on each arm of the trial, and a Poisson likelihood for the counts of incident cases of infection. The approach is similar in spirit to the Bayesian analysis of the binomial risk ratio described by Aitchison and Bacon-Shone. However, the focus of our interest is not on incorporating prior information into the design of trials for efficacy, but rather on evaluating whether or not the Bayesian approach with vague prior information produces comparable results to a frequentist approach. A review of methods for constructing exact and large sample intervals for vaccine efficacy is provided as a framework for comparison. The confidence interval methods are assessed by comparing the size and power of tests of vaccine efficacy in proposed intermediate sized randomized double blinded placebo controlled trials.