Bayesian credible intervals for binomial proportions in a single patient trial.
Practitioners are often asking if the treatment successfully improved performance. Many times this question is directed towards the outcome of a single individual. In this article, we develop a method to assess the improvement of a single individual who is administered a test of percent correct at pre-treatment and post-treatment. A Bayesian approach is taken where the number correct is modelled as a binomial random variable and the percent correct is set to a beta prior distribution. The first model assumes percent correct at pre-test is equal to the percent correct at post-test and the posterior predictive distribution is used to evaluate the change in the number correct. We subsequently model the proportions correct at pre-test and post-test as unequal. The second model then assumes independent proportions and the third assumes correlated beta distributions for the two proportions. 95% credible intervals are calculated for the various methods for number of correct at post-test given a particular level at pre-test. An example using data from a cochlear implant clinical trial is presented where clinicians recorded percent correct in a consonant-nucleus-consonant test.
Oleson
Jacob J
JJ
Department of Biostatistics, The University of Iowa, 200 Hawkins Drive, The University of Iowa, Iowa City, IA 52242-1009, USA. jacob-oleson@uiowa.edu
D001499:Bayes Theorem; D016010:Binomial Distribution; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003054:Cochlear Implants / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D016896:Treatment Outcome