title: Estimation of the treatment effect under an incomplete block crossover design in binary data - A conditional likelihood approach.
abstract: A random effects logistic regression model is proposed for an incomplete block crossover trial comparing three treatments when the underlying patient response is dichotomous. On the basis of the conditional distributions, the conditional maximum likelihood estimator for the relative effect between treatments and its estimated asymptotic standard error are derived. Asymptotic interval estimator and exact interval estimator are also developed. Monte Carlo simulation is used to evaluate the performance of these estimators. Both asymptotic and exact interval estimators are found to perform well in a variety of situations. When the number of patients is small, the exact interval estimator with assuring the coverage probability larger than or equal to the desired confidence level can be especially of use. The data taken from a crossover trial comparing the low and high doses of an analgesic with a placebo for the relief of pain in primary dysmenorrhea are used to illustrate the use of estimators and the potential usefulness of the incomplete block crossover design.
authors.affiliation: Department of Mathematics and Statistics, College of Sciences, San Diego State University, San Diego, CA, USA.
authors.forename: Kung-Jong
authors.initials: KJ
authors.lastname: Lui
mesh_terms: D000700:Analgesics / Q000008:administration & dosage / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000379:methods; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D009010:Monte Carlo Method; D010146:Pain / Q000188:drug therapy; D016896:Treatment Outcome
chemical_list: D000700:Analgesics
keywords: Crossover trial; conditional maximum likelihood estimator; dichotomous responses; exact interval estimator; incomplete block