title: On the joint analysis of longitudinal responses and early discontinuation in randomized trials.
abstract: Our focus is on the joint analysis of longitudinal nonnormal responses and early discontinuation in (pre)-clinical trials. Separate models are fitted to the two series (response and discontinuation) to account for covariate and time effects. The serial dependence and the dependence between response and drop-out are also modeled. This is done using particular dependence functions, called copulas. Copulas are used to create a joint distribution with given marginal distributions. Applications are given for the analysis of heart rate/morbidity in toxicology and pain severity/intake of rescue medications in a trial on migraine. Using copulas, the level of dependence between two variables remains invariant to changes in the marginal distribution of either variable. This proves interesting in modeling the association in a longitudinal setting when responses change over time.
authors.affiliation: Statistics and Information Sciences, Eli Lilly & Company, B-1348 Mont-Saint-Guibert, Belgium. francois@lilly.com
authors.forename: François
authors.initials: F
authors.lastname: Vandenhende
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambert
mesh_terms: D000465:Algorithms; D000818:Animals; D006339:Heart Rate / Q000187:drug effects; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D008881:Migraine Disorders / Q000188:drug therapy; D015233:Models, Statistical; D010352:Patient Dropouts; D011338:Probability Theory; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D051381:Rats; D012107:Research Design / Q000706:statistics & numerical data*