title: Predictive event modelling in multicenter clinical trials with waiting time to response.
abstract: A new analytic statistical technique for predictive event modeling in ongoing multicenter clinical trials with waiting time to response is developed. It allows for the predictive mean and predictive bounds for the number of events to be constructed over time, accounting for the newly recruited patients and patients already at risk in the trial, and for different recruitment scenarios. For modeling patient recruitment, an advanced Poisson-gamma model is used, which accounts for the variation in recruitment over time, the variation in recruitment rates between different centers and the opening or closing of some centers in the future. A few models for event appearance allowing for 'recurrence', 'death' and 'lost-to-follow-up' events and using finite Markov chains in continuous time are considered. To predict the number of future events over time for an ongoing trial at some interim time, the parameters of the recruitment and event models are estimated using current data and then the predictive recruitment rates in each center are adjusted using individual data and Bayesian re-estimation. For a typical scenario (continue to recruit during some time interval, then stop recruitment and wait until a particular number of events happens), the closed-form expressions for the predictive mean and predictive bounds of the number of events at any future time point are derived under the assumptions of Markovian behavior of the event progression. The technique is efficiently applied to modeling different scenarios for some ongoing oncology trials. Case studies are considered.
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Harlow, Essex, United Kingdom. Vladimir.V.Anisimov@gmail.com
authors.forename: Vladimir V
authors.initials: VV
authors.lastname: Anisimov
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data*; D018579:Patient Selection; D013997:Time Factors