title: Continuous event monitoring via a Bayesian predictive approach.
abstract: In clinical trials, continuous monitoring of event incidence rate plays a critical role in making timely decisions affecting trial outcome. For example, continuous monitoring of adverse events protects the safety of trial participants, while continuous monitoring of efficacy events helps identify early signals of efficacy or futility. Because the endpoint of interest is often the event incidence associated with a given length of treatment duration (e.g., incidence proportion of an adverse event with 2 years of dosing), assessing the event proportion before reaching the intended treatment duration becomes challenging, especially when the event onset profile evolves over time with accumulated exposure. In particular, in the earlier part of the study, ignoring censored subjects may result in significant bias in estimating the cumulative event incidence rate. Such a problem is addressed using a predictive approach in the Bayesian framework. In the proposed approach, experts' prior knowledge about both the frequency and timing of the event occurrence is combined with observed data. More specifically, during any interim look, each event-free subject will be counted with a probability that is derived using prior knowledge. The proposed approach is particularly useful in early stage studies for signal detection based on limited information. But it can also be used as a tool for safety monitoring (e.g., data monitoring committee) during later stage trials. Application of the approach is illustrated using a case study where the incidence rate of an adverse event is continuously monitored during an Alzheimer's disease clinical trial. The performance of the proposed approach is also assessed and compared with other Bayesian and frequentist methods via simulation.
authors.affiliation: Janssen Research and Development, LLC, USA.
authors.forename: Jianing
authors.initials: J
authors.lastname: Di
authors.affiliation: Janssen Research and Development, LLC, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Wang
authors.affiliation: Janssen Research and Development, LLC, USA.
authors.forename: H Robert
authors.initials: HR
authors.lastname: Brashear
authors.affiliation: Janssen Research and Development, LLC, USA.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Dragalin
authors.affiliation: Janssen Research and Development, LLC, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Krams
mesh_terms: D000544:Alzheimer Disease / Q000188:drug therapy; D061067:Antibodies, Monoclonal, Humanized / Q000009:adverse effects / Q000627:therapeutic use; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D016903:Drug Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005544:Forecasting; D006801:Humans; D015233:Models, Statistical
chemical_list: D061067:Antibodies, Monoclonal, Humanized; C545458:bapineuzumab
keywords: Bayesian; adaptive design; clinical trial; continuous event monitoring