Data Analysis and Related Applications, Volume 2

9Predicting Event Counts in Event-Driven Clinical Trials Accounting for Cure and Ongoing Recruitment

We consider event-driven clinical trials, where analysis is performed when a pre-determined number of clinical events has occurred, for example, progression in oncology and a stroke in cardiovascular trials. We refer to this number of events as the “sample size”. At the interim stage, one of the main tasks is predicting the number of events over time and the time to reach specific milestones, accounting for events that may occur in patients yet to be recruited. Therefore, in such trials, we need to model patient recruitment and event counts together.

In this chapter, we develop a new analytic approach which accounts for the opportunity of patients to be cured and also for them to dropout and be unavailable for a follow-up.

Recruitment is modeled using a Poisson–gamma model developed in previous publications. For the process of event occurrence, we assume that the time to the main event and the time to dropout are independent random variables and we have developed a few models using exponential, Weibull and log-normal distributions. This technique is supported by well-developed, tested and documented software. The results are illustrated using simulation and a real dataset with reference to the developed software.

9.1. Introduction

An important aspect of event-driven trials is the operational design at the initial and interim stages, i.e. predicting the event counts over time ...