Event-driven type design for clinical trials with recurrent events

TL;DR

This work addresses the challenge of designing trials with recurrent events by extending event-driven principles to account for within-subject dependence. It develops an event-driven type design that blinds the treatment allocation and monitors the robust variance $v^2$ under the marginal means model to guarantee the target power for the log-rate ratio $\beta_0$, regardless of nuisance quantities. The authors derive a blinded estimator $\hat{v}_{blind}^2$, validate the approach through extensive simulations against fixed designs, and demonstrate practical utility with a real CGD trial dataset. The method enhances trial efficiency and robustness against misspecification, offering a principled, allocation-blinded framework for recurrent-event trials that preserves statistical properties while allowing ongoing decision-making.

Abstract

It is a common practice in randomized clinical trials with the standard survival outcome to follow patients until a prespecified number of events have been observed, a type of trial known as the event-driven trial. The event-driven design ensures that the target power for a specified type 1 error rate is achieved to detect the target hazard ratio, regardless of the specification of other quantities. To understand the treatment effect for chronic conditions, the analysis of recurrent events has gained popularity in randomized controlled trials, particularly large-scale confirmatory trials. In the absence of within-subject correlation among multiple events, a similar event-driven design can be employed for recurrent event outcomes. On the other hand, in the presence of the within-subject correlation, one needs to model the correlation among recurrent events in evaluating power and setting the sample size. However, information useful in modeling the within-subject correlation is limited at the design stage. Failing to consider the correlation properly may lead to underpowered studies. We propose an event-driven type design for recurrent event outcomes. Our method ensures the target power for the target treatment effect, regardless of the specification of other quantities, by monitoring the robust variance under the marginal rates/means model in a blinded manner. We investigate the operating characteristics of the proposed monitoring procedure in simulation studies. The results of simulation studies showed that the proposed blinded monitoring procedure controlled the power well so that the test possessed the target power and did not lead to serious inflation of the type 1 error rate. Furthermore, we illustrate the proposed method using a real clinical trial dataset.

Figures (1)

• Figure 1: (a) shows the changes in $\hat{v}^2$ (dark blue curve) over monitoring time, with the light blue shaded region representing the bootstrapped confidence interval for $\hat{v}^2$. The red curve corresponds to the changes in event counts over time. (b) shows the transformed predicted power and corresponding bootstrapped upper and lower bounds using equation \ref{['power']} with $\beta_0=\log(0.3)$ and two-sided significance level $\alpha=0.05$ over monitoring time.