Decision Policies for Early Stage Clinical Trials with Multiple Endpoints

Abstract

Before a drug can be prescribed to patients, it must be shown to be safe and effective for a certain indication in a controlled clinical trial (known as Phase III). Such studies are costly to run and expose patients to potential risks. Therefore, after initial studies in human subjects show the drug’s safety (Phase I), studies with a small number of patients are run to assess the prospects of the drug (Phase II). If the number of patients in a Phase II study is not be sufficient to detect differences in the variable of interest (e.g. number of hospitalizations due to heart failure); a surrogate variable which is predictive of the variable of interest is used instead. A decision framework originally proposed by Lalonde (2007) is used in industry to determine, based on a single surrogate endpoint, whether to “Go” ahead with a Phase III study, or to “Stop” development of the drug. In some therapeutic areas, a single endpoint is not sufficient to predict the Phase III variable of interest; several related endpoints are used instead. Endpoints which are considered clinically related may be grouped into domains. How to best combine several disease markers across different domains to achieve the desired probabilities of correct/incorrect decisions is an open question. This report presents an extension to multiple endpoints of the decision framework proposed by Lalonde. In this extension, decision policies are formulated in two levels. First, a Go or Stop decision is made for each domain, for example by individually comparing each of the relevant endpoints to certain thresholds. Performing multiple comparisons heightens the risk of an incorrect Go decision. This risk can be controlled effectively by using the Simes procedure (1986), which is a special case of the Benjamini-Hochberg (1995) method. Domain-level decisions are then combined into policies fulfilling a monotonicity property. This property enables the calculation of upper bounds for the probability of an incorrect decision, and lower bounds for the probability of a correct decision. These calculations are performed both for purely synthetic endpoints and for a case study involving endpoints related to heart failure. The resulting bounds are analogues to the statistical notions of Type I error and power, respectively. Heuristics are derived to help practitioners decide which endpoints to include, depending on the statistical power of these endpoints and on which combinations of true effects are of clinical interest. Overall, the framework proposed in this report can represent many of the policies used by practitioners when designing Phase II studies with multiple endpoints. The outcome of the simulations presented in this theses can guide the selection of endpoints in order to achieve the desired bounds on the probabilities of correct and incorrect decisions.