Two-treatment comparison based on joint toxicity and efficacy ordered alternatives in cancer trials

Flexible designs for phase II comparative clinical trials involving two response variables

The aim of phase II clinical trials is to determine whether an experimental treatment is sufficiently promising and safe to justify further testing. The need for reduced sample size arises naturally in phase II clinical trials owing to both technical and ethical reasons, motivating a significant part of research in the field during recent years, while another significant part of the research effort is aimed at more complex therapeutic schemes that demand the consideration of multiple endpoints to make decisions. In this paper, our attention is restricted to phase II clinical trials in which two treatments are compared with respect to two dependent dichotomous responses proposing some flexible designs. These designs permit the researcher to terminate the clinical trial when high rates of favorable or unfavorable outcomes are observed early enough requiring in this way a small number of patients. From the mathematical point of view, the proposed designs are defined on bivariate sequences of multi-state trials, and the corresponding stopping rules are based on various distributions related to the waiting time until a certain number of events appear in these sequences. The exact distributions of interest, under a unified framework, are studied using the Markov chain embedding technique, which appears to be very useful in clinical trials for the sample size determination. Tables of expected sample size and power are presented. The numerical illustration showed a very good performance for these new designs.

Methods of joint evaluation of efficacy and toxicity in phase II clinical trials

Phase II clinical trials in oncology are usually conducted to evaluate the anti-tumor effect. Because phase I trials are small studies, the maximum tolerated dose of a new drug may not be precisely established and the recommended dose used may lead to excessive toxicity. We investigate the methods proposed by Conaway-Petroni and Bryant-Day allowing early termination of phase II clinical trials and based on joint evaluation of treatment efficacy and safety. Both study designs are computed to minimize the expected accrual under the null hypothesis. As two criteria are considered, the null hypothesis is an area. Each method defines two specific type I error risks. Bryant-Day demonstrate that response and toxicity may be considered as independent (Phi=1). We compare the properties of these two methods with exact calculation according to objective criteria and present one example from a study conducted in France. The two methods differ with regard to the definition of the risks and the assumption of independence. They are similar in terms of expected accruals when Phi=1. Deviations from the assumption of independence induce minor consequences on the type I error risks when the constraint on the type II error risk is less than 15%. Choosing Phi has a minimal impact on expected accrual. Finally, one type I error risk (alpha00) defined by Conaway-Petroni dramatically increases in the case of deviation from the assumption made on Phi. Due to its robustness in relation to a deviation from the independence assumption, we recommend the use of the Bryant-Day method in clinical practice.

A nonparametric comparison of the effectiveness of treatments: a multivariate toxicity-penalized approach

In cancer clinical trials, the amount of treatment dose actually received by a patient may be limited by severe toxicity or lack of efficacy. A nonparametric approach is proposed for comparing the effectiveness of treatments based on the bivariate relationship defined by the doses at which efficacy and toxicity are observed to occur. Simulation studies are used to contrast the performance of the new procedure with the usual method of comparing percentages of patients who have effective results. Data from a randomized clinical trial of patients with metastatic nonseminomatous germ cell tumors are used to illustrate the method.