The choice of a primary endpoint is an important issue when designing a clinical trial. It is common to use composite endpoints as a primary endpoint because it increases the number of observed events, captures more information and is expected to increase the power. However, combining events that have no similar clinical importance and have different treatment effects makes the interpretation of the results cumbersome and might reduce the power of the corresponding tests. Gómez and Lagakos proposed the ARE (asymptotic relative efficiency) method to choose between a composite or one of its components as primary endpoint comparing the efficacy of a treatment based on the times to each of these endpoints. The aim of this paper is to expand the ARE method to binary endpoints. We show that the ARE method depends on six parameters including the degree of association between components, event proportion, and effect of therapy given by the corresponding odds ratio of the single endpoints. A case study is presented to illustrate the methodology. We conclude with efficient guidelines for discerning which could be the best suited primary endpoint given anticipated parameters.
This is the peer reviewed version of the following article: Bofill, M., Gomez, G. Selection of composite binary endpoints in clinical trials. "Biometrical journal", 12 Octubre 2017, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/bimj.201600229/pdf. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Composite binary endpoints (CBE), defined as the union of several binary endpoints, are frequently used as the primary endpoint in a clinical trial. The specification of the treatment effect on the composite endpoint requires the information of their components and the degree of association between them. We summarize the treatment effect on the composite endpoint by means of the odds ratio and show that is determined by six parameters including the degree of association between components, the event proportion and the corresponding odds ratio of the individual endpoints. The purpose of this talk is to explore sample size formulations for CBE in terms of the marginal odds ratios, the event proportions and the degrees of association. We discuss the influence of each of the parameters of the composite in the required sample size. While anticipated values for the marginal parameters are often easier to guess and most of the sample size formulations depend on those, anticipating the degree of association between the components is a much harder task. Within the framework of multiple co-primary binary endpoints, several authors have addressed the influence of association on sample size . However, approximations to the needed sample size of a CBE with partial or null knowledge of the correlation are limited. We aim to develop alternative formulas for the computation of the sample size for CBE.
Composite binary endpoints are widely chosen as primary endpoint in clinical trials. The use of composite endpoints entails diculties in the interpretation of the results since the composite eect might not re ect the eect of its components. We propose a methodology to quantify the gain in eciency of using the composite binary endpoint instead of its most relevant component as primary endpoint to lead the trial. The method, based on the Asymptotic Relative Eciency (ARE), depends on six parameters including the degree of association between components, the event proportion and the eect of therapy given by the corresponding odds ratio of the single endpoints. We apply the ARE method to several scenarios dened by dierent values of the anticipated parameters and conclude with recommendations for discerning which could be the best suited primary endpoint given anticipated parameters.
One of the key issues in clinical trial design is to calculate the suitable sample size to detect a given treatment effect in the main response or primary endpoint, for given significance level and power. In studies where the primary endpoint is assessed by binary endpoints, as success versus failure, the standard procedure of sample size calculation is based on the normal approximation to the binomial, often taking into account finite sample size correction. Several formulas can be used depending on whether the odds ratio, the probability risk or the probability difference is defined for the effect. We restrict this presentation to the odds ratio formulation. Composite binary endpoints (CBE), defined as the union of two individual binary endpoints, are frequently used as the primary endpoint in a clinical trial. The derivation of the sample size for a CBE requires the specification of its odds ratio which is uniquely determined by the odds ratio and event rates of its marginal components and the degree of association between them in each treatment group. While the marginal parameters can be presumably anticipated, the association’s degree between marginal endpoints is usually unknown. The goal of this presentation is two–fold. First, we discuss the conditions under which the normality assumption is considered reliable given the parameter constellation of the CBE and which is the role of the level of association on these conditions. Second, aiming to provide a practical formulation in terms of the marginal parameters, we study different formulations for the sample size for a CBE taking into account the unknown association between components.
Randomized clinical trials provide compelling evidence that a study treatment causes an effect on human health. A primary endpoint ought to be chosen to confirm the effectiveness of the treatment and is the basis for computing the number of subjects in a Randomized clinical trial. Often a Composite Endpoint based on a combination of individual endpoints is chosen as a Primary Endpoint. As a tool for a more informed decision between using the Composite Endpoint as Primary Endpoint or one of its components, the ARE method is proposed. This method uses the Asymptotic Relative Efficiency (ARE) between the two possible logrank tests to compare the effect of the treatment. CompARE, a web-based interface tool is presented. CompARE computes the asymptotic relative efficiency in terms of interpretable parameters such as the anticipated probabilities of observing the primary and secondary endpoints and the relative treatment effects on every endpoint given by the corresponding hazard ratios. The ARE method is extended to observational studies as well as to Binary Composite Endpoints. A discussion on how to use the ARE method for the derivation of the sample size when the proportional hazards assumption does not hold, will conclude.