The Use of a Binary Composite Endpoint and Sample Size Requirement: Influence of Endpoints Overlap

Marsal, Josep-Ramon; Ferreira‐González, Ignacio; Bertran, Sandra; Ribera, Aída; Permanyer-Miralda, Gaietà; Garcı́a-Dorado, David; Gómez, Guadalupe

doi:10.1093/aje/kww105

Cited by 10 publications

(17 citation statements)

References 26 publications

(48 reference statements)

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“…We are working on an extension of our methods to account for different group correlations. Moreover, we are currently studying and implementing in CompARE other association measures for characterizing the strength of dependence between pairs of binary endpoints, such as the relative overlap, which, in practice, might be easier to anticipate.…”

Section: Discussionmentioning

confidence: 99%

A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components

Roig

Melis

2019

Statistics in Medicine

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Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown. We first show that the sample size for composite binary endpoints is strongly dependent on the correlation and, second, that slight deviations in the prior information on the marginal parameters may result in underpowered trials for achieving the study objectives at a pre-specified significance level. We propose a general strategy for calculating the required sample size when the correlation is not specified and accounting for uncertainty in the marginal parameter values. We present the web platform CompARE to characterize composite endpoints and to calculate the sample size just as we propose in this paper. We evaluate the performance of the proposal with a simulation study and illustrate it by means of a real case study using CompARE. KEYWORDS composite binary endpoints, correlated endpoints, sample sizeStatistics in Medicine. 2019;38:1935-1956 wileyonlinelibrary.com/journal/sim

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Section: Discussionmentioning

confidence: 99%

A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components

Roig

Melis

2019

Statistics in Medicine

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“…However, the time-to-event analysis can be approximated though working with the probability of event by a certain follow-up time as a binary outcome, so the sample should not be very different with both approximations. Although there are many publications addressing the statistical methods to analyze the data from clinical trials using CEs, the problem of the sample size computation and its relationship with the strength of association between components of a binary CE has not been addressed in depth [7,12,15,17,26,27]. Sozu et al .…”

Section: Discussionmentioning

confidence: 99%

“…RE and the k’-th AE) happening together. Note that with these six parameters ( π i = 1,1 , π i = 1, k ′ , π i = 1,1 k ′ , π i = 2,1, π i = 2, k ′ , π i = 2,1 k ′ ) the SSR of the RE and the CE can be determined[7,16] and thus it can become apparent whether it is worth the combination of the RE with the AE in a CE. Usually π i 1 , π ik , are known, but π i ,1 k ′ is sometimes unknown[17].…”

Section: Methodsmentioning

confidence: 99%

“…Then, this combination of the RE and the AE with the lowest SSR is considered as the new RE and the algorithm starts again seeking among the k-2 remaining outcomes. Bin-CE additionally estimates the rate of the CE (1) and the effect of the CE on the treatment group (the methodology to compute these parameters has been discussed previously [7,14,16]).…”

Section: Methodsmentioning

confidence: 99%

“…In this case, although it is anticipated that a certain number of patients may suffer both events these variables are “moderately correlated”, and the number of patients experiencing at least one of the components would be expected to be higher than the number of patients experiencing ACS or stroke if they had been employed as a single primary outcome [12]. In a previous work [12] we assessed the impact of the strength of the association between two components of a binary CE on the SSR. Specifically, the stronger the association between both components the lower the potential reduction of SSR.…”

Section: Introductionmentioning

confidence: 99%

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Bin-CE: A comprehensive web application to decide upon the best set of outcomes to be combined in a binary composite endpoint

et al. 2018

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The estimation of the Sample Size Requirement (SSR) when using a binary composite endpoint (i.e. two or more outcomes combined in a unique primary endpoint) is not trivial. Besides information about the rate of events for each outcome, information about the strength of association between the outcomes is crucial, since it can determine an increase or decrease of the SSR. Specifically, the greater the strength of association between outcomes the higher the SSR. We present Bin-CE, a free tool to assist clinicians for computing the SSR for binary composite endpoints. In a first step, the user enters a set of candidate outcomes, the assumed rate of events for each outcome and the assumed effect of therapy on each outcome. Since the strength of the association between outcomes is usually unknown, a semi-parametric approach linking the a priori clinical knowledge of the potential degree of association between outcomes with the exact values of these parameters was programmed with Bin-CE. Bin-CE works with a recursive algorithm to choose the best combination of outcomes that minimizes the SSR. In addition, Bin-CE computes the sample size using different algorithms and shows different figures plotting the magnitude of the sample size reduction, and the effect of different combinations of outcomes on the rate of the primary endpoint. Finally, Bin-CE is programmed to perform sensitivity analyses. This manuscript presents the mathematic bases and introduces the reader to the use of Bin-CE using a real example.

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Sample size estimation using a latent variable model for mixed outcome co‐primary, multiple primary and composite endpoints

McMenamin

Barrett

Berglind

et al. 2022

Statistics in Medicine

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Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co‐primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one‐dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four‐dimensional endpoint and find that the sample size required for the co‐primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.

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The Use of a Binary Composite Endpoint and Sample Size Requirement: Influence of Endpoints Overlap

Cited by 10 publications

References 26 publications

A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components

A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components

Bin-CE: A comprehensive web application to decide upon the best set of outcomes to be combined in a binary composite endpoint

Sample size estimation using a latent variable model for mixed outcome co‐primary, multiple primary and composite endpoints

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