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

Lookup NU author(s): Dr Jessica Barrett, Professor James WasonORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. 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.


Publication metadata

Author(s): McMenamin ME, Barrett JK, Berglind A, Wason JMS

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2022

Volume: 41

Issue: 13

Pages: 2303-2316

Print publication date: 15/06/2022

Online publication date: 23/02/2022

Acceptance date: 02/02/2022

Date deposited: 04/04/2022

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley and Sons Ltd

URL: https://doi.org/10.1002/sim.9356

DOI: 10.1002/sim.9356


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Funding

Funder referenceFunder name
BRC-1215-20014
MC_UU_00002/5

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