Clinical trials with mechanism evaluation of intervention(s): mind the power and sample size calculation.

Autor: Lee KM; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, UK. kim.lee@kcl.ac.uk., Hellier J; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, UK., Emsley R; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, UK.
Jazyk: angličtina
Zdroj: Trials [Trials] 2024 Aug 06; Vol. 25 (1), pp. 527. Date of Electronic Publication: 2024 Aug 06.
DOI: 10.1186/s13063-024-08358-5
Abstrakt: Background: Mediation analysis, often completed as secondary analysis to estimating the main treatment effect, investigates situations where an exposure may affect an outcome both directly and indirectly through intervening mediator variables. Although there has been much research on power in mediation analyses, most of this has focused on the power to detect indirect effects. Little consideration has been given to the extent to which the strength of the mediation pathways, i.e., the intervention-mediator path and the mediator-outcome path respectively, may affect the power to detect the total effect, which would correspond to the intention-to-treat effect in a randomized trial.
Methods: We conduct a simulation study to evaluate the relation between the mediation pathways and the power of testing the total treatment effect, i.e., the intention-to-treat effect. Consider a sample size that is computed based on the usual formula for testing the total effect in a two-arm trial. We generate data for a continuous mediator and a normal outcome using the conventional mediation models. We estimate the total effect using simple linear regression and evaluate the power of a two-sided test. We explore multiple data generating scenarios by varying the magnitude of the mediation paths whilst keeping the total effect constant.
Results: Simulations show the estimated total effect is unbiased across the considered scenarios as expected, but the mean of its standard error increases with the magnitude of the mediator-outcome path and the variability in the residual error of the mediator, respectively. Consequently, this affects the power of testing the total effect, which is always lower than planned when the mediator-outcome path is non-trivial and a naive sample size was employed. Analytical explanation confirms that the intervention-mediator path does not affect the power of testing the total effect but the mediator-outcome path. The usual effect size consideration can be adjusted to account for the magnitude of the mediator-outcome path and its residual error.
Conclusions: The sample size calculation for studies with efficacy and mechanism evaluation should account for the mediator-outcome association or risk the power to detect the total effect/intention-to-treat effect being lower than planned.
(© 2024. The Author(s).)
Databáze: MEDLINE
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