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Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes.
Tian, Zizhong; Esserman, Denise; Tong, Guangyu; Blaha, Ondrej; Dziura, James; Peduzzi, Peter; Li, Fan.
Afiliación
  • Tian Z; Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
  • Esserman D; Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
  • Tong G; Yale Center for Analytical Sciences, Yale University, New Haven, Connecticut, USA.
  • Blaha O; Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
  • Dziura J; Yale Center for Analytical Sciences, Yale University, New Haven, Connecticut, USA.
  • Peduzzi P; Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
  • Li F; Yale Center for Analytical Sciences, Yale University, New Haven, Connecticut, USA.
Stat Med ; 41(4): 645-664, 2022 02 20.
Article en En | MEDLINE | ID: mdl-34978097
Motivated by a suicide prevention trial with hierarchical treatment allocation (cluster-level and individual-level treatments), we address the sample size requirements for testing the treatment effects as well as their interaction. We assume a linear mixed model, within which two types of treatment effect estimands (controlled effect and marginal effect) are defined. For each null hypothesis corresponding to an estimand, we derive sample size formulas based on large-sample z-approximation, and provide finite-sample modifications based on a t-approximation. We relax the equal cluster size assumption and express the sample size formulas as functions of the mean and coefficient of variation of cluster sizes. We show that the sample size requirement for testing the controlled effect of the cluster-level treatment is more sensitive to cluster size variability than that for testing the controlled effect of the individual-level treatment; the same observation holds for testing the marginal effects. In addition, we show that the sample size for testing the interaction effect is proportional to that for testing the controlled or the marginal effect of the individual-level treatment. We conduct extensive simulations to validate the proposed sample size formulas, and find the empirical power agrees well with the predicted power for each test. Furthermore, the t-approximations often provide better control of type I error rate with a small number of clusters. Finally, we illustrate our sample size formulas to design the motivating suicide prevention factorial trial. The proposed methods are implemented in the R package H2x2Factorial.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proyectos de Investigación Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proyectos de Investigación Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido