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Power and sample size calculation for paired recurrent events data based on robust nonparametric tests.
Su, Pei-Fang; Chung, Chia-Hua; Wang, Yu-Wen; Chi, Yunchan; Chang, Ying-Ju.
Afiliação
  • Su PF; Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan.
  • Chung CH; Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan.
  • Wang YW; Institute of Allied Health Science, National Cheng Kung University, Tainan, 70101, Taiwan.
  • Chi Y; Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan.
  • Chang YJ; Institute of Allied Health Science, National Cheng Kung University, Tainan, 70101, Taiwan.
Stat Med ; 36(11): 1823-1838, 2017 05 20.
Article em En | MEDLINE | ID: mdl-28183151
ABSTRACT
The purpose of this paper is to develop a formula for calculating the required sample size for paired recurrent events data. The developed formula is based on robust non-parametric tests for comparing the marginal mean function of events between paired samples. This calculation can accommodate the associations among a sequence of paired recurrent event times with a specification of correlated gamma frailty variables for a proportional intensity model. We evaluate the performance of the proposed method with comprehensive simulations including the impacts of paired correlations, homogeneous or nonhomogeneous processes, marginal hazard rates, censoring rate, accrual and follow-up times, as well as the sensitivity analysis for the assumption of the frailty distribution. The use of the formula is also demonstrated using a premature infant study from the neonatal intensive care unit of a tertiary center in southern Taiwan. Copyright © 2017 John Wiley & Sons, Ltd.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estatísticas não Paramétricas / Tamanho da Amostra Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estatísticas não Paramétricas / Tamanho da Amostra Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article