Title: Sample size re-estimation without un-blinding for time-to-event outcomes in oncology clinical trials

 

Authors: Lihong Huang1, Jianling Bai1, Hao Yu1, Feng Chen1,2

 

Institutions: 1Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; 2Ministry of Education Key Laboratory for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.

 

Abstract: Sample size re-estimation is essential in oncology studies. However, the use of blinded sample size reassessment for survival data has been rarely reported. Based on the density function of the exponential distribution, an expectation-maximization (EM) algorithm of the hazard ratio was derived, and several simulation studies were used to verify its applications. The method had obvious variation in the hazard ratio estimates and overestimation for the relatively small hazard ratios. Our studies showed that the stability of the EM estimation results directly correlated with the sample size, the convergence of the EM algorithm was impacted by the initial values, and a balanced design produced the best estimates. No reliable blinded sample size re-estimation inference can be made in our studies, but the results provide useful information to steer the practitioners in this field from repeating the same endeavor.

 

Keywords: oncology study, clinical trial, sample size re-estimation, expectation-maximization algorithm

 

Full Text: JBR-2016-0111.pdf

 

J biomed Res published on 15 March, 2017, doi:10.7555/JBR.31.20160111