Erinn M Hade1, David Jarjoura. 1. Center for Biostatistics, The Ohio State University, Columbus, OH, USA. hade.2@osu.edu
Abstract
BACKGROUND: During the recruitment phase of a randomized breast cancer trial, investigating the time to recurrence, we found a strong suggestion that the failure probabilities used at the design stage were too high. Since most of the methodological research involving sample size re-estimation has focused on normal or binary outcomes, we developed a method which preserves blinding to re-estimate sample size in our time to event trial. PURPOSE: A mistakenly high estimate of the failure rate at the design stage may reduce the power unacceptably for a clinically important hazard ratio. We describe an ongoing trial and an application of a sample size re-estimation method that combines current trial data with prior trial data or assumes a parametric model to re-estimate failure probabilities in a blinded fashion. METHODS: Using our current blinded trial data and additional information from prior studies, we re-estimate the failure probabilities to be used in sample size re-calculation. We employ bootstrap re-sampling to quantify uncertainty in the re-estimated sample sizes. RESULTS: At the time of re-estimation data from 278 patients were available, averaging 1.2 years of follow up. Using either method, we estimated a sample size increase of zero for the hazard ratio because the estimated failure probabilities at the time of re-estimation differed little from what was expected. We show that our method of blinded sample size re-estimation preserves the type I error rate. We show that when the initial guess of the failure probabilities are correct, the median increase in sample size is zero. LIMITATIONS: Either some prior knowledge of an appropriate survival distribution shape or prior data is needed for re-estimation. CONCLUSIONS: In trials when the accrual period is lengthy, blinded sample size re-estimation near the end of the planned accrual period should be considered. In our examples, when assumptions about failure probabilities and HRs are correct the methods usually do not increase sample size or otherwise increase it by very little. Clinical Trials 2010; 7: 219. http://ctj.sagepub.com.
RCT Entities:
BACKGROUND: During the recruitment phase of a randomized breast cancer trial, investigating the time to recurrence, we found a strong suggestion that the failure probabilities used at the design stage were too high. Since most of the methodological research involving sample size re-estimation has focused on normal or binary outcomes, we developed a method which preserves blinding to re-estimate sample size in our time to event trial. PURPOSE: A mistakenly high estimate of the failure rate at the design stage may reduce the power unacceptably for a clinically important hazard ratio. We describe an ongoing trial and an application of a sample size re-estimation method that combines current trial data with prior trial data or assumes a parametric model to re-estimate failure probabilities in a blinded fashion. METHODS: Using our current blinded trial data and additional information from prior studies, we re-estimate the failure probabilities to be used in sample size re-calculation. We employ bootstrap re-sampling to quantify uncertainty in the re-estimated sample sizes. RESULTS: At the time of re-estimation data from 278 patients were available, averaging 1.2 years of follow up. Using either method, we estimated a sample size increase of zero for the hazard ratio because the estimated failure probabilities at the time of re-estimation differed little from what was expected. We show that our method of blinded sample size re-estimation preserves the type I error rate. We show that when the initial guess of the failure probabilities are correct, the median increase in sample size is zero. LIMITATIONS: Either some prior knowledge of an appropriate survival distribution shape or prior data is needed for re-estimation. CONCLUSIONS: In trials when the accrual period is lengthy, blinded sample size re-estimation near the end of the planned accrual period should be considered. In our examples, when assumptions about failure probabilities and HRs are correct the methods usually do not increase sample size or otherwise increase it by very little. Clinical Trials 2010; 7: 219. http://ctj.sagepub.com.
Authors: M W Retsky; R Demicheli; D E Swartzendruber; P D Bame; R H Wardwell; G Bonadonna; J F Speer; P Valagussa Journal: Breast Cancer Res Treat Date: 1997-09 Impact factor: 4.872
Authors: Richard R Love; Nguyen Ba Duc; D Craig Allred; Nguyen Cong Binh; Nguyen Van Dinh; Nguyen Ngoc Kha; Tran Van Thuan; Syed K Mohsin; Le Dinh Roanh; Hoang Xuan Khang; Trinh Luong Tran; Tran Tu Quy; Nguyen Van Thuy; Pham Nhu Thé; Ton That Cau; Nguyen Dinh Tung; Dang Thanh Huong; Le Minh Quang; Nguyen Ngoc Hien; Le Thuong; Tian-Zhen Shen; Ye Xin; Qian Zhang; Thomas C Havighurst; Yonghong Fred Yang; Bruce E Hillner; David L DeMets Journal: J Clin Oncol Date: 2002-05-15 Impact factor: 44.544
Authors: Richard R Love; Adriano V Laudico; Nguyen Van Dinh; D Craig Allred; Gemma B Uy; Le Hong Quang; Jonathan Disraeli S Salvador; Stephen Sixto S Siguan; Maria Rica Mirasol-Lumague; Nguyen Dinh Tung; Noureddine Benjaafar; Narciso S Navarro; Tran Tu Quy; Arturo S De La Peña; Rodney B Dofitas; Orlino C Bisquera; Nguyen Dieu Linh; Ta Van To; Gregory S Young; Erinn M Hade; David Jarjoura Journal: J Natl Cancer Inst Date: 2015-03-19 Impact factor: 13.506