Sample size re-estimation in a breast cancer trial.
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.


OBJECTIVE
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.


CONCLUSIONS
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.
Hade
Erinn M
EM
Center for Biostatistics, The Ohio State University, Columbus, OH, USA. hade.2@osu.edu
Jarjoura
David
D
Lai Wei
D000465:Algorithms; D001943:Breast Neoplasms / Q000628:therapy*; D023381:Endpoint Determination; D005260:Female; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018401:Sample Size
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