title: Sample size re-estimation in a breast cancer trial.
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.
  
  
  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.
authors.affiliation: Center for Biostatistics, The Ohio State University, Columbus, OH, USA. hade.2@osu.edu
authors.forename: Erinn M
authors.initials: EM
authors.lastname: Hade
authors.forename: David
authors.initials: D
authors.lastname: Jarjoura
authors.lastname: Lai Wei
mesh_terms: 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
references.citation: J Clin Oncol. 2008 Jan 10;26(2):253-7
references.pmid: 18086800
references.citation: Stat Med. 1998 Jan 15;17(1):89-100
references.pmid: 9463852
references.citation: Breast Cancer Res Treat. 1997 Sep;45(2):193-202
references.pmid: 9342444
references.citation: Biometrics. 1998 Jun;54(2):696-705
references.pmid: 9629649
references.citation: Stat Med. 1997 Sep 15;16(17):1913-23
references.pmid: 9304763
references.citation: Biometrics. 1994 Dec;50(4):1029-41
references.pmid: 7786985
references.citation: Stat Med. 1992 Jan 15;11(1):55-66
references.pmid: 1557576
references.citation: Biometrics. 2001 Jun;57(2):625-31
references.pmid: 11414593
references.citation: Stat Med. 2001 Sep 15-30;20(17-18):2625-43
references.pmid: 11523073
references.citation: J Clin Oncol. 2002 May 15;20(10):2559-66
references.pmid: 12011136
references.citation: Control Clin Trials. 1996 Dec;17(6):526-35
references.pmid: 8974211
references.citation: J Natl Cancer Inst. 2006 Jul 19;98(14):948-51
references.pmid: 16849671
references.citation: Biometrics. 1988 Mar;44(1):229-41
references.pmid: 3358991
references.citation: J Clin Oncol. 1996 Oct;14(10):2738-46
references.pmid: 8874335
references.citation: Stat Med. 1999 May 30;18(10):1199-214
references.pmid: 10363340
references.citation: Am Stat. 1995 Jan 1;49(1):43-47
references.pmid: 24039272