title: Sample size re-assessment leading to a raised sample size does not inflate type I error rate under mild conditions.
abstract: BACKGROUND
  One major concern with adaptive designs, such as the sample size adjustable designs, has been the fear of inflating the type I error rate. In (Stat Med 23:1023-1038, 2004) it is however proven that when observations follow a normal distribution and the interim result show promise, meaning that the conditional power exceeds 50%, type I error rate is protected. This bound and the distributional assumptions may seem to impose undesirable restrictions on the use of these designs. In (Stat Med 30:3267-3284, 2011) the possibility of going below 50% is explored and a region that permits an increased sample size without inflation is defined in terms of the conditional power at the interim.
  
  
  METHODS
  A criterion which is implicit in (Stat Med 30:3267-3284, 2011) is derived by elementary methods and expressed in terms of the test statistic at the interim to simplify practical use. Mathematical and computational details concerning this criterion are exhibited.
  
  
  RESULTS
  Under very general conditions the type I error rate is preserved under sample size adjustable schemes that permit a raise. The main result states that for normally distributed observations raising the sample size when the result looks promising, where the definition of promising depends on the amount of knowledge gathered so far, guarantees the protection of the type I error rate. Also, in the many situations where the test statistic approximately follows a normal law, the deviation from the main result remains negligible. This article provides details regarding the Weibull and binomial distributions and indicates how one may approach these distributions within the current setting.
  
  
  CONCLUSIONS
  There is thus reason to consider such designs more often, since they offer a means of adjusting an important design feature at little or no cost in terms of error rate.
authors.affiliation: Department of Oncology and Cancer Epidemiology, Clinical Sciences, Lund University and Skåne University Hospital, 221 85 Lund, Sweden. Per.Broberg@med.lu.se
authors.forename: Per
authors.initials: P
authors.lastname: Broberg
mesh_terms: D015982:Bias; D002986:Clinical Trials as Topic / Q000592:standards*; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size