title: Assessment of the Gould-Shih procedure for sample size re-estimation.
abstract: The power of a clinical trial is partly dependent upon its sample size. With continuous data, the sample size needed to attain a desired power is a function of the within-group standard deviation. An estimate of this standard deviation can be obtained during the trial itself based upon interim data; the estimate is then used to re-estimate the sample size. Gould and Shih proposed a method, based on the EM algorithm, which they claim produces a maximum likelihood estimate of the within-group standard deviation while preserving the blind, and that the estimate is quite satisfactory. However, others have claimed that the method can produce non-unique and/or severe underestimates of the true within-group standard deviation. Here the method is thoroughly examined to resolve the conflicting claims and, via simulation, to assess its validity and the properties of its estimates. The results show that the apparent non-uniqueness of the method's estimate is due to an apparently innocuous alteration that Gould and Shih made to the EM algorithm. When this alteration is removed, the method is valid in that it produces the maximum likelihood estimate of the within-group standard deviation (and also of the within-group means). However, the estimate is negatively biased and has a large standard deviation. The simulations show that with a standardized difference of 1 or less, which is typical in most clinical trials, the standard deviation from the combined samples ignoring the groups is a better estimator, despite its obvious positive bias.
authors.affiliation: Department of Biostatistics and Data Management, Wyeth Consumer Healthcare, Madison, NJ 07940, USA. waksmaj@wyeth.com
authors.forename: Joel A
authors.initials: JA
authors.lastname: Waksman
mesh_terms: D000465:Algorithms; D015982:Bias; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015203:Reproducibility of Results; D012107:Research Design; D018401:Sample Size; D016896:Treatment Outcome