2006
DOI: 10.1002/sim.2516
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Conditional power calculations for clinical trials with historical controls

Abstract: A trial of a new therapy is to be compared to results from a previous trial of patients treated with a standard therapy. For a given sample size for the trial of the new therapy, we desire the power, against a specific alternative hypothesis, for the hypothesis test of the null hypothesis that the therapies are equivalent. Alternatively, the sample size required for the trial of the new therapy is needed for a target power. We explain why a popular method for doing these calculations is wrong, and discuss alte… Show more

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Cited by 23 publications
(27 citation statements)
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“…The sample size needed for the experimental group is obtained as n e = rT . Further, although the exponential assumption may seem restrictive for determining sample size, this approach is known to work well compared with a more general approach which adopts the proportional hazards assumption and uses a log-rank test (Korn and Freidlin, 2006). …”
Section: Review Of Standard Methodsmentioning
confidence: 99%
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“…The sample size needed for the experimental group is obtained as n e = rT . Further, although the exponential assumption may seem restrictive for determining sample size, this approach is known to work well compared with a more general approach which adopts the proportional hazards assumption and uses a log-rank test (Korn and Freidlin, 2006). …”
Section: Review Of Standard Methodsmentioning
confidence: 99%
“…It requires the (unrealistic) assumption that the observed HC proportion of successes equals the true HC proportion of successes. As a result, many researchers (Kepner and Wackerly, 2001; Lee and Tseng, 2001; Korn and Freidlin, 2006) have demonstrated that this method could not preserve the nominal power and type I error when taking uncertainty about the HC data into account. To facilitate discussion about this uncertainty, we introduce the concepts of conditional power and conditional type I error for historical control studies with survival outcomes.…”
Section: Introductionmentioning
confidence: 99%
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“…Rubinstein et al [11] give methodology for calculating the required sample size for randomized studies using the logrank statistic. Korn and Freidlin [12] show how this approach can be extended to one-armed studies compared against historical controls, if the patient data are available.…”
Section: Historically Controlled Studiesmentioning
confidence: 99%
“…Makuch and Simon (1980) developed a sample size formula for HCTs with binary outcomes (Makuch-Simon (M-S) approach). Korn and Freidlin (2006) compared the performance of various approaches to HCT sample size calculation. In assessing the power, however, only the sampling variability from the experimental treatment arm is taken into account, due to the consideration that the measurements from the historical control (HC) arm have been observed before the clinical trial.…”
Section: Introductionmentioning
confidence: 99%