Adapting by calibration the sample size of a phase III trial on the basis of phase II data.
The problem of estimating the sample size for a phase III trial on the basis of existing phase II data is considered, where data from phase II cannot be combined with those of the new phase III trial. Focus is on the test for comparing the means of two independent samples. A launching criterion is adopted in order to evaluate the relevance of phase II results: phase III is run if the effect size estimate is higher than a threshold of clinical importance. The variability in sample size estimation is taken into consideration. Then, the frequentist conservative strategies with a fixed amount of conservativeness and Bayesian strategies are compared. A new conservative strategy is introduced and is based on the calibration of the optimal amount of conservativeness - calibrated optimal strategy (COS). To evaluate the results we compute the Overall Power (OP) of the different strategies, as well as the mean and the MSE of sample size estimators. Bayesian strategies have poor characteristics since they show a very high mean and/or MSE of sample size estimators. COS clearly performs better than the other conservative strategies. Indeed, the OP of COS is, on average, the closest to the desired level; it is also the highest. COS sample size is also the closest to the ideal phase III sample size M(I) , showing averages and MSEs lower than those of the other strategies. Costs and experimental times are therefore considerably reduced and standardized. However, if the ideal sample size M(I) is to be estimated the phase II sample size n should be around the ideal phase III sample size, i.e. n ≥2M(I) /3.
Martini
Daniele De
DD
Dipartimento SEMEQ, Università del Piemonte Orientale, Novara, Italia. demartin@eco.unipmn.it
D001499:Bayes Theorem; D002138:Calibration; D017322:Clinical Trials, Phase II as Topic; D017326:Clinical Trials, Phase III as Topic; D003627:Data Interpretation, Statistical; D006801:Humans; D018401:Sample Size