Multiple imputation for early stopping of a complex clinical trial.
It is desirable to have procedures available for stopping a clinical trial early if there appears to be no treatment effect. Conditional power procedures allow for early stopping in favor of the null hypothesis if the probability of rejecting H0 at the planned end of the trial given the current data and a value of the parameter of interest is below some threshold level. Lan, Simon, and Halperin (1982, Communications in Statistics C1, 207-219) proposed a stochastic curtailment procedure that calculates the conditional power under the alternative hypothesis. Alternatively, predictive power procedures incorporate information from the observed data by averaging the conditional power over the posterior distribution of the parameter. For complex problems in which explicit evaluation of conditional power is not possible, we propose treating the problem of projecting the outcome of a trial given the current data as a missing data problem. We then complete the data using multiple imputation and thus eliminate the need for explicit calculation of conditional power. We apply this method to AIDS Clinical Trials Group (ACTG) protocol 118 and to several simulated clinical trials.
Betensky
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Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. betensky@hsph.harvard.edu
D019380:Anti-HIV Agents / Q000627:therapeutic use; D001699:Biometry; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D016049:Didanosine / Q000627:therapeutic use; D015658:HIV Infections / Q000188:drug therapy / Q000401:mortality; D006801:Humans; D016014:Linear Models; D016016:Proportional Hazards Models; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
D019380:Anti-HIV Agents; D016049:Didanosine