Increasing the sample size when the unblinded interim result is promising.
Increasing the sample size based on unblinded interim result may inflate the type I error rate and appropriate statistical adjustments may be needed to control the type I error rate at the nominal level. We briefly review the existing approaches which allow early stopping due to futility, or change the test statistic by using different weights, or adjust the critical value for final test, or enforce rules for sample size recalculation. The implication of early stopping due to futility and a simple modification to the weighted Z-statistic approach are discussed. In this paper, we show that increasing the sample size when the unblinded interim result is promising will not inflate the type I error rate and therefore no statistical adjustment is necessary. The unblinded interim result is considered promising if the conditional power is greater than 50 per cent or equivalently, the sample size increment needed to achieve a desired power does not exceed an upper bound. The actual sample size increment may be determined by important factors such as budget, size of the eligible patient population and competition in the market. The 50 per cent-conditional-power approach is extended to a group sequential trial with one interim analysis where a decision may be made at the interim analysis to stop the trial early due to a convincing treatment benefit, or to increase the sample size if the interim result is not as good as expected. The type I error rate will not be inflated if the sample size may be increased only when the conditional power is greater than 50 per cent. If there are two or more interim analyses in a group sequential trial, our simulation study shows that the type I error rate is also well controlled.
Chen
Y H Joshua
YH
Merck Research Laboratories, Blue Bell, PA 19422, USA. joshua_chen@merck.com
DeMets
David L
DL
Lan
K K Gordon
KK
D019380:Anti-HIV Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D015658:HIV Infections / Q000188:drug therapy; D015497:HIV-1 / Q000276:immunology; D006801:Humans; D012367:RNA, Viral / Q000097:blood; D012107:Research Design; D018401:Sample Size
D019380:Anti-HIV Agents; D012367:RNA, Viral