![]() Many investigators dislike the Pocock approach, however, because of its properties at the final stage of analysis. Of the three procedures described in the table, it provides the best chance of early trial termination. The Pocock approach uses the same significance level at each of the R interim analyses. Notice different approaches 'spend' or distribute the overall significance differently across the interim and final analyses. View the corresponding four rows in the middle of the table to determine critical values for each interim and the final analysis. In another situation with three interim analyses and a final analysis, R=4. On the other hand, had the choice been a Haybittle-Peto approach, the first test would be conducted with bound 3.0 and the final analysis at 1.96. If using O'Brien-Fleming approach, the interim analysis is conducted with bound 2.782 and final analysis with bound 1.967. Using these first two rows of the table, we find the critical values for the interim analysis and for the final analysis. Rįor example, if we plan one interim analysis and a final analysis, we will select the row in this table with R=2. The table is constructed under the assumption that n patients are accrued at each of the R statistical analyses so that the total sample size is \(N = nR\). These are illustrated in the following table for an overall significance level of \(\alpha = 0.05\) and for R = 2,3,4,5. ![]() There are primarily three schemes for selecting the boundary points which have been proposed. The boundary points are chosen such that the overall significance level does not exceed the desired \(\alpha\). At the \(r^\) interim analysis, the clinical trial is terminated with rejection of the null hypothesis if: Also, we let \(B_1, \dots, B_R\) denote the corresponding boundary points (critical values). We are adding to the dataset and analyzing the current set that has been collected. Suppose that the group sequential approach consists of R analyses, and we let \(Z_1, \dots, Z_R\) denote the test statistic at the R times of hypothesis testing. Again, let's focus more on the concepts than the statistical details. The group sequential analysis is defined as the situation in which only a few scheduled analyses are conducted. Usually this frequency of interim analyses detects treatment effects nearly as early as continuous monitoring. In fact, for most multi-center clinical trials, interim statistical analyses are conducted only once or twice per year. In most clinical trials, it is not necessary to perform a statistical analysis after each patient is accrued. Therefore, the frequentist approach to interim monitoring of clinical trials focuses on controlling the type I error rate. From a frequentist point of view, repeated hypothesis testing of accumulating data increases the type I error rate of a clinical trial.
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