Group sequential trials

The purpose of interim analyses in group sequential trials is to determine if the clinical trial should be terminated at that point. The rationale for interim analyses of data that are accumulating over time in a clinical trial was established almost 40 years ago, and considerable attention has subsequently focused on the development of statistical approaches and decision-making processes that facilitate the implementation of data monitoring and interim analyses for the early termination of a clinical trial (Chow and Liu, 2004). 

A common concern for a group sequential trial utilizing repeated interim ANOVA analysis is an inflated chance of observing a spurious result leading to an inflated Type I (false positive) error rate. Table 31.1 (24) illustrates the impact on the Type I error rate (a) based on the number of interim analyses each controlled at a = 0.05. 

Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika 1977 64 191-99. 

Clinical trials, with almost no exception, are longitudinal (Chow and Liu, 2004). This means that data are accumulated sequentially over time. From the perspective outlined so far in the book, the statistical analysis takes place once the number of subjects stated in the study protocol have been enrolled, randomized, and completed their participation in the trial. This approach can be called the Fixed design or fixed sample design approach. Another design of interest in clinical trials is the group sequential design, in which interim analysis plays a crucial role. 

In the fixed sample clinical trial approach, one analysis is performed once all of the data have been collected. The chosen nominal significance level (the Type I error rate) will have been stated in the study protocol and/or the statistical analysis plan. This value is likely to be 0.05 As we have seen, declaring a finding statistically significant is typically done at the 5% p-level. In a group sequential clinical trial, the plan is to conduct at least one interim analysis and possibly several of them. This procedure will also be discussed in the trial s study protocol and/or the statistical analysis plan. For example, suppose the plan is to perform a maximum of five analyses (the fifth would have been the only analysis conducted had the trial adopted a fixed sample approach), and it is planned to enroll 1,000 subjects in the trial. The first interim analysis would be conducted after data had been collected for the first fifth of the total sample size, i.e., after 200 subjects. If this analysis provided compelling evidence to terminate the trial, it would be terminated at that point. If compelling evidence to terminate the trial was not obtained, the trial would proceed to the point where two-fifths of the total sample size had been recruited, at which point the second interim analysis would be conducted. All of the accumulated data collected to this point, i.e., the data from all 400 subjects, would be used in this analysis. 

By its nature, therefore, the group sequential design involves the possibility of multiple testing. In this example it is possible that five analyses could be conducted on data collected in this clinical trial. As discussed in Section 7.10, there is an inherent problem with multiple testing. As more tests are performed, it becomes increasingly likely that a Type I error will occur, i.e., that a result will erroneously be declared as statistically significant. As also noted at that point, however, the problem can be addressed completely satisfactorily by taking appropriate statistical care. 

O Brien and Fleming proposed what has become one of the most widely used group sequential boundaries. In this case, the boundary values are very extreme early in die trial, when the results are still quite unstable. The boundary values become less extreme as the trial progresses, with the critical value at the scheduled final analysis being close to the conventional critical value. This boundary has the desirable property of being very conservative early on when one would be wary of unstable efficacy and safety results. 

Gordon PH, Cheung YK, Mitsumoto H, Levin B (2005) Novel phase II design for clinical trials of ALS using selection paradigm and group sequential analysis. Amyotroph Lateral Scler 6 14—15. 

Pocock SJ. Interim analyses for randomized trials the group sequential approach. Biometrics 1982 38 153-62. 

Fleming TR, Harrington DP, O Brien PC. Designs for group sequential tests. Controlled Clinical Trials 1984 5 348-61. 

Jennison C, TumbuU BW (2000). Group Sequential Methods with Applications to Clinical Trials. Boca Raton, IL Chapman Hall/CRC. 

C. Jennison and B. W. Turnbull, Group sequential methods with applications to clinical trials. Chapman Hall/CRC, Boca Raton, FL, 2000. 

Citron ML, Berry DA, Cirrincione C, et al. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer First report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J CUn Oncol 2003 21 1431-1439. 

Naive estimators associated with sequential tests may also be biased in the sense that the expectation of standard fixed trial estimators taken over all possible trials run to the same stopping rule may not be equal to the true treatment effect. Adjustments for estimators and associated intervals are possible but raise similar issues to those discussed under section 19.2.1. For example, suppose that we have a fixed parallel-group trial and calculate, as a conventional treatment estimate, the difference between the mean outcomes in each group. Suppose we then notice, however, on looking at the results for the patients ordered over time, that had we run this trial as a given sequential trial with three looks at equal intervals, then we would have stopped exactly at the point dictated by the fixed trial, which happens to correspond to the second look of the sequential trial. Had we carried out the sequential trial we should have adjusted the treatment estimator, but since we have carried out a fixed trial we shall not. 

Freedman LS, Spiegelhalter DJ (1989) Comparison of Bayesian with group sequential-methods for monitoring cUnical trials. Controlled Clinical Trials 10 357-367. 

Group sequential method. A frequentist approach to running and analysing sequential trials whereby hypothesis tests are carried out at agreed intervals in such a way as to ensure that the probability, for the trial as a whole, of making a type I error, given that the null hypothesis is true, does not exceed some nominal level chosen... 

Citron, M. L., Berry, D. A., Cirrincione, C., Hudis, C., Winer, E. R, Gradishar, W. J., Davidson, N. E., Martino, S., Livingston, R., Ingle, J. N., Perez, E. A., Carpenter, J., Hurd, D., Holland, J. R, Smith, B. L., Sartor, C. I., Leung, E. H., Abrams, J., Schilsky, R. L., Muss, H. B. and Norton, L., Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer First report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741, /. Clin. Oncol, 21(8), 1431, 2003. 

Jennison, C. and Turnbull, B.W. 1993. Group sequential tests for bivariate response interim analyses of clinical trials with both efficacy and safety endpoints. Biometrics 49 741. 

Group sequential designs, particularly in noninferiority safety studies where many events may occur after accrual during the follow-up phase, are unlikely to stop early. The design described here, instead, uses accruing information to predict how many events the current cohort is likely to provide with addition follow-up. Therefore, rather than having many patients with short exposure times, it relies on a smaller number of patients with greater exposure times—with that optimal number chosen adaptively and based upon internal trial data. 

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