Clinical Trials

❘❙❚■ Chapter 31 | Interim Monitoring and Stopping RulesFigure 1. The effect of repeated statistical tests (on the same data) on the Type I error rate. The results arebased on 200,000 simulations of two groups of binomial data with a 50% chance of success in both groupsand a final sample size of 100 per group. Equally spaced chi-squared tests were used. The graph showsthat the greater the number of interim analyses carried out, the greater the chance of Type I errors.50% –40% –Type I error level30% –20% –10% –★0% – |0|10|20|30|40|50Number of tests★ = two-test example as described in the text.What statistical methods can be used for interim monitoring?One issue that arises when monitoring a trial is choosing statistical analysismethods for assessing the safety, efficacy, and futility endpoints. The primaryproblem occurs when carrying out multiple hypothesis tests during the course ofa clinical trial. In the usual hypothesis-testing situation, the Type I error (or falsepositive)– the rejection of the null hypothesis when it is actually true – iscontrolled at some level of significance, typically 5%. The overall Type I errorlevel increases with the number of tests, as shown in Figure 1. For example, if the5% level is used on two tests – one at the midpoint of the trial and one at the endof the trial – the overall Type I error level is actually 9%. This means that in 9%of trials with no treatment effect, the null hypothesis that there is no effect wouldbe falsely rejected.356