Figure 3. Logical flow of interim modelling analyses.

This approach uses available data from the baseline surveys in each trial cluster and information on process indicators of coverage and intensity available for each cluster within each trial arm gathered after the start of the trial. These data would not include observed HIV incidence. The interim modelling analysis may come to one of four conclusions. (i) The targeted effect size on HIV is likely to be achieved at the end of the study without having to modify the intervention targets/implementation strategy. (ii) The targeted effect size is unlikely to be achieved, and therefore the intervention targets/implementation strategy need to be revised. (iii) The targeted effect size is unlikely to be achieved, even if the intervention targets are improved to their realistic maximum, unless there is a change in the study design (such as an increase in sample size or study duration). (iv) There is little chance of being able to detect an impact at the end of the trial even if the study duration is increased. The number of interim analyses should be predetermined at the start of the trial and take into account trial characteristics, logistical considerations (such as the time and cost required to regularly update programmatic data during the trial and to perform the modelling analyses), and the statistical effect of the interim analysis and proposed changes on the overall type I error.