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Abstract

The gold standard for clinical studies are blinded randomized trials, but such a design is not always feasible due to ethical or practical reasons. Using an external historical control group out of an earlier conducted trial or registry might be an option. When using historical controls, one often faces the situation of non-comparable study populations. Matching procedures may help to build balanced samples for comparison. In this thesis an adaptive matched case-control trial design is established, which allows for a sample size recalculation at a planned interim analysis with the goal to enhance the matching rate at final analysis. The recalculation is based on the lower confidence interval limit of the matching rate observed at interim analysis. The newly developed resampling CI method estimates the 1:1 matching rate using a bootstrap like procedure (without replacement) and equal-sized groups for matching at interim. A naïve approach would be to use all patients for estimating the matching rate and directly reflect this value for recalculating the sample size. The new approach shows good performance in terms of power and type I error rate but needs more newly recruited patients than the naïve approach. Additionally, investigations for the time point of interim analysis are done. Simulations result in a number of 1/2 to 2/3 of the control patients, however, it seems that the time point is more depending on the actual number of patients used for matching than on the proportion. However, if the historical control group is large and for example only a small phase II trial is feasible the before described method might not be a good choice. Rather, each intervention patient may find more than one matching partner. Therefore, an iterative procedure to determining the number of matching partners is developed. The idea is an interim analysis, which includes an iterative increase in the number of matching partners and a parallel calculation of the matching rate. The number will be increased as long as the 1:M matching rate is higher than the 1:1 matching rate including a potential tolerance. The 1:M matching rate at interim analysis can then be used for recalculating the sample size. This procedure is easy to implement and can be combined with many study designs, such as two-stage designs. One has to note that the number of matching partners highly depends on the overlap of patient populations, meaning a small overlap leads to a low number of matching partners and vice versa. To conclude, by involving the trial-specific matching rate in the sample size recalculation one is able to enhance power in a matched case-control trial.

Not only in the generation of evidence unbalanced patient cohorts arise, but also in evidence synthesis this poses a problem. A common situation in evidence synthesis is an indirect comparison, where the comparison of interest, assume treatment A versus C, is not examined in a direct comparison. But there are trials comparing A with treatment B and another trial comparing C and B. using those trials to calculate a treatment effect for A versus C is called indirect comparison. It is likely that the independent trials AB and CB do not have the same underlying population. A special case, where individual patient data is available for one of the trials is assumed. Then a matching-like procedure can help to balance the cohorts, this method is called matching adjusted indirect comparison which is not sufficiently examined, yet. Another widely used method for indirect comparisons is the method of Bucher. A method comparison between those two methods is conducted for clinically relevant scenarios where assumptions of the methods are violated. Simulations lead to the conjecture that indirect comparisons are considerably underpowered. The method of Bucher and the matching adjusted indirect comparison show similar performance in scenarios without cross-trial differences. The matching approach leads to higher coverage and power when populations differ, effect modifiers are present, and regression models are not sufficiently adjusted. But matching confounders which do not modify the effect leads to increased bias. Until now, indirect comparisons are applied using one study per treatment comparison because the matching adjusted indirect comparison is designed for this setting. Nevertheless, it is likely that there are two or even more studies comparing the same treatments. When synthesizing evidence, one should always aim to include all appropriate evidence. Therefore, approaches to include multiple studies in indirect comparisons are introduced and compared. All include a step for combining treatment effects and one for calculating indirect treatment effects. The main difference between the approaches is the order of those two steps. An increasing number of studies can enhance power to desired regions above 80%, but it was not possible to identify one best performing method over all considered scenarios. In conclusion, when applying matching procedures in evidence synthesis the underlying situation needs to be checked carefully, and matching variables need to be chosen carefully because adjusting for confounders influences the precision of the indirect comparison.