Martin Posch

Vienna Medical University, Austria

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Enrichment designs for the development of personalized medicine

If the response to treatment depends on genetic biomarkers, it is important to identify (sub-)populations where the treatment has a positive benefit risk balance. One approach to identify relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analysis are challenging because different types of risks are associated with inference on subgroups: On the one hand, ignoring a relevant subpopulation one could miss a treatment option due to a dilution of the treatment effect in the full population. Even, if the diluted treatment effect can be demonstrated in an Overall population, it is not ethical to treat patients that do not benefit from the treatmemt, if they can be identified in advance. On the other hand selecting a spurious sub-population is not without risk either: it might increase the risk to approve an inefficient treatment (inflating the type 1 error rate), or may wrongly lead to restricting an efficient treatment to a too narrow fraction of a potential benefiting population. The latter can not only lead to a reduced revenue from the drug, but is also unfavourable from a public health perspective. We investigate these risks for non-adaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. Quantifying the risks with utility functions the characteristics of such adaptive and non-adaptive designs are compared for a range of scenarios.