Level of evidence for promising subgroup findings in an overall non-significant trial.

In drug development and drug licensing, it sometimes occurs that a new drug does not demonstrate effectiveness for the full study population, but there appears to be benefit in a relevant, pre-defined subgroup. This raises the question, how strong the evidence from such a subgroup is, and which confirmatory testing strategies are the most appropriate ones. Hence, we considered the type I error and the power of a subgroup result in a trial with non-significant overall results and of suitable replication strategies. In the case of a single trial, the inflation of the overall type I error is substantial and can be up to twice as large, especially in relatively small subgroups. This also increases to the risk of starting a replication trial that should not be done, if such a second trial is not already available. The overall type I error is almost controlled by using an appropriate replication strategy. This confirms the required cautious interpretation of promising subgroups, even in the case that overall trial results were perceived to be close to significance.

Sequential tests for gene-environment interactions in matched case-control studies.

The sample size necessary to detect a significant gene x environment interaction in an observational study can be large. For reasons of cost-effectiveness and efficient use of available biological samples we investigated the properties of sequential designs in matched case-control studies to test for both non-hierarchical and hierarchical interactions. We derived the test statistics Z and V and their characteristics when applied in a two-sided triangular test. Results of simulations show good agreement with theoretical values for V and the type I error. Power values were larger than their theoretical values for very large sample sizes. Median gain in efficiency was about 27 per cent. For a 'rare' phenotype gain in efficiency was larger when the alternative hypothesis was true than under the null hypothesis. Sequential designs lead to substantial efficiency gains in tests for interaction in matched case-control studies.