Speaker
Description
In clinical development it is essential to identify subgroups of patients who exhibit a beneficial treatment effect, ideally before moving to confirmatory trials. Such subgroups are often defined by predictive biomarkers with corresponding cut-off values. However, data-driven selection of biomarkers or cut-offs introduces selection bias, i.e. the treatment effect within the selected subgroup is overestimated.
In previous work, the approximate Bayesian computation (ABC) algorithm was used to correct for this selection bias, but it was limited to situations with a reduced number of potential subgroups. Machine learning (ML) methods explore a much wider range of subgroups, but this also increases the risk of bias and thus the challenge for effective bias correction. In this work we investigate how ML-based subgroup selection, specifically model-based partitioning (MOB), can be combined with the ABC algorithm to correct for selection bias. We first set up the methods by adapting MOB for subgroup selection and extending the ABC algorithm to time-to-event settings. Then, we evaluate our approach in terms of bias, overlap with the true subgroup, rate of correct biomarker inclusion and similarity in subgroup size in simulation studies based on the ADEMP framework.
Results from the simulation study indicate that the ABC approach effectively reduces the bias of treatment effect estimates in subgroups identified by MOB. The root mean squared error (RMSE) of the naïve estimates can be decreased from 0.171 to 0.112 in scenarios with large subgroup effects. Nevertheless, the approach also has some limitations: the ABC algorithm is computationally intensive, the performance highly depends on the choice of prior distributions and is less effective when the true subgroup effect is weak. In summary, our findings highlight the importance of addressing selection bias in ML-based subgroup selection and demonstrate how the ABC framework can provide a reasonable correction strategy.
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