Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system

Observational healthcare data offer the potential to enable identification of risks of medical products, but appropriate methodology has not yet been defined. The self-controlled cohort method, which compares the post-exposure outcome rate with the pre-exposure rate among an exposed cohort, has been proposed as a potential approach for risk identification but its performance has not been fully assessed.

To evaluate the performance of the self-controlled cohort method as a tool for risk identification in observational healthcare data.

The method was applied to 399 drug-outcome scenarios (165 positive controls and 234 negative controls across 4 health outcomes of interest) in 5 real observational databases (4 administrative claims and 1 electronic health record) and in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively.

Method performance was evaluated through area under ROC curve (AUC), bias, and coverage probability.

The self-controlled cohort design achieved strong predictive accuracy across the outcomes and databases under study, with the top-performing settings exceeding AUC >0.76 in all scenarios. However, the estimates generated were observed to be highly biased with low coverage probability.

If the objective for a risk identification system is one of discrimination, the self-controlled cohort method shows promise as a potential tool for risk identification. However, if a system is intended to generate effect estimates to quantify the magnitude of potential risks, the self-controlled cohort method may not be suitable, and requires substantial calibration to be properly interpreted under nominal properties.