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Abstract
The objective of this paper is to compare the performance of seven methods, namely new user cohort, case control, self-controlled case series, self-controlled cohort, disproportionality analysis, temporal pattern discovery, and longitudinal gamma poisson shrinker, as tools for risk identification in observational healthcare data. The experiment applied each method to 399 drug-outcome scenarios in 5 real observational databases. Method performance was evaluated through Area Under the receiver operator characteristics Curve (AUC), bias, mean square error, and confidence interval coverage probability. Multiple methods offer strong predictive accuracy, with AUC>0.70 achievable for all outcomes and databases with more than one analytical approach. Self-controlled methods, namely self-controlled case series, temporal pattern discovery, self-controlled cohort, had higher predictive accuracy than cohort and case-control methods across all databases and outcomes. In conclusion, the observational healthcare data can inform risk identification of medical product effects on acute liver injury, acute myocardial infarction, acute renal failure and gastrointestinal bleeding.