Rediscovering drug side effects: the impact of analytical assumptions on the detection of associations in EHR data

Large clinical datasets can be used to discover and monitor drug side effects. Many previous studies analyzed symptom data as discrete events. However, some drug side effects are inferred from continuous variables such as weight or blood pressure. These require additional assumptions for analysis. For example, we can define positive/negative thresholds and time windows within which we expect to see the side effect. In this paper, we discuss the impact of such assumptions on the ability to detect known continuous drug side effects using statistical and visualization techniques. Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect. Categorization of the exposure variable improved side effect detection but negatively impacted model fit. To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.