The Extent and Magnitude of Bias in Case-Crossover Studies of Real-World Non-transient Medications Patterns: A Simulation Study with Real-World Examples.

introductionA case-crossover study is a self-controlled design most appropriate for evaluating transient medication exposures. However, it has increasingly been used in studies of chronic medications and can cause bias in effect estimates that vary based on the pattern of medication use. The goal of this study was to evaluate the magnitude of this bias across different medication-use patterns.

objectiveTo quantify the magnitude of the bias introduced by different medication patterns and evaluate different case-crossover approaches to mitigate the bias.

methodsWe conducted a simulation study evaluating the bias introduced by (1) seven common medication patterns separately, and (2) cohort with 15 different patterns combined. We evaluated each scenario under risk ratios of 0.50, 0.75, 1.00, 1.50, and 2.00. Each approach was analyzed using conditional logistic regression comparing the probability of exposure on the outcome day to 30 days prior. A case-time-control design was used in each of the scenarios. Sensitivity analysis was performed to evaluate the impact on the estimates when changing the length of the risk and control windows. We conducted a real-world example focusing on sodium-glucose co-transporter-2 inhibitor users as real-world examples.

resultsThe case-crossover design resulted in unbiased estimates when patterns were consistent with transient exposures but were biased upward with prolonged exposure patterns. The magnitude of the bias varies by patterns or pattern combinations. When evaluating prolonged exposures individually or combined as a cohort with mixture patterns, case-time-control with extended risk and control window (30 days) produced unbiased results (mean bias ≤ 0.03).

conclusionResearchers who use the case-crossover design to evaluate non-transient exposures should implement recommended methods to account for biases.