Validating an approach to overcome the immeasurable time bias in cohort studies: a real-world example and Monte Carlo simulation study

Abstract

Abstract Background Immeasurable time bias arises from the lack of in-hospital medication information. It has been suggested that time-varying adjustment for hospitalization may minimize this potential bias. However, whereas we examined this issue in one case study, there remains a need to assess the validity of this approach in other settings. Methods Using a Monte Carlo simulation, we generated synthetic immeasurable time-varying hospitalization-related factors of duration, frequency and timing. Nine scenarios were created by combining three frequency scenarios and three duration scenarios, where the empirical cohort distribution of hospitalization was used to simulate the ‘timing’. We used Korea’s healthcare database and a case example of β-blocker use and mortality among patients with heart failure. We estimated the gold-standard hazard ratio (HR) with 95% CI using inpatient and outpatient drug data, and that of the pseudo-outpatient setting using outpatient data only. We assessed the validity of adjusting for time-varying hospitalization in nine different scenarios, using relative bias, confidence limit ratio (CLR) and mean squared error (MSE) compared with the empirical gold-standard estimate across bootstrap resamples. Results With the real-world gold standard (HR 0.73; 95% CI 0.67–0.80) as the reference estimate, adjusting for time-varying hospitalization (0.71; 0.63–0.80) effectively reduced the immeasurable time bias and had the following performance metrics across the nine scenarios: relative bias (range: –7.08% to 0.61%), CLR (1.28 to 1.36) and MSE (0.0005 to 0.0031). Conclusions The approach of adjusting for time-varying hospitalization consistently reduced the immeasurable time bias in Monte Carlo simulated data.