Analytical approaches to minimizing immeasurable time bias in cohort studies.

BACKGROUND

Immeasurable time bias exaggerates drug benefits in pharmacoepidemiological studies due to exposure misclassification arising from the inability to measure in-hospital medications in many health care databases.

METHODS

To compare the ability of different methodological approaches to minimize immeasurable time bias, we conducted a cohort study of β-blocker use and all-cause mortality among patients with heart failure (HF), using a nationwide health care database which contains both in- and outpatient prescriptions. In our gold-standard analysis, we assessed exposure using a time-varying approach involving both in- and outpatient prescriptions. Cox proportional hazard models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) of mortality, with exposure to β-blockers defined as a time-varying variable. To estimate the magnitude of the immeasurable time bias, we repeated the analyses using outpatient prescriptions only and compared 10 approaches to minimize the bias, which are categorized as restriction, adjustment, assumption and weighting.

RESULTS

The HR for β-blocker use versus non-use was 0.76 (95% CI: 0.71 to 0.80) in our gold-standard analysis. When exposure assessment was restricted to outpatient prescriptions only, β-blocker use was substantially more protective (HR 0.43, 95% CI: 0.40 to 0.46). Of the 10 approaches examined, adjusting for hospitalization as a time-varying variable successfully minimized the bias (HR 0.75, 95% CI: 0.68 to 0.82).

CONCLUSIONS

The immeasurable time bias can result in substantial bias in pharmacoepidemiological studies. Time-varying adjustment for hospitalization appears to reduce the immeasurable time bias in the absence of inpatient medication data.

Informations de copyright

© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.