An empirical assessment of immeasurable time bias in the setting of nested case-control studies: Statins and all-cause mortality among patients with heart failure.

Abstract

Immeasurable time bias exaggerates drug benefits in pharmacoepidemiologic studies due to exposure misclassification that occurs due to the lack of inpatient drug data in many healthcare databases. To estimate the magnitude of immeasurable time bias and assess potential approaches to minimize it, we conducted a nested case-control study of statin use and mortality among heart failure patients using the South Korean nationwide healthcare database, which contains both inpatient and outpatient medication data. Using both inpatient and outpatient medication data to define the gold standard exposure definition, we assessed 10 different analytical methods in which exposure was defined using outpatient medication data only. We compared different methodological approaches to reduce immeasurable time bias: restricting to nonhospitalized patients, adjusting for hospitalization, weighting by either measurable time (nonhospitalized time during 90-d period) or outpatient time, and computing the odds ratios (ORs) using 90-day cumulative probability of exposure produced by the Kaplan-Meier product-limit estimator for cases and controls. The three approaches that most closely approximated the gold standard (hazard ratio [HR] 1.20; 95% confidence interval [CI], 1.05-1.37) were weighting by either measurable (HR 1.09; 95% CI, 0.92-1.28) or outpatient time (HR 1.14; 95% CI, 0.96-1.34) in the unexposed or by estimating the 90-day exposure probability (HR 1.31; 95% CI, 1.11-1.51). The use of one of these three methods may be suggested as an approach to minimize immeasurable time bias in nested case-control studies.