Dataset Information

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses.

ABSTRACT:

Background

Impact of immeasurable time bias (IMTB) is yet to be examined in self-controlled designs.

Methods

We conducted case-crossover, case-time-control, and case-case-time-control analyses using Korea's healthcare database. Two empirical examples among elderly patients were used: 1) benzodiazepines-hip fracture; 2) benzodiazepines-mortality. For cases, the date of hip fracture diagnosis or death was defined as the index date, and the inherited date of their matched cases for controls or future cases. Exposure was assessed in the 1-30 day (hazard) and 61-90 day (control) windows preceding the index date. A non-missing exposure setting included in- and outpatient prescriptions and the pseudo-outpatient setting included only the outpatients. Conditional logistic regression was done to estimate odds ratios (ORs) with 95% confidence intervals (CIs), where the relative difference in OR among the two settings was calculated to quantify the IMTB.

Results

The IMTB had negligible impacts in the hip fracture example in the case-crossover (non-missing exposure setting OR 1.27; 95% CI, 1.12-1.44; pseudo-outpatient setting OR 1.21; 95% CI, 1.06-1.39; magnitude 0.05), case-time-control (OR 1.18; 95% CI, 0.98-1.44; OR 1.13; 95% CI, 0.92-1.38; 0.04, respectively), and case-case-time-control analyses (OR 0.99; 95% CI, 0.80-1.23; OR 0.94; 95% CI, 0.75-1.18; 0.05, respectively). In the mortality example, IMTB had significant impacts in the case-crossover (non-missing exposure setting OR 1.44; 95% CI, 1.36-1.52; pseudo-outpatient setting OR 0.72; 95% CI, 0.67-0.78; magnitude 1.00), case-time-control (OR 1.38; 95% CI, 1.26-1.51; OR 0.68; 95% CI, 0.61-0.76; 1.03, respectively), and case-case-time-control analyses (OR 1.27; 95% CI, 1.15-1.40; OR 0.62; 95% CI, 0.55-0.69; 1.05, respectively).

Conclusion

Although IMTB had negligible impacts on the drug's effect on acute events, as these are unlikely to be accompanied with hospitalizations, it negatively biased the drug's effect on mortality, an outcome with prodromal phases, in the three self-controlled designs.

SUBMITTER: Jeong HE

PROVIDER: S-EPMC9794445 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses.

Jeong Han Eol HE Lee Hyesung H Oh In-Sun IS Filion Kristian B KB Shin Ju-Young JY

Journal of epidemiology 20211125 2

<h4>Background</h4>Impact of immeasurable time bias (IMTB) is yet to be examined in self-controlled designs.<h4>Methods</h4>We conducted case-crossover, case-time-control, and case-case-time-control analyses using Korea's healthcare database. Two empirical examples among elderly patients were used: 1) benzodiazepines-hip fracture; 2) benzodiazepines-mortality. For cases, the date of hip fracture diagnosis or death was defined as the index date, and the inherited date of their matched cases for c ...[more]

PMID: 34053964

Similar Datasets

Project description:BackgroundDespite the frequent use of self-controlled methods in pharmacoepidemiological studies, the factors that may bias the estimates from these methods have not been adequately compared in real-world settings. Here, we comparatively examined the impact of a time-varying confounder and its interactions with time-invariant confounders, time trends in exposures and events, restrictions, and misspecification of risk period durations on the estimators from three self-controlled methods. This study analyzed self-controlled case series (SCCS), case-crossover (CCO) design, and sequence symmetry analysis (SSA) using simulated and actual electronic medical records datasets.MethodsWe evaluated the performance of the three self-controlled methods in simulated cohorts for the following scenarios: 1) time-invariant confounding with interactions between the confounders, 2) time-invariant and time-varying confounding without interactions, 3) time-invariant and time-varying confounding with interactions among the confounders, 4) time trends in exposures and events, 5) restricted follow-up time based on event occurrence, and 6) patient restriction based on event history. The sensitivity of the estimators to misspecified risk period durations was also evaluated. As a case study, we applied these methods to evaluate the risk of macrolides on liver injury using electronic medical records.ResultsIn the simulation analysis, time-varying confounding produced bias in the SCCS and CCO design estimates, which aggravated in the presence of interactions between the time-invariant and time-varying confounders. The SCCS estimates were biased by time trends in both exposures and events. Erroneously short risk periods introduced bias to the CCO design estimate, whereas erroneously long risk periods introduced bias to the estimates of all three methods. Restricting the follow-up time led to severe bias in the SSA estimates. The SCCS estimates were sensitive to patient restriction. The case study showed that although macrolide use was significantly associated with increased liver injury occurrence in all methods, the value of the estimates varied.ConclusionsThe estimations of the three self-controlled methods depended on various underlying assumptions, and the violation of these assumptions may cause non-negligible bias in the resulting estimates. Pharmacoepidemiologists should select the appropriate self-controlled method based on how well the relevant key assumptions are satisfied with respect to the available data.

Dataset Information

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses.

Background

Methods

Results

Conclusion

Publications

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses.

Similar Datasets

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