Zachary Burningham1, Tao He2, Chia-Chen Teng2, Xi Zhou2, Jonathan Nebeker2, Brian C Sauer2. 1. Salt Lake City Veterans Affairs Medical Center (VAMC), Health Services Research and Development (HSRD), Informatics, Decision-Enhancement and Analytic Sciences (IDEAS) Center, 500 Foothill Drive, Salt Lake City, UT, 84148, USA. zachary.burningham@va.gov. 2. Salt Lake City Veterans Affairs Medical Center (VAMC), Health Services Research and Development (HSRD), Informatics, Decision-Enhancement and Analytic Sciences (IDEAS) Center, 500 Foothill Drive, Salt Lake City, UT, 84148, USA.
Abstract
INTRODUCTION: The case-crossover (CCO) design was originally intended to study exposures characterized as intermittent with acute effects. The performance of the CCO design is not well characterized under alternative exposure and outcome relationships. OBJECTIVE: The purpose of this study was to evaluate the ability of the CCO to identify simulated treatment effects under different drug exposures and outcomes relationships while varying the duration of the 1:1 matched risk and control windows. METHODS: The simulated data were obtained from the Observational Medical Dataset Simulator, version 2 (OSIM2). The area under the receiver operator characteristic curve (AUC) was calculated to compare CCO performance across outcome types, simulated relative risk (RR), and duration of risk and control windows. RESULTS: The AUC for acute outcomes was higher for shorter risk and control windows and improved with higher simulated RR. For example, the AUC for the simulated RR of 4 was 0.95 for a 30-day window length and 0.78 for a 360-day window length. The AUC for the accumulative outcomes increased with longer risk and control windows and stronger simulated RR. For example, the AUC for the simulated RR of 4 was 0.85 for a 360-day window length and 0.23 for a 30-day window length. Risk and control window lengths did not appear to sufficiently alter the AUC for insidious onset outcomes. CONCLUSIONS: The CCO performed best for acute-onset outcomes, but may be useful for exploring adverse outcomes with accumulative effects. Careful consideration must be given to the hypothesized drug exposure and outcome distribution because specification of risk and control window duration affects CCO performance.
INTRODUCTION: The case-crossover (CCO) design was originally intended to study exposures characterized as intermittent with acute effects. The performance of the CCO design is not well characterized under alternative exposure and outcome relationships. OBJECTIVE: The purpose of this study was to evaluate the ability of the CCO to identify simulated treatment effects under different drug exposures and outcomes relationships while varying the duration of the 1:1 matched risk and control windows. METHODS: The simulated data were obtained from the Observational Medical Dataset Simulator, version 2 (OSIM2). The area under the receiver operator characteristic curve (AUC) was calculated to compare CCO performance across outcome types, simulated relative risk (RR), and duration of risk and control windows. RESULTS: The AUC for acute outcomes was higher for shorter risk and control windows and improved with higher simulated RR. For example, the AUC for the simulated RR of 4 was 0.95 for a 30-day window length and 0.78 for a 360-day window length. The AUC for the accumulative outcomes increased with longer risk and control windows and stronger simulated RR. For example, the AUC for the simulated RR of 4 was 0.85 for a 360-day window length and 0.23 for a 30-day window length. Risk and control window lengths did not appear to sufficiently alter the AUC for insidious onset outcomes. CONCLUSIONS: The CCO performed best for acute-onset outcomes, but may be useful for exploring adverse outcomes with accumulative effects. Careful consideration must be given to the hypothesized drug exposure and outcome distribution because specification of risk and control window duration affects CCO performance.
Keywords:
Central Nervous System Dysfunction; Condition Pair; Discordant Pair; Relative Risk Estimate; Window Length
Authors: Giulia P Consiglio; Andrea M Burden; Malcolm Maclure; Lisa McCarthy; Suzanne M Cadarette Journal: Pharmacoepidemiol Drug Saf Date: 2013-09-13 Impact factor: 2.890