Quantifying the impact of time-varying baseline risk adjustment in the self-controlled risk interval design
Purpose The self-controlled risk interval design is commonly used to assess the association between an acute exposure and an adverse event of interest, implicitly adjusting for fixed, non-time-varying covariates. Explicit adjustment needs to be made for time-varying covariates, for example, age in young children. It can be performed via either a fixed or random adjustment. The random-adjustment approach can provide valid point and interval estimates but requires access to individual-level data for an unexposed baseline sample. The fixed-adjustment approach does not have this requirement and will provide a valid point estimate but may underestimate the variance. We conducted a comprehensive simulation study to evaluate their performance.
Methods We designed the simulation study using empirical data from the Food and Drug Administration-sponsored Mini-Sentinel Post-licensure Rapid Immunization Safety Monitoring Rotavirus Vaccines and Intussusception study in children 5-36.9 weeks of age. The time-varying confounder is age. We considered a variety of design parameters including sample size, relative risk, time-varying baseline risks, and risk interval length.
Results The random-adjustment approach has very good performance in almost all considered settings. The fixed-adjustment approach can be used as a good alternative when the number of events used to estimate the time-varying baseline risks is at least the number of events used to estimate the relative risk, which is almost always the case.
Conclusions We successfully identified settings in which the fixed-adjustment approach can be used as a good alternative and provided guidelines on the selection and implementation of appropriate analyses for the self-controlled risk interval design. Copyright (C) 2015 John Wiley & Sons, Ltd.
Li, L., Kulldorff, M., Russek-Cohen, E., Kawai, A. T., & Hua, W. (2015). Quantifying the impact of time-varying baseline risk adjustment in the self-controlled risk interval design. Pharmacoepidemiology and Drug Safety, 24(12), 1304-1312. https://doi.org/10.1002/pds.3885