Abstract
Estimates of the relationship between an outcome and an exposure are biased by imperfect ascertainment of the outcome of interest. In studies using data derived from electronic health records (EHRs), misclassification of outcomes is common and is often related to patient characteristics. For instance, patients with greater comorbid disease burden may use the healthcare system more frequently making it more likely that the EHR will contain a record of their diagnosis, possibly resulting in poorer outcome classification for healthier patients who do not seek care as frequently. This is particularly problematic in studies of time-to-event outcomes in which both the occurrence of an event and the timing of the event, if it occurs, may be captured with error in the EHR. Misclassification-adjusted estimators in the context of time-to-event outcomes are available using discrete time proportional hazards models but may be biased if operating characteristics of the EHR-derived endpoint vary across exposure categories. Motivated by an algorithm for identifying second breast cancer events using EHR data, we investigated the implications of using an imperfectly assessed outcome with differential measurement error in time-to-event analyses. We used simulation studies to demonstrate the magnitude of bias induced by failure to account for error in the status and timing of recurrence and compared alternative methods for correcting this bias. We conclude with general guidance on accounting for outcome misclassification in time-to-event studies using EHR data.
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Acknowledgements
Research reported in this paper was supported by the National Cancer Institute of the National Institutes of Health under award number R01CA120562 and R21CA143242. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Hubbard, R.A., Harton, J., Zhu, W., Wang, L., Chubak, J. (2017). Accounting for Differential Error in Time-to-Event Analyses Using Imperfect Electronic Health Record-Derived Endpoints. In: Chen, DG., Jin, Z., Li, G., Li, Y., Liu, A., Zhao, Y. (eds) New Advances in Statistics and Data Science. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-69416-0_14
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