Objectives: Studies of cancer recurrences and second primary tumors require information on outcome dates. Little is known about how well electronic health record-based algorithms can identify dates or how errors in dates can bias analyses.
Research Design: We assessed rule-based and model-fitting approaches to assign event dates using a previously published electronic health record-based algorithm for second breast cancer events (SBCE). We conducted a simulation study to assess bias due to date assignment errors in time-to-event analyses.
Subjects: From a cohort of 3152 early-stage breast cancer patients, 358 women accurately identified as having had an SBCE served as the basis for this analysis.
Measures: Percent of predicted SBCE dates identified within +/-60 days of the true date was the primary measure of accuracy. In the simulation study, bias in hazard ratios (HRs) was estimated by averaging the difference between HRs based on algorithm-assigned dates and the true HR across 1000 simulations each with simulated N=4000.
Results: The most accurate date algorithm had a median difference between the true and predicted dates of 0 days with 82% of predicted dates falling within 60 days of the true date. Bias resulted when algorithm sensitivity and specificity varied by exposure status, but was minimal when date assignment errors were of the magnitude observed for our date assignment method.
Conclusions: SBCE date can be relatively accurately assigned based on a previous algorithm. While acceptable in many scenarios, algorithm-assigned dates are not appropriate to use when operating characteristics are likely to vary by the study exposure.
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