Nonparametric estimation with left truncated semi-competing risks data
نویسندگان
چکیده
SUMMARY Cause-specific hazard and cumulative incidence function are of practical importance in competing risks studies. Inferential procedures for these quantities are well developed and can be applied to semi-competing risks data, where a terminating event censors a non-terminating event, after coercing the data into the competing risks format. Complications arise when there is left truncation of the terminating event, as often occurs in observational studies. The competing risks analysis naively truncates the non-terminating event using the left trunca-tion time for the terminating event, which may lead to large efficiency losses. We propose simple nonparametric estimators which use all semi-competing risks information and do not require aritificial truncation. The uniform consistency and weak convergence of the estima-tors are established and variance estimators are provided. Simulation studies and an analysis of a diabetes registry demonstrate large efficiency gains over the naive estimators.
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