Censored quantile regression models, which offer great flexibility in assessing covariate effects on event times, have attracted considerable research interest. In this study, we consider flexible estimation and inference procedures for competing risks quantile regression, which not only provides meaningful interpretations by using cumulative incidence quantiles but also extends the conventional accelerated failure time model by relaxing some of the stringent model assumptions, such as global linearity and unconditional independence. Current method for censored quantile regressions often involves the minimization of the
In prevalent cohort studies where subjects are recruited at a cross‐section, the time to an event may be subject to length‐biased sampling, with the observed data being either the forward recurrence time, or the backward recurrence time, or their sum. In the regression setting, assuming a semiparametric accelerated failure time model for the underlying event time, where the intercept parameter is absorbed into the nuisance parameter, it has been shown that the model remains invariant under these observed data setups and can be fitted using standard methodology for accelerated failure time model estimation, ignoring the length bias. However, the efficiency of these estimators is unclear, owing to the fact that the observed covariate distribution, which is also length biased, may contain information about the regression parameter in the accelerated life model. We demonstrate that if the true covariate distribution is completely unspecified, then the naive estimator based on the conditional likelihood given the covariates is fully efficient for the slope.
more » « less- NSF-PAR ID:
- 10367239
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Scandinavian Journal of Statistics
- Volume:
- 49
- Issue:
- 2
- ISSN:
- 0303-6898
- Format(s):
- Medium: X Size: p. 525-541
- Size(s):
- p. 525-541
- Sponsoring Org:
- National Science Foundation
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