Details
- Autor(en) / Beteiligte
- Titel
- The accelerated gap times model
- Ist Teil von
- Biometrika, 2005-09, Vol.92 (3), p.647-666
- Ort / Verlag
- Oxford: Oxford University Press
- Erscheinungsjahr
- 2005
- Link zum Volltext
- Quelle
- Alma/SFX Local Collection
- Beschreibungen/Notizen
- This paper develops a new semiparametric model for the effect of covariates on the conditional intensity of a recurrent event counting process. The model is a transparent extension of the accelerated failure time model for univariate survival data. Estimation of the regression parameter is motivated by semiparametric efficiency considerations, extending the class of weighted log-rank estimating functions originally proposed in Prentice (1978) and subsequently studied in detail by Tsiatis (1990) and Ritov (1990). A novel rank-based one-step estimator for the regression parameter is proposed. An Aalen-type estimator for the baseline intensity function is obtained. Asymptotics are handled with empirical process methods, and finite sample properties are studied via simulation. Finally, the new model is applied to the bladder tumour data of Byar (1980).
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0006-3444eISSN: 1464-3510DOI: 10.1093/biomet/92.3.647Titel-ID: cdi_proquest_journals_201695726
- Format
- –
- Schlagworte
- Applications, Biology, psychology, social sciences, Censoring, Censorship, Consistent estimators, Counting process, Cox regression, Estimation methods, Estimators, Exact sciences and technology, Inference, Linear inference, regression, Log-rank statistic, Martingale, Mathematics, Multiple events, Neoplasia, Nonparametric inference, Placebos, Probability and statistics, Random variables, Rank regression, Recurrent events, Regression analysis, Reliability, life testing, quality control, Renewals, Sciences and techniques of general use, Standard error, Statistics, Survival data, Weighting functions