caa a22 4500 605476152 CHVBK 20210128100355.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10985-014-9317-5 doi (NATIONALLICENCE)springer-10.1007/s10985-014-9317-5 Z-estimation and stratified samples: application to survival models [Elektronische Daten] [Norman Breslow, Jie Hu, Jon Wellner] The infinite dimensional Z-estimation theorem offers a systematic approach to joint estimation of both Euclidean and non-Euclidean parameters in probability models for data. It is easily adapted for stratified sampling designs. This is important in applications to censored survival data because the inverse probability weights that modify the standard estimating equations often depend on the entire follow-up history. Since the weights are not predictable, they complicate the usual theory based on martingales. This paper considers joint estimation of regression coefficients and baseline hazard functions in the Cox proportional and Lin-Ying additive hazards models. Weighted likelihood equations are used for the former and weighted estimating equations for the latter. Regression coefficients and baseline hazards may be combined to estimate individual survival probabilities. Efficiency is improved by calibrating or estimating the weights using information available for all subjects. Although inefficient in comparison with likelihood inference for incomplete data, which is often difficult to implement, the approach provides consistent estimates of desired population parameters even under model misspecification. Springer Science+Business Media New York, 2015 Semiparametric models nationallicence Proportional hazards nationallicence Additive hazards nationallicence Calibration of sampling weights nationallicence Model misspecification nationallicence Survey sampling nationallicence Breslow Norman Department of Biostatistics, University of Washington, Seattle, WA, USA aut Hu Jie Department of Biostatistics, University of Washington, Seattle, WA, USA aut Wellner Jon Department of Statistics, University of Washington, Seattle, WA, USA aut Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/4(2015-10-01), 493-516 1380-7870 21:4<493 2015 21 10985 https://doi.org/10.1007/s10985-014-9317-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10985-014-9317-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Breslow Norman Department of Biostatistics, University of Washington, Seattle, WA, USA aut NATIONALLICENCE 700 1- Hu Jie Department of Biostatistics, University of Washington, Seattle, WA, USA aut NATIONALLICENCE 700 1- Wellner Jon Department of Statistics, University of Washington, Seattle, WA, USA aut NATIONALLICENCE 773 0- Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/4(2015-10-01), 493-516 1380-7870 21:4<493 2015 21 10985