caa a22 4500 60547625X CHVBK 20210128100355.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s10985-014-9299-3 doi (NATIONALLICENCE)springer-10.1007/s10985-014-9299-3 Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression [Elektronische Daten] [Hans van Houwelingen, Hein Putter] By far the most popular model to obtain survival predictions for individual patients is the Cox model. The Cox model does not make any assumptions on the underlying hazard, but it relies heavily on the proportional hazards assumption. The most common ways to circumvent this robustness problem are 1) to categorize patients based on their prognostic risk score and to base predictions on Kaplan-Meier curves for the risk categories, or 2) to include interactions with the covariates and suitable functions of time. Robust estimators of the $$t_0$$ t 0 -year survival probabilities can also be obtained from a "stopped Cox” regression model, in which all observations are administratively censored at $$t_0$$ t 0 . Other recent approaches to solve this robustness problem, originally proposed in the context of competing risks, are pseudo-values and direct binomial regression, based on unbiased estimating equations. In this paper stopped Cox regression is compared with these direct approaches. This is done by means of a simulation study to assess the biases of the different approaches and an analysis of breast cancer data to get some feeling for the performance in practice. The tentative conclusion is that stopped Cox and direct models agree well if the follow-up is not too long. There are larger differences for long-term follow-up data. There stopped Cox might be more efficient, but less robust. Springer Science+Business Media New York, 2014 Direct binomial regression nationallicence Landmarking nationallicence Proportional hazards regression nationallicence Pseudo-observations nationallicence Stopped Cox regression nationallicence van Houwelingen Hans Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S-5-P, PO Box 9600, 2300 RC, Leiden, The Netherlands aut Putter Hein Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S-5-P, PO Box 9600, 2300 RC, Leiden, The Netherlands aut Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/2(2015-04-01), 180-196 1380-7870 21:2<180 2015 21 10985 https://doi.org/10.1007/s10985-014-9299-3 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/s10985-014-9299-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- van Houwelingen Hans Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S-5-P, PO Box 9600, 2300 RC, Leiden, The Netherlands aut NATIONALLICENCE 700 1- Putter Hein Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S-5-P, PO Box 9600, 2300 RC, Leiden, The Netherlands aut NATIONALLICENCE 773 0- Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/2(2015-04-01), 180-196 1380-7870 21:2<180 2015 21 10985