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. 2017 Apr 4;17:52. doi: 10.1186/s12874-017-0327-3

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© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 3 — Simulations: differential censoring by age. Empirical bias (top; with 95% confidence interval), standard deviation (middle) and relative mean-squared error (bottom) of the estimate subdistribution hazard ratio (SHR) from Fine–Gray models with four different censoring estimates: pooled (model 1), separated by age (model 2), separated by treatment (model 3) and separated by age and treatment (model 4). Simulated data had a 10% risk of censoring due to loss to follow-up in young patients, and a censoring hazard ratio of 2.7 for older patients. The true exposure effect was zero in the left-hand column and a SHR of 2.7 in the right-hand column