• Tanujit Dey, Anish Mukherjee, and Sounak Chakraborty.
• Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH; Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. Electronic address: tdey@bwh.harvard.edu.
• Chest. 2020 Jul 1; 158 (1S): S39-S48.

AbstractSurvival (time-to-event) analysis is commonly used in clinical research. Key features of performing a survival analysis include checking proportional hazards assumptions, reporting CIs for hazards ratios and relative risks, graphically displaying the findings, and analyzing with consideration of competing risks. This article provides a brief overview of important statistical considerations for survival analysis. Censoring schemes, different methods of survival function estimation, and ways to compare survival curves are described. We also explain competing risk and how to model survival data in the presence of it. Different kinds of bias that influence survival estimation and avenues to model the data under these circumstances are also described. Several analysis techniques are accompanied by graphical representations illustrating proper reporting strategies. We provide a list of guiding statements for researchers and reviewers.Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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