BACKGROUND: Survival times are often used to compare treatments. Survival data are a special type of data, and therefore have to be analyzed with special methods. METHODS: We illustrate special techniques for analyzing survival times by applying them to a publication on the treatment of patients with brain tumors. The present article is based on textbooks of statistics, a selective review of the literature, and the authors' own experience. RESULTS: Survival times are analyzed with the Kaplan-Meier method, which yields two measures of interest: survival rates and the median survival time. The log-rank test is used to compare survival times across treatment groups. Cox regression is used in multivariable models. The hazard ratio, a descriptive measure for differences in survival times, is explained. CONCLUSION: If survival times are analyzed without the use of special techniques, or if the underlying assumptions are not taken into account, faulty interpretation may result. Readers of scientific publications should know these pitfalls and be able to judge for themselves whether the chosen analytical method is correct.
BACKGROUND: Survival times are often used to compare treatments. Survival data are a special type of data, and therefore have to be analyzed with special methods. METHODS: We illustrate special techniques for analyzing survival times by applying them to a publication on the treatment of patients with brain tumors. The present article is based on textbooks of statistics, a selective review of the literature, and the authors' own experience. RESULTS: Survival times are analyzed with the Kaplan-Meier method, which yields two measures of interest: survival rates and the median survival time. The log-rank test is used to compare survival times across treatment groups. Cox regression is used in multivariable models. The hazard ratio, a descriptive measure for differences in survival times, is explained. CONCLUSION: If survival times are analyzed without the use of special techniques, or if the underlying assumptions are not taken into account, faulty interpretation may result. Readers of scientific publications should know these pitfalls and be able to judge for themselves whether the chosen analytical method is correct.
Authors: Katja von Hoff; Bernward Hinkes; Nicolas U Gerber; Frank Deinlein; Uwe Mittler; Christian Urban; Martin Benesch; Monika Warmuth-Metz; Niels Soerensen; Isabella Zwiener; Heiko Goette; Paul G Schlegel; Torsten Pietsch; Rolf D Kortmann; Joachim Kuehl; Stefan Rutkowski Journal: Eur J Cancer Date: 2009-02-26 Impact factor: 9.162
Authors: Sophie Pietschmann; André O von Bueren; Guido Henke; Michael Josef Kerber; Rolf-Dieter Kortmann; Klaus Müller Journal: J Neurooncol Date: 2014-08-27 Impact factor: 4.130
Authors: Daniel Wiese; Antoinette M Stroup; Aniruddha Maiti; Gerald Harris; Shannon M Lynch; Slobodan Vucetic; Victor H Gutierrez-Velez; Kevin A Henry Journal: Int J Environ Res Public Health Date: 2021-04-29 Impact factor: 3.390