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Statistics and Research Methods for Acute Care and General SurgeonsSurvival Analysis

Statistics and Research Methods for Acute Care and General Surgeons: Survival Analysis [Clinical studies play a key role in the continuous development of the treatment of diseases to improve the survival of patients. Thus, a solid knowledge regarding how to collect and analyze survival data is crucial for medical researchers involved in such studies. How can we understand the impact of a treatment in modifying the survival probability of our patients? How can we account for the sequence of events that occurred at different time points? How can we be sure that an eventual survival benefit is intrinsically connected to the treatment and not to a more benevolent disease, not so much aggressive? In this chapter, we will focus our attention on these topics through some clinical examples that may better explain how to manage time-to-event data.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Statistics and Research Methods for Acute Care and General SurgeonsSurvival Analysis

Part of the Hot Topics in Acute Care Surgery and Trauma Book Series
Editors: Ceresoli, Marco; Abu-Zidan, Fikri M.; Staudenmayer, Kristan L.; Catena, Fausto; Coccolini, Federico
Famularo, Simone; Bernasconi, Davide
Statistics and Research Methods for Acute Care and General Surgeons — Dec 14, 2022
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-13817-1
Pages
89 –108
DOI
10.1007/978-3-031-13818-8_8
Publisher site
See Chapter on Publisher Site

Abstract

[Clinical studies play a key role in the continuous development of the treatment of diseases to improve the survival of patients. Thus, a solid knowledge regarding how to collect and analyze survival data is crucial for medical researchers involved in such studies. How can we understand the impact of a treatment in modifying the survival probability of our patients? How can we account for the sequence of events that occurred at different time points? How can we be sure that an eventual survival benefit is intrinsically connected to the treatment and not to a more benevolent disease, not so much aggressive? In this chapter, we will focus our attention on these topics through some clinical examples that may better explain how to manage time-to-event data.]

Published: Dec 14, 2022

Keywords: Survival analysis; Kaplan–Meyer; Cox; Multivariate regression; Life table; Cancer; Treatments comparison; Hazard ratio; Risk of mortality; Surgical outcomes; Long-term outcomes; Overall survival; Disease free survival; End-points; Censoring

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