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.
Famularo, S., Bernasconi, D. (2022). Survival Analysis. In M. Ceresoli, F.M. Abu-Zidan, K.L. Staudenmayer, F. Catena, F. Coccolini (a cura di), Statistics and Research Methods for Acute Care and General Surgeons (pp. 89-108). Springer Nature [10.1007/978-3-031-13818-8_8].
Survival Analysis
Bernasconi D.
2022
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
