TY - CHAP
M1 - Book, Section
TI - Regression Modeling of Time-to-Event Data: Survival Analysis
A1 - Glantz, Stanton A.
A1 - Slinker, Bryan K.
A1 - Neilands, Torsten B.
PY - 2017
T2 - Primer of Applied Regression and Analysis of Variance, 3e
AB - Logistic regression makes it possible to investigate the relationship between multiple independent variables and a dichotomous dependent variable. As illustrated by the examples in Chapter 12, such an analysis is often done to investigate the determinants of the presence of some disease or the effectiveness of some therapy, such as investigating whether bone cancer patients respond to chemotherapy. Many such questions are answered using clinical trials in which patients are recruited into a study, randomized to one treatment or another, and then followed for some time to observe the outcome. Because the chances of something happening (disease developing or therapy failing) generally increase as time passes, it is important that all subjects be observed for the same length of time and that the outcomes in all subjects are known when analyzing the results of such a study using logistic regression. Although there is nothing wrong in theory (and often in practice) with this approach, there are often situations in which it is not practical to follow all the experimental subjects for the same length of time. Likewise, in longitudinal epidemiological studies, people are followed forward in time following exposure to some potential toxin (e.g., secondhand smoke) to see if they subsequently develop a disease (breast cancer). In both cases, a thorough analysis needs to take into account the length of time that a subject has been in the study.
SN -
PB - McGraw-Hill Education
CY - New York, NY
Y2 - 2024/03/02
UR - accessbiomedicalscience.mhmedical.com/content.aspx?aid=1141902153
ER -