Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns.
We consider a general model for anomaly detection in a longitudinal cohort mortality pattern based on logistic joinpoint regression with unknown joinpoints. We discuss backward and forward sequential procedures for selecting both the locations and the number of joinpoints. Estimation of the model parameters and the selection algorithms are illustrated with longitudinal data on cancer mortality in a cohort of chemical workers.
Department of Mathematics, University of Louisville, Louisville, KY 40292, USA.
Michal
M
Czajkowski
Ryan
R
Gill
Grzegorz
G
Rempala
D000465:Algorithms; D001699:Biometry; D002617:Chemical Industry; D015331:Cohort Studies; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D009026:Mortality / Q000639:trends*; D009369:Neoplasms / Q000401:mortality; D009784:Occupational Diseases / Q000401:mortality; D016273:Occupational Exposure / Q000009:adverse effects