TY - JOUR
T1 - Discrete-Time Event History Analysis Using Segmented Hazards
AU - Gardner, William
AU - Meyer, Marion
AU - Ketterlinus, Robert
N1 - Funding Information:
This research was partially supported by a contract from the National Institute of Child Health and Human Development. Thanks to Jack McArdle, Tim Tolson, and Karleen Preator for editorial help, technical advice, and patience. Thanks also to the person on the Inrerner who reminded us of Jennrich’s article. Address correspondence to William Gardner, Department of Psychiatry, University of Pittsburgh School of Medicine, 381 1 O’Hara Street, Pittsburgh, PA 15238, U.S.A.
PY - 1991
Y1 - 1991
N2 - Event history analysis is a means of explaining variation in the timing of events in individual life histories. This article describes methods for overcoming two difficult problems likely to be encountered in applications of event history analysis to studies of aging and human development. First, in many studies the ages of occurrence of critical life events are recorded in discrete units such as years, but the probability distributions of life events are usually specified in continuous-time form. We show how to estimate models for discrete-time data based on an underlying continuous-time specification. Second, the standard distributions for life events often fail to capture the complex age-dependence seen in actual data. We show how to construct a model using segmented hazards, that is, a composite of different functions for different segments of time. To illustrate these points, we study the age of first intercourse of 11,883 subjects from the National Longitudinal Study of Youth.
AB - Event history analysis is a means of explaining variation in the timing of events in individual life histories. This article describes methods for overcoming two difficult problems likely to be encountered in applications of event history analysis to studies of aging and human development. First, in many studies the ages of occurrence of critical life events are recorded in discrete units such as years, but the probability distributions of life events are usually specified in continuous-time form. We show how to estimate models for discrete-time data based on an underlying continuous-time specification. Second, the standard distributions for life events often fail to capture the complex age-dependence seen in actual data. We show how to construct a model using segmented hazards, that is, a composite of different functions for different segments of time. To illustrate these points, we study the age of first intercourse of 11,883 subjects from the National Longitudinal Study of Youth.
UR - https://www.scopus.com/pages/publications/0026399095
U2 - 10.1080/03610739108253902
DO - 10.1080/03610739108253902
M3 - Article
C2 - 1820290
AN - SCOPUS:0026399095
SN - 0361-073X
VL - 17
SP - 251
EP - 260
JO - Experimental Aging Research
JF - Experimental Aging Research
IS - 4
ER -