Award Abstract #9811983
Inference With Contextual and Individual Level Time-Dependent Covariates in Event History Models

NSF Org:	SES Division of Social and Economic Sciences
Initial Amendment Date:	August 28, 1998
Latest Amendment Date:	August 28, 1998
Award Number:	9811983
Award Instrument:	Fellowship
Program Manager:	Cheryl L. Eavey SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral & Economic Sciences
Start Date:	January 1, 1999
Expires:	December 31, 1999 (Estimated)
Awarded Amount to Date:	$64218
Investigator(s):	Susan Murphy samurphy@umich.edu (Principal Investigator)
Sponsor:	University of Michigan Ann Arbor 3003 South State St. Ann Arbor, MI 48109 734/764-1817
NSF Program(s):	METHOD, MEASURE & STATS, STATISTICS
Field Application(s):	0000099 Other Applications NEC
Program Reference Code(s):	OTHR, 0000
Program Element Code(s):	1333, 1269

ABSTRACT

Social scientists use event history models in order the understand the causes of variation in the duration and timing of many life events such as the duration of poverty spells, timing of retirement, timing of premarital birth, timing of drug abuse initiation, etc. An endogenous time-dependent covariate such as amount of governmental support and person-tailored intervention programs may be used to explain this variation. Since the value of the endogenous time-dependent covariate may be partially determined or selected by the subject, it is important to adjust for confounding. Confounding may occur when both the time-dependent covariate and the duration or timing are outcomes of a common cause. In this case the time-dependent covariate may be associated with the variation in the duration or timing, yet may not cause the variation. Yet in designing intervention programs or social programs it is important to understand the causes of the variation in duration/timing. In order to ascertain the proportion of variation in duration/timing caused by time-dependent covariate, confounding must be controlled. However, proper adjustment for confounding of the effect of a time-dependent covariate on the outcome needs very careful thought. Indeed, the traditional approach of controlling for confounding by including the confounder in the event history analysis model will often only introduce more confounding. This project will (1) apply an experimental perspective to questions concerning the effect of a time-dependent covariate on duration/timing, (2) illustrate the confounding issues inherent in measuring the effects of time-dependent covariates, (3) illustrate how confounding may be eliminated, and (4) develop research methodology to eliminate confounding of the effect of time-dependent contextual covariates on hazard rates in multilevel models. This research is supported by the Methodology, Measurement, and Statistics Program and the Statistics and Probability Program under the Mid-Career Methodological Opportunities Fellowship Announcement.

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