Generalized Additive Models for Current Status Data |
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Authors: | Shiboski Stephen C |
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Institution: | (1) Department of Epidemiology and Biostatistics, University of California San Francisco, Box 0560, San Francisco, CA, 94143-0560, USA. E-mail |
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Abstract: | Current status data arise in studies where the target measurement is the time of occurrence of some event, but observations
are limited to indicators of whether or not the event has occurred at the time the sample is collected - only the current
status of each individual with respect to event occurrence is observed. Examples of such data arise in several fields, including
demography, epidemiology, econometrics and bioassay. Although estimation of the marginal distribution of times of event occurrence
is well understood, techniques for incorporating covariate information are not well developed. This paper proposes a semiparametric
approach to estimation for regression models of current status data, using techniques from generalized additive modeling and
isotonic regression. This procedure provides simultaneous estimates of the baseline distribution of event times and covariate
effects. No parametric assumptions about the form of the baseline distribution are required. The results are illustrated using
data from a demographic survey of breastfeeding practices in developing countries, and from an epidemiological study of heterosexual
Human Immunodeficiency Virus (HIV) transmission.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | Current status data survival analysis generalized additive model semiparametric estimation isotonic regression |
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