Multiple Imputation For Interval Censored Data With Auxiliary Variables

Chiu-Hsieh Hsu, Jeremy Taylor and Susan Murray
Additional contact information
Chiu-Hsieh Hsu: Arizona Cancer Center
Jeremy Taylor: University of Michigan
Susan Murray: University of Michigan Biostatistics

No 1025, The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: We propose a nonparametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and the measures of uncertainty are easily obtained. In addition to the event time of primary interest, there are frequently other auxiliary variables that are associated with the event time. For the goal of estimating the marginal survival distribution, these auxiliary variables may provide some additional information about the event time for the interval censored observations. We extend the imputation methods to incorporate information from auxiliary variables with potentially complex structures. To conduct the imputation, we use a working failure-time proportional hazards model to define an imputing risk set for each censored observations. The imputation schemes consist of using the data in the imputing risk set to create an exact event time for each interval censored observation. In simulation studies we show that the use of multiple imputation methods can improve the efficiency of estimators and reduce the effect of missing visits when compared to simpler approaches. We apply the approach to cytomegalovirus shedding data from an AIDS clinical trial, in which CD4 count is the auxiliary variable.

Keywords: auxiliary variables; interval censored; multiple imputation (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:umichbiostat-1025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.bepress.com/cgi/viewcontent.cgi?article=1025&context=umichbiostat (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bep:mchbio:1025

Access Statistics for this paper

More papers in The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press
Bibliographic data for series maintained by Christopher F. Baum ().