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Multiple Imputation For Interval Censored Data With Auxiliary Variables

Author

Listed:
  • Chiu-Hsieh Hsu

    (Arizona Cancer Center)

  • Jeremy Taylor

    (University of Michigan)

  • Susan Murray

    (University of Michigan Biostatistics)

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.

Suggested Citation

  • Chiu-Hsieh Hsu & Jeremy Taylor & Susan Murray, 2004. "Multiple Imputation For Interval Censored Data With Auxiliary Variables," The University of Michigan Department of Biostatistics Working Paper Series 1025, Berkeley Electronic Press.
    • Handle: RePEc:bep:mchbio:1025
    Note: oai:bepress.com:umichbiostat-1025
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    References listed on IDEAS

    as
    1. Daniel F. Heitjan & Roderick J. A. Little, 1991. "Multiple Imputation for the Fatal Accident Reporting System," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 13-29, March.
    2. Wei Pan, 2000. "A Multiple Imputation Approach to Cox Regression with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(1), pages 199-203, March.
    3. Taylor, Jeremy M. G. & Murray, Susan & Hsu, Chiu-Hsieh, 2002. "Survival estimation and testing via multiple imputation," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 221-232, July.
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