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Identifiability assumptions for missing covariate data in failure time regression models.

Paul J Rathouz1

  • 1Department of Health Studies, University of Chicago, Chicago, IL 60637, USA. prathouz@uchicago.edu

Biostatistics (Oxford, England)
|July 15, 2006
PubMed
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The missing at random (MAR) assumption for missing covariate data in survival models is difficult to justify. New assumptions are proposed, offering identifiable failure time models and consistent estimators.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • * Traditional survival models rely on the missing at random (MAR) assumption for covariate data.
  • * MAR implies missingness depends on observed data, including failure or censoring times.
  • * Justifying MAR in practical applications often requires strong, unverified assumptions.

Purpose of the Study:

  • * To investigate missingness mechanisms that yield MAR data in survival models.
  • * To evaluate the practical limitations of the MAR assumption.
  • * To propose and validate alternative missingness assumptions for survival data.

Main Methods:

  • * Explored scenarios where missing covariate data depends on true failure (T) and censoring (C) times.
  • * Identified conditions under which MAR is tenable.

Related Experiment Videos

  • * Developed two novel missingness assumptions: dependence on T only, and dependence on C only.
  • * Proposed methods to assess the plausibility of these new assumptions.

    • Main Results:

    • * MAR is challenging to justify without additional strong assumptions.
    • * Proposed assumptions (T-dependent or C-dependent missingness) lead to identifiable failure time models.
    • * Missingness independent of T allows consistent estimation using complete case analysis.
    • * Missingness independent of C enables a complete record likelihood estimator for parametric models.

    Conclusions:

    • * The MAR assumption is often impractical for missing covariate data in survival analysis.
    • * Novel T-dependent and C-dependent missingness assumptions provide identifiable models.
    • * Proposed methods offer practical alternatives and validation strategies for real-world data analysis.