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Imputing missing time-dependent covariate values for the discrete time Cox model.

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Statistical Methods in Medical Research
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Summary

This study introduces a novel method for handling missing data in time-dependent Cox models. The chained equations approach effectively imputes values, showing good performance except in severe non-random missingness scenarios.

Keywords:
MICE imputationMissing datafully conditional specification imputationincomplete covariatemissing covariatemultiple imputation

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Missing data in time-dependent covariates pose challenges in survival analysis.
  • Accurate imputation is crucial for reliable estimation in Cox models.

Purpose of the Study:

  • To present a time-sequential chained equations procedure for imputing missing values of time-dependent covariates in discrete time Cox models.
  • To evaluate the performance of this imputation method under various missing data patterns.

Main Methods:

  • Developed a multiply-imputation procedure using chained equations in a time-sequential manner.
  • The imputation model incorporates current and previous covariates and the survival outcome.
  • Applied the method to diabetic data to assess cancer risk associated with glucose control.
  • Conducted simulations to assess bias and coverage.

Main Results:

  • The proposed estimator demonstrated good performance (bias and coverage) for missing completely at random, missing at random, and moderate non-missing at random patterns.
  • Significant bias and low coverage were observed under very strong non-missing at random patterns.
  • The procedure is implementable using standard multiple imputation software (e.g., SAS FCS, R MICE).

Conclusions:

  • The time-sequential chained equations method is a viable approach for imputing missing time-dependent covariates in discrete time Cox models.
  • Caution is advised for strong non-missing at random data patterns, where the method may yield biased results.
  • The method requires grouping continuous event times into intervals for application.