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Imputing missing covariates in time-to-event analysis within distributed research networks: A simulation study.

Dongdong Li1, Jenna Wong1, Xiaojuan Li1

  • 1Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA.

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Summary
This summary is machine-generated.

Multiple imputation methods combined with meta-analysis are valid for distributed research networks (DRNs) with missing covariate data in time-to-event analyses. Performance varies by effect size and missingness, with Random Forest showing efficiency in heterogeneous settings.

Keywords:
Cox modeldistributed research networksmissing covariatesmultiple imputationsimulation study

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Epidemiology

Background:

  • Distributed research networks (DRNs) present challenges for standard multiple imputation due to data pooling limitations.
  • The performance of imputation methods combined with meta-analysis in DRNs for time-to-event analyses is not well-established.

Approach:

  • Evaluated four imputation algorithms: approximated linear (Approx), substantive model compatible (SMC), random forest (RF), and classification and regression trees (CART).
  • Utilized simulation studies based on a real-world dataset to assess imputation performance under various missingness mechanisms and effect sizes.

Key Points:

  • All tested imputation methods outperformed complete-case analysis, yielding unbiased and more efficient estimates, especially under homogeneous missingness.
  • Random Forest (RF) demonstrated higher efficiency under heterogeneous missingness mechanisms.
  • Distributed imputation combined via meta-analysis produced results comparable to imputation using pooled data.

Conclusions:

  • Imputation within DRNs is valid and feasible for addressing missing covariate data in time-to-event analyses.
  • The effectiveness of different imputation algorithms (Approx, SMC, RF, CART) is contingent upon effect sizes and the extent of missing data.