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Distributed Cox proportional hazards regression using summary-level information.

Dongdong Li1, Wenbin Lu2, Di Shu3

  • 1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA.

Biostatistics (Oxford, England)
|February 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distributed method for fitting Cox proportional hazards models without sharing individual data across multiple sites. This approach enables robust statistical analysis while preserving data privacy and overcoming logistical challenges in multi-site research.

Keywords:
Distributed Cox PH regressionMeta-analysisMulti-site studySummary-level information

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Individual-level data sharing is often infeasible in multi-site studies due to privacy and logistical constraints.
  • Existing methods like meta-analysis may not fully leverage multi-site data for complex models.
  • There is a need for distributed statistical methodologies that preserve data privacy.

Approach:

  • Proposes a general distributed methodology to fit Cox proportional hazards models using only summary-level statistics.
  • Develops an approximated partial likelihood score function for inference on log hazard ratios.
  • The method accommodates stratified/unstratified models, discrete/continuous variables, and multiple covariates.

Key Points:

  • Enables fitting stratified Cox models with a single summary-level data transfer.
  • Derived asymptotic properties of the proposed estimators.
  • Compared proposed estimators against pooled individual-level data and meta-analysis methods via simulations.

Conclusions:

  • The distributed methodology provides a viable alternative to individual-level data sharing for multi-site Cox model fitting.
  • Demonstrates the method's application in comparing bariatric surgeries (sleeve gastrectomy vs. Roux-en-Y gastric bypass) for postoperative readmission.
  • Facilitates privacy-preserving statistical analysis in collaborative research settings.