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Inverse probability weighted Cox model in multi-site studies without sharing individual-level data.

Di Shu1, Kazuki Yoshida2, Bruce H Fireman3

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

Statistical Methods in Medical Research
|August 27, 2019
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Summary

New methods allow hazard ratio estimation in multi-site studies without pooling data. These approaches facilitate robust statistical inference across sites, enhancing collaborative research efficiency.

Keywords:
Cox modeldistributed data networksinverse probability weightingmulti-site studyprivacy protectionrisk set

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Data Science

Background:

  • Estimating marginal hazard ratios using Cox proportional hazards models is crucial in multi-site studies.
  • Pooling individual-level data across sites is often impractical due to privacy and logistical challenges.

Purpose of the Study:

  • To propose and validate methods for hazard ratio inference in multi-site studies without data pooling.
  • To enable robust statistical analysis while minimizing data transfer requirements.

Main Methods:

  • Developed three novel methods for estimating hazard ratios from summary-level data.
  • Method 1 utilizes an eight-column risk-set table for direct estimation.
  • Methods 2 and 3 employ bootstrap re-sampling strategies with four-column and site-specific risk-set tables.

Main Results:

  • All proposed methods successfully replicate hazard ratio and variance estimates from pooled individual-level data analyses.
  • The methods require only a single file transfer between contributing sites and the analysis center.
  • Statistical performance was demonstrated using both simulated and real-world datasets.

Conclusions:

  • The proposed methods offer feasible and statistically sound alternatives for conducting Cox proportional hazards analyses in multi-site settings.
  • These approaches overcome data pooling limitations, promoting efficient and secure collaborative research.
  • The techniques facilitate robust inference on hazard ratios without compromising data privacy.