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Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: Applications to distributed computing under disclosure constraints.

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Survival Analysis Without Sharing of Individual Patient Data by Using a Gaussian Copula.

Federico Bonofiglio1,2

  • 1Evidence and Value Generation Team, Veramed GmbH, Frankfurt am Main, Germany.

Pharmaceutical Statistics
|July 8, 2024
PubMed
Summary

This study introduces a Gaussian copula (GC) method to generate pseudodata from non-disclosive aggregates, enabling survival analyses without sharing individual patient data (IPD). The GC method approximates IPD bootstrap utility while preserving privacy.

Keywords:
Gaussian‐copulaKaplan–Meierdistributed‐computing‐networkmultilevel‐Coxmulti‐centerprivacy

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Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Data Privacy in Healthcare

Background:

  • Individual patient data (IPD) is crucial for Cox regression and Kaplan-Meier survival analyses.
  • Sharing IPD is often restricted due to privacy and proprietary concerns, hindering essential clinical research.
  • Existing methods for survival estimation without direct IPD access have limitations.

Purpose of the Study:

  • To propose and evaluate a novel method for generating pseudodata that approximates IPD for survival analyses.
  • To enable robust survival estimations while circumventing legal and privacy barriers associated with IPD sharing.
  • To assess the utility and limitations of the proposed Gaussian copula (GC) method compared to existing approaches.

Main Methods:

  • Developed a Gaussian copula (GC) model to generate pseudodata using non-disclosive IPD aggregates (marginal moments, correlation matrix).
  • Collected aggregates via a central computer and used them as parameters for the GC.
  • Performed survival inferences on the generated pseudodata, treating it as original IPD.

Main Results:

  • The GC method successfully generates pseudodata that approximates the inferential utility of IPD bootstrap.
  • GC-derived inferences may be more conservative and have limitations in subgroup analyses compared to direct IPD analysis.
  • The proposed method effectively addresses privacy and property concerns related to IPD sharing.

Conclusions:

  • The Gaussian copula (GC) method offers a viable solution for conducting IPD-based survival analyses when direct data sharing is not feasible.
  • Increased sharing of IPD aggregates could facilitate secondary research and alleviate concerns regarding data access.
  • This approach enhances the feasibility of essential survival analyses in clinical research while upholding data privacy.