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Using Quadratic Programming to Reconstruct Data From Published Survival and Competing Risks Analyses.

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  • 1School of Mathematical Sciences, Lancaster University, Lancaster, Lancashire, UK.

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Summary
This summary is machine-generated.

This study introduces a novel quadratic programming method to reconstruct pseudo-individual patient data from survival analyses. This approach enhances meta-analysis and cost-effectiveness modeling by utilizing more available data.

Keywords:
competing risks analysispseudo‐individual patient dataquadratic programmingsurvival analysis

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Area of Science:

  • Biostatistics
  • Health Economics
  • Medical Informatics

Background:

  • Accurate reconstruction of pseudo-individual patient data (IPD) from published survival studies is crucial for meta-analysis, evidence synthesis, and cost-effectiveness decision modeling.
  • Existing methods for pseudo-IPD retrieval, primarily from Kaplan-Meier plots, have limitations in extensibility to diverse survival data types and incorporating all available information.

Purpose of the Study:

  • To propose an optimization-based approach using quadratic programming (QP) to reconstruct pseudo-IPD from survival data.
  • To demonstrate the method's capability to incorporate auxiliary information like marked censoring times.
  • To extend the approach for reconstructing competing risks survival data from cumulative incidence functions.

Main Methods:

  • Formulated the IPD reconstruction as a quadratic program (QP) with linear constraints.
  • Developed a method to incorporate auxiliary data, including marked censoring times.
  • Applied the QP approach to reconstruct competing risks survival data from cumulative incidence functions.

Main Results:

  • The QP-based method outperforms existing algorithms, especially when data on numbers at risk and marked censoring times are available.
  • The approach successfully reconstructed patient-level data from a published study on advanced stage follicular lymphoma.
  • Demonstrated superior performance in simulation studies compared to traditional methods.

Conclusions:

  • The proposed QP-based method offers a flexible and powerful approach for reconstructing pseudo-IPD from various survival data types.
  • This technique improves the accuracy and completeness of data available for health economic evaluations and clinical research.
  • Facilitates more robust secondary data analyses and evidence synthesis from published literature.