Using quadratic programming to reconstruct data from published survival and competing risks analyses

Titman, Andrew (2026) Using quadratic programming to reconstruct data from published survival and competing risks analyses. Statistics in Medicine. pp. 1-25. ISSN 0277-6715 (In Press)

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Abstract

The ability to retrieve pseudo-individual patient data (IPD) from published survival study results is important to facilitate meta-analysis, evidence synthesis or secondary data analyses for the purpose of decision modelling for cost effectiveness analysis. While established methods exist for retrieving pseudo-IPD from Kaplan--Meier plots, these algorithms are not easily extendable to other types of survival data, nor do they allow all available information to be incorporated. An optimization-based approach is proposed where the task of reconstructing the IPD is formulated as a quadratic program (QP) with linear constraints. The method easily allows auxiliary information such as marked censoring times. Moreover, the same approach can be used to reconstruct patient-level competing risks survival data from published cumulative incidence functions. In simulation, the QP-based method is shown to outperform existing algorithms particularly when data on numbers at risk and marked censoring times are available. The methods are illustrated through reconstruction of data from a published study on patients with advanced stage follicular lymphoma.