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ARXIV:2604.12137 · MEDICAL AI · SUBMITTED 15 APR · 16:48 UTC · FRESHNESS STALE
ARXIV:2604.12137MEDICAL AISUBMITTED 15 APR · 16:48 UTCFRESHNESS STALEVasiliki Stoumpou · Dimitris Bertsimas · Samuel Singer · Georgios Antonios Margonis · arXiv
A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation.
Opportunity summary
Pain A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation. Methods: We developed a three-step framework to address unobserved confounding in observational survival data.
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in observational…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results: In three observational cohorts (nine comparisons), balancing U improved agreement with trial HRs in all cases; in the strongest settings, it reduced absolute…
Medical AI moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation.
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10.48550/arXiv.2604.12137A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation.
Abstract
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in observational survival data. First, we infer a latent prognostic factor (U) from restricted mean survival time (RMST) discrepancies between patients with similar observed factors, the same treatment, and divergent outcomes, leveraging the idea that the aggregate effect of unmeasured factors can be inferred even if individual factors cannot. Second, we balance U with observed baseline covariates using prognostic matching, entropy balancing, or inverse probability of treatment weighting. Third, we apply multivariable survival analysis to estimate hazard ratios (HRs). We evaluated the framework in three observational cohorts with RCT benchmarks, two RCT cohorts, and six multicenter observational cohorts. Results: In three observational cohorts (nine comparisons), balancing U improved agreement with trial HRs in all cases; in the strongest settings, it reduced absolute log-HR error by approximately ten-fold versus using observed covariates alone (mean reduction 0.344; p=0.001). In two RCT cohorts, U was balanced across arms (most SMDs <0.1) and adjustment had minimal impact on log-HRs (mean absolute change 0.08). Across six multicenter cohorts, balancing U within centers reduced cross-center dispersion in chemotherapy log-HR estimates (mean reduction 0.147; p=0.016); when populations were directly balanced across centers to account for case-mix differences, cross-center survival differences were narrowed in 75%-100% of comparisons. Conclusions: Inferring and balancing a latent prognostic signal may reduce unobserved confounding and improve treatment-effect estimation from real-world data.
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PROBLEM
A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation. Methods: We developed a three-step framework to address unobserved confounding in observational survival data.
METHOD
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in obs...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results: In three observational cohorts (nine comparisons), balancing U improved agreement with trial HRs in all cases; in the strongest settings, it reduced absolute log-HR error by approximately ten-fol...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation. Methods: We developed a three-step framework to address unobserved confounding in observational survival data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in observational survival data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results: In three observational cohorts (nine comparisons), balancing U improved agreement with trial HRs in all cases; in the strongest settings, it reduced absolute log-HR error by approximately ten-fold versus using observed covariates alone (mean reduction 0.344; p=0.001). Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework to infer and balance latent prognostic factors from real-world survival data to improve treatment-effect estimation.
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