{"record_key":"software-based-causal-survival-analysis-and-model-ranking","identifier":"CU27024","external_key":"CU27024","title":"Software-based causal survival analysis and model-ranking workflow","summary":null,"url":"https://inventions.techventures.columbia.edu/technologies/software-based-causal-survival--CU27024","json_url":"https://inventions.techventures.columbia.edu/technologies/software-based-causal-survival--CU27024.json","object_key":"technologies","description":"This technology is a software-based causal survival analysis and model-ranking workflow for estimating treatment effects to guide model or experiment selection from time-to-event cohort data.\nUnmet Need: Reliable causal survival analysis for incomplete cohort data\nCommonly used survival regression methods and model-selection approaches can produce biased or unstable conclusions from confounding, censoring, model misspecification, or limited validation. Reliable approaches are needed that estimate treatment benefit or harm from human cohort data when outcomes are time-to-event or follow-up is incomplete. Inaccurate treatment-effect estimates and poorly chosen mechanistic models can misdirect clinical interpretation, research priorities, and future experiment selection.\nThe Technology: Causal survival analytics for reliable treatment-effect and model selection\nThis technology is a software-based workflow for analyzing time-to-event cohort data, including datasets with right-censored outcomes. It applies causal survival analysis methods to account for measured confounding and incomplete follow-up, enabling treatment-effect estimation over time with reduced sensitivity to model misspecification. The workflow also ranks candidate mechanistic models based on out-of-sample prediction of a common measurable endpoint, rather than relying primarily on in-sample fit. It can further prioritize future experiments by balancing expected information gain against practical constraints, with validation performed on censored survival datasets using out-of-sample performance metrics. This provides a more objective and efficient basis for estimating treatment effects, selecting models, and planning follow-up studies.\nApplications:\nPharmaceutical screening tool for treatment efficacy\nCarcinogenesis modeling for long-term risk forecasting\nOncology tool for predicting patient outcomes\nScreening tool for observational clinical datasets (e.g. safety, efficacy, mortality)\nSoftware for health technology assessment using clinical or market data\nHealthcare payers evaluation systems\nResearch tool for model and experiment selection\nResearch tool for censored survival outcomes (e.g. epidemiology, biostatistics, public health, clinical)\nAdvantages:\nCombines causal survival analysis, mechanistic model evaluation, and experiment-prioritization workflows in a single software framework\nAccounts for measured confounding and right-censoring datasets\nSupports robust estimators that reduce sensitivity to model misspecification\nRanks models using out-of-sample prediction to better reflect performance\nRecommends future experiments from expected information gain balanced with practical constraints\nLead Inventor:\nIgor Shuryak, M.D., Ph.D.: https://www.crr.columbia.edu/profile/igor-shuryak-md\nRelated Publications:\nShuryak I, Hahnfeldt P, Hlatky L, Sachs RK, Brenner DJ. “A new view of radiation-induced cancer: integrating short-and long-term processes. Part I: approach.” Radiation and environmental biophysics. 2009 Aug;48(3):263-74.: https://pubmed.ncbi.nlm.nih.gov/19536557/\nShuryak I, Hahnfeldt P, Hlatky L, Sachs RK, Brenner DJ. “A new view of radiation-induced cancer: integrating short-and long-term processes. Part II: second cancer risk estimation.” Radiation and environmental biophysics. 2009 Aug;48(3):275-86.: https://pubmed.ncbi.nlm.nih.gov/19499238/\nShuryak I, Brenner DJ, Ullrich RL. “Radiation-induced carcinogenesis: mechanistically based differences between gamma-rays and neutrons, and interactions with DMBA.” PloS one. 2011 Dec 14;6(12):e28559.: https://pubmed.ncbi.nlm.nih.gov/22194850/\nShuryak I, Dadachova E. “Quantitative modeling of microbial population responses to chronic irradiation combined with other stressors.” PloS one. 2016 Jan 25;11(1):e0147696.: https://pubmed.ncbi.nlm.nih.gov/26808049/\nShuryak I. “Mechanistic modeling of dose and dose rate dependences of radiation-induced DNA double strand break rejoining kinetics in Saccharomyces cerevisiae.” Plos one. 2016 Jan 7;11(1):e0146407.: https://pubmed.ncbi.nlm.nih.gov/26741137/\nShuryak I, Turner HC, Pujol-Canadell M, Perrier JR, Garty G, Brenner DJ. “Machine learning methodology for high throughput personalized neutron dose reconstruction in mixed neutron+ photon exposures.” Scientific reports. 2021 Feb 17;11(1):4022.: https://pubmed.ncbi.nlm.nih.gov/33597632/\nShuryak I, Sachs RK, Brenner DJ. “Quantitative modeling of carcinogenesis induced by single beams or mixtures of space radiations using targeted and non-targeted effects.” Scientific Reports. 2021 Dec 6;11(1):23467.: https://pubmed.ncbi.nlm.nih.gov/34873209/\nShuryak I, Wang E, Brenner DJ. “Understanding the impact of radiotherapy fractionation on overall survival in a large head and neck squamous cell carcinoma dataset: a comprehensive approach combining mechanistic and machine learning models.” Frontiers in Oncology. 2024 Aug 13;14:1422211.: https://pubmed.ncbi.nlm.nih.gov/39193391/\nWang E, Shuryak I, Brenner DJ. “A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice.” Scientific Reports. 2024 Aug 2;14(1):17974.: https://pubmed.ncbi.nlm.nih.gov/39095647/\nWang E, Shuryak I, Brenner DJ. “Quantitative modeling of mortality patterns in dogs exposed to alpha particle emitting radionuclides: Insights from competing risks and causal inference machine learning.” Plos one. 2025 Jul 21;20(7):e0328082.: https://pubmed.ncbi.nlm.nih.gov/40690476/\nTech Ventures Reference:\nIR CU27024\nLicensing Contact: Joan Martinez: mailto:techtransfer@columbia.edu","attributes":[{"key":"inventors","label":"Inventors","values":["Eric Wang","Igor Shuryak"]},{"key":"manager","label":"Manager","values":["Joan Martinez"]},{"key":"depts","label":"Departments","values":["Center for Radiological Research","Radiation Oncology"]},{"key":"divs","label":"Divisions","values":["Columbia University Medical Center (CUMC)"]},{"key":"file_number","label":"File Number","values":["CU27024"]},{"key":"date_released","label":"Release Date","values":["Nov 9, 2026"]}],"related":[]}