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Grant Details

Grant Number: 5R01CA269696-02 Interpret this number
Primary Investigator: Ning, Jing
Organization: University Of Tx Md Anderson Can Ctr
Project Title: Statistical Methods for Integration of Multiple Data Sources Toward Precision Cancer Medicine
Fiscal Year: 2023


Abstract

Project Summary: The primary objective of this research is to develop novel statistical and computational tools to evaluate new and existing cancer therapies for precision cancer medicine, with a principal focus on integrating multiple data sources including randomized controlled trials (RCT) and real world data (RWD). All of the aims are motivated by multidisciplinary collaboration. Evidence-based clinical decision making involves synthesizing available research evidence from multiple resources, including RCT and RWD. Pivotal RCTs are the primary evidence that established the oncologic equivalence or efficacy of local and systemic treatments. However, a recent systematic review found little agreement between population-based RWD and RCTs when comparing the same oncologic treatment regimens. This difference is thought to stem from the highly selective criteria used for trial enrollment coupled with the rapidly changing nature of multidisciplinary cancer care. Moreover, heterogeneous treatment effects by disease biologic tumor subtype on survival outcomes has not been examined sufficiently in early RCTs. We will develop statistical tools and software to evaluate the agreement of findings from RCTs and the real-world patient population, reassessing standard treatment guidelines on local- regional therapies for early-stage breast cancer by patients’ clinical and tumor subtypes. While the proposed methodology is agnostic to disease type, we will use breast cancer patients as proof of principle for the approaches proposed. The specific aims are: (1) to estimate and assess the agreement of treatment efficacy on survival outcomes across multiple studies (e.g., RCT and RWD) using nonparametric calibration weights to adjust for treatment selection bias and heterogeneity between studies; (2) to test the existence of a subgroup of patients with enhanced treatment effect and predict subgroup membership of a treatment using a semi-parametric isotonic- Cox model, and to develop a concordance-assisted learning tool for threshold identification to guide patient treatment selection; (3) to infer the treatment effects on breast cancer-specific survival when the cause of death is unknown in RWD by integrating data from RCT and RWD; (4) to estimate treatment effect for rare subtypes of breast cancer by combining external aggregate data with individual-level data to improve inference efficiency; and (5) to develop and disseminate publicly available, user-friendly software and facilitate the reproducibility and applications of our methods to multiple existing databases, including large-population-level data and RCT data for breast cancer research. The proposed research will advance general methodologic development in comparative effectiveness and precision medicine research by efficiently integrating multiple data sources. More importantly, the study findings could improve evidence-based treatment recommendations, better informing clinicians to select optimal treatments according to patients’ tumor subtypes and other characteristics, thus furthering clinical care via better integration of clinical science.



Publications

Addressing subject heterogeneity in time-dependent discrimination for biomarker evaluation.
Authors: Jiang X. , Li W. , Li R. , Ning J. .
Source: Statistics in medicine, 2024-03-30; 43(7), p. 1341-1353.
EPub date: 2024-01-29.
PMID: 38287471
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Improved mortality prediction for pediatric acute liver failure using dynamic prediction strategy.
Authors: Li R. , Wang J. , Zhang C. , Squires J.E. , Belle S.H. , Ning J. , Cai J. , Squires R.H. .
Source: Journal of pediatric gastroenterology and nutrition, 2024 Feb; 78(2), p. 320-327.
EPub date: 2023-12-12.
PMID: 38374548
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Longitudinal varying coefficient single-index model with censored covariates.
Authors: Wang S. , Ning J. , Xu Y. , Shih Y.T. , Shen Y. , Li L. .
Source: Biometrics, 2024-01-29; 80(1), .
PMID: 38364803
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Dynamic risk score modeling for multiple longitudinal risk factors and survival.
Authors: Zhang C. , Ning J. , Cai J. , Squires J.E. , Belle S.H. , Li R. .
Source: Computational statistics & data analysis, 2024 Jan; 189, .
EPub date: 2023-08-30.
PMID: 37720873
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A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information.
Authors: Ding M. , Li R. , Qin J. , Ning J. .
Source: Biometrics, 2023 Dec; 79(4), p. 3227-3238.
EPub date: 2023-06-13.
PMID: 37312587
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Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index.
Authors: Wang J. , Jiang X. , Ning J. .
Source: Biostatistics (Oxford, England), 2023-11-10; , .
EPub date: 2023-11-10.
PMID: 37952117
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Clinical outcome and therapeutic impact on neuroendocrine neoplasms of the breast: a national cancer database study.
Authors: Yang L. , Lin H. , Shen Y. , Roy M. , Albarracin C. , Ding Q. , Huo L. , Chen H. , Wei B. , Bu H. , et al. .
Source: Breast cancer research and treatment, 2023 Nov; 202(1), p. 23-32.
EPub date: 2023-08-11.
PMID: 37566192
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Reassessing Estrogen Receptor Expression Thresholds for Breast Cancer Prognosis in HER2-negative Patients Using Shape Restricted Modeling.
Authors: Dong W. , Fujii T. , Ning J. , Iwase T. , Qin J. , Ueno N.T. , Shen Y. .
Source: Research square, 2023-10-28; , .
EPub date: 2023-10-28.
PMID: 37961619
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Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study.
Authors: Pak D. , Ning J. , Kryscio R.J. , Shen Y. .
Source: Lifetime data analysis, 2023 Oct; 29(4), p. 752-768.
EPub date: 2023-05-20.
PMID: 37210470
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Detection method has independent prognostic significance in the PLCO lung screening trial.
Authors: Long J.P. , Shen Y. .
Source: Scientific reports, 2023-08-17; 13(1), p. 13382.
EPub date: 2023-08-17.
PMID: 37591907
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Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes.
Authors: Zhang J. , Ning J. , Li R. .
Source: Statistics in biosciences, 2023 Jul; 15(2), p. 353-371.
EPub date: 2023-02-02.
PMID: 37691982
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Conditional concordance-assisted learning under matched case-control design for combining biomarkers for population screening.
Authors: Li W. , Li R. , Yan Q. , Feng Z. , Ning J. .
Source: Statistics in medicine, 2023-04-30; 42(9), p. 1398-1411.
EPub date: 2023-02-02.
PMID: 36733187
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Effectiveness Without Efficacy: Cautionary Tale from a Landmark Breast Cancer Randomized Controlled Trial.
Authors: Shen Y. , Ning J. , Lin H.Y. , Shaitelman S.F. , Kuerer H.M. , Bedrosian I. .
Source: Journal of Cancer, 2023; 14(2), p. 193-199.
EPub date: 2023-01-01.
PMID: 36741254
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Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.
Authors: Li Z. , Shen Y. , Ning J. .
Source: Journal of the American Statistical Association, 2023; 118(544), p. 2276-2287.
EPub date: 2023-06-26.
PMID: 38505403
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Semiparametric regression modeling of the global percentile outcome.
Authors: Liu X. , Ning J. , He X. , Tilley B.C. , Li R. .
Source: Journal of statistical planning and inference, 2023 Jan; 222, p. 149-159.
EPub date: 2022-06-24.
PMID: 36467464
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Assessing predictive discrimination performance of biomarkers in the presence of treatment-induced dependent censoring.
Authors: Zhang C. , Ning J. , Belle S.H. , Squires R.H. , Cai J. , Li R. .
Source: Journal of the Royal Statistical Society. Series C, Applied statistics, 2022 Nov; 71(5), p. 1137-1157.
EPub date: 2022-05-25.
PMID: 36466585
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