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Grant Details

Grant Number: 5R01CA129102-12 Interpret this number
Primary Investigator: Taylor, Jeremy
Organization: University Of Michigan At Ann Arbor
Project Title: Statistical Methods for Cancer Biomarkers
Fiscal Year: 2021


Abstract

Project Summary/Abstract Individualized prognostic models abound in clinical biomedicine. They are used to make predictions of the future, derived from individual patient characteristics, and will play increasingly important roles in the move towards per- sonalized medicine. They can be used in the settings of early detection and screening, or after a cancer diagnosis to help decide on treatment, or after treatment to monitor for progression and recurrence. While some models are well established, they likely have the potential to be improved through the use of additional variables. Larger and better quality training datasets and improved statistical models and methods will improve their accuracy, but the potential for largest improvement is through new biomarkers. Since cancer is a heterogenous disease with multifactorial etiology, many clinical and molecular factors will likely aid in predicting the future for a patient, and would be candidates for inclusion in a new model. The challenge we will address in this research is how to de- velop a new model that both includes the new biomarkers and makes use of the knowledge implicit in the existing models, when the datasets that are available containing the new biomarkers are only of modest size. To develop a new model from a new dataset of modest size that contains the new biomarkers, the typical approach will be to analyze these data, as a separate entity, and build a model based on that analysis. However, this approach does not utilize the external information from an established model. Such external information will often be available, however it may come in the form of regression coefficients, odds ratios or other summary statistics for a subset of the variables, or in the form of a prediction from an online calculator. We will consider a variety of statistical methods for incorporating the external information. The methods we propose to develop are motivated by specific head and neck cancer and prostate cancer stud- ies, but have much broader applicability to other cancers and other diseases. In the head and neck study the additional new biomarkers to be incorporated in to the prediction models are HPV status and other molecular biomarkers. For the prostate cancer risk prediction model the new bimarkers are based on proteins measured from urine. The research is separated into three specific aims. The first aim considers the situation in which there is a modest sized new dataset, that includes a new biomarker, and there is an existing prediction model, that does not include this new biomarker. The external information comes in the form of estimates and standard errors of regression parameters from an established prediction model based on a subset of the predictors. We propose a number of different frequentist and Bayesian methods, in which the information on the lower dimensional parameter space is used via inequality constraints and Lagrange multipliers, through prior distributions and through a novel transformation approach. The properties of the approaches will be compared in the situation of continuous and binary response variables. In the second aim the external information comes in the form of a prediction from one or more calculators, and specifically the predictions for each individual in our own data are used. We include in this aim consideration of the situation where there are multiple established prediction models and where the outcome variable is the survival time. We consider different possible methodological approaches, one is an adaptation of the methods in the first aim, a second very general method is to incorporate synthetic data generated from the existing models and a third general method uses weights that enable the new biomarker to have a stronger role for observations that were were not predicted well by the existing models. In the third aim we consider the situation where there may be a panel of new biomarkers, and there is also knowledge about the unadjusted association between each new biomarker and the outcome variable, as might be available from a genome-wide association study. A novel nonparametric Bayes approach is proposed to solve this problem.



Publications

Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.
Authors: Han P. , Li H. , Park S.K. , Mukherjee B. , Taylor J.M.G. .
Source: Biometrics, 2024-07-01 00:00:00.0; 80(3), .
PMID: 39101548
Related Citations

Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.
Authors: Boonstra P.S. , Owen D.R. , Kang J. .
Source: Pharmaceutical Statistics, 2024-02-23 00:00:00.0; , .
EPub date: 2024-02-23 00:00:00.0.
PMID: 38400582
Related Citations

Robust data integration from multiple external sources for generalized linear models with binary outcomes.
Authors: Choi K. , Taylor J.M.G. , Han P. .
Source: Biometrics, 2024-01-29 00:00:00.0; 80(1), .
PMID: 38364808
Related Citations

surtvep: An R package for estimating time-varying effects.
Authors: Luo L. , Wu W. , Taylor J.M.G. , Kang J. , Kleinsasser M.J. , He K. .
Source: Journal Of Open Source Software, 2024; 9(98), .
EPub date: 2024-06-28 00:00:00.0.
PMID: 39717690
Related Citations

Surrogacy validation for time-to-event outcomes with illness-death frailty models.
Authors: Roberts E.K. , Elliott M.R. , Taylor J.M.G. .
Source: Biometrical Journal. Biometrische Zeitschrift, 2023-09-29 00:00:00.0; , p. e2200324.
EPub date: 2023-09-29 00:00:00.0.
PMID: 37776057
Related Citations

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.
Authors: Luo L. , He K. , Wu W. , Taylor J.M. .
Source: Statistical Methods In Medical Research, 2023 Sep; 32(9), p. 1664-1679.
EPub date: 2023-07-05 00:00:00.0.
PMID: 37408385
Related Citations

Integrating Information from Existing Risk Prediction Models with No Model Details.
Authors: Han P. , Taylor J.M.G. , Mukherjee B. .
Source: The Canadian Journal Of Statistics = Revue Canadienne De Statistique, 2023 Jun; 51(2), p. 355-374.
EPub date: 2022-04-15 00:00:00.0.
PMID: 37346757
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A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.
Authors: Gu T. , Taylor J.M.G. , Mukherjee B. .
Source: Biometrics, 2023-03-06 00:00:00.0; , .
EPub date: 2023-03-06 00:00:00.0.
PMID: 36876883
Related Citations

Data integration: exploiting ratios of parameter estimates from a reduced external model.
Authors: Taylor J.M.G. , Choi K. , Han P. .
Source: Biometrika, 2023 Mar; 110(1), p. 119-134.
EPub date: 2022-04-12 00:00:00.0.
PMID: 36798840
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Survival prediction models: an introduction to discrete-time modeling.
Authors: Suresh K. , Severn C. , Ghosh D. .
Source: Bmc Medical Research Methodology, 2022-07-26 00:00:00.0; 22(1), p. 207.
EPub date: 2022-07-26 00:00:00.0.
PMID: 35883032
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MIAMI: Mutual Information-based Analysis of Multiplex Imaging data.
Authors: Seal S. , Ghosh D. .
Source: Bioinformatics (oxford, England), 2022-06-24 00:00:00.0; , .
EPub date: 2022-06-24 00:00:00.0.
PMID: 35748713
Related Citations

Utility based approach in individualized optimal dose selection using machine learning methods.
Authors: Li P. , Taylor J.M.G. , Boonstra P.S. , Lawrence T.S. , Schipper M.J. .
Source: Statistics In Medicine, 2022-03-28 00:00:00.0; , .
EPub date: 2022-03-28 00:00:00.0.
PMID: 35343595
Related Citations

Utility based approach in individualized optimal dose selection using machine learning methods.
Authors: Li P. , Taylor J.M.G. , Boonstra P.S. , Lawrence T.S. , Schipper M.J. .
Source: Statistics In Medicine, 2022-03-28 00:00:00.0; , .
EPub date: 2022-03-28 00:00:00.0.
PMID: 35343595
Related Citations

The association between inflammatory biomarkers and statin use among patients with head and neck squamous cell carcinoma.
Authors: Getz K.R. , Bellile E. , Zarins K.R. , Chinn S.B. , Taylor J.M.G. , Rozek L.S. , Wolf G.T. , Mondul A.M. .
Source: Head & Neck, 2022-03-25 00:00:00.0; , .
EPub date: 2022-03-25 00:00:00.0.
PMID: 35338544
Related Citations

Sufficient Dimension Reduction: An Information-Theoretic Viewpoint.
Authors: Ghosh D. .
Source: Entropy (basel, Switzerland), 2022-01-22 00:00:00.0; 24(2), .
EPub date: 2022-01-22 00:00:00.0.
PMID: 35205462
Related Citations

Profiling Parkinson's disease cognitive phenotypes via resting-state magnetoencephalography.
Authors: Simon O.B. , Rojas D.C. , Ghosh D. , Yang X. , Rogers S.E. , Martin C.S. , Holden S.K. , Kluger B.M. , Buard I. .
Source: Journal Of Neurophysiology, 2022-01-01 00:00:00.0; 127(1), p. 279-289.
EPub date: 2021-12-22 00:00:00.0.
PMID: 34936515
Related Citations

Multiple imputation with missing data indicators.
Authors: Beesley L.J. , Bondarenko I. , Elliot M.R. , Kurian A.W. , Katz S.J. , Taylor J.M. .
Source: Statistical Methods In Medical Research, 2021-10-13 00:00:00.0; , p. 9622802211047346.
EPub date: 2021-10-13 00:00:00.0.
PMID: 34643465
Related Citations

A novel approach to understanding Parkinsonian cognitive decline using minimum spanning trees, edge cutting, and magnetoencephalography.
Authors: Simon O.B. , Buard I. , Rojas D.C. , Holden S.K. , Kluger B.M. , Ghosh D. .
Source: Scientific Reports, 2021-10-05 00:00:00.0; 11(1), p. 19704.
EPub date: 2021-10-05 00:00:00.0.
PMID: 34611218
Related Citations

Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm.
Authors: Roberts E.K. , Elliott M.R. , Taylor J.M.G. .
Source: Statistics In Medicine, 2021-09-15 00:00:00.0; , .
EPub date: 2021-09-15 00:00:00.0.
PMID: 34528260
Related Citations

Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.
Authors: Tang M. , Wang L. , Gorin M.A. , Taylor J.M.G. .
Source: Statistics In Medicine, 2021-09-07 00:00:00.0; , .
EPub date: 2021-09-07 00:00:00.0.
PMID: 34490942
Related Citations

Accounting for not-at-random missingness through imputation stacking.
Authors: Beesley L.J. , Taylor J.M.G. .
Source: Statistics In Medicine, 2021-08-29 00:00:00.0; , .
EPub date: 2021-08-29 00:00:00.0.
PMID: 34459011
Related Citations

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.
Authors: Raghavan S. , Josey K. , Bahn G. , Reda D. , Basu S. , Berkowitz S.A. , Emanuele N. , Reaven P. , Ghosh D. .
Source: Annals Of Epidemiology, 2021-07-17 00:00:00.0; , .
EPub date: 2021-07-17 00:00:00.0.
PMID: 34280545
Related Citations

A meta-inference framework to integrate multiple external models into a current study.
Authors: Gu T. , Taylor J.M.G. , Mukherjee B. .
Source: Biostatistics (oxford, England), 2021-07-16 00:00:00.0; , .
EPub date: 2021-07-16 00:00:00.0.
PMID: 34269371
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A copula-based approach for dynamic prediction of survival with a binary time-dependent covariate.
Authors: Suresh K. , Taylor J.M.G. , Tsodikov A. .
Source: Statistics In Medicine, 2021-06-14 00:00:00.0; , .
EPub date: 2021-06-14 00:00:00.0.
PMID: 34124771
Related Citations

Evaluation of predictive model performance of an existing model in the presence of missing data.
Authors: Li P. , Taylor J.M.G. , Spratt D.E. , Karnes R.J. , Schipper M.J. .
Source: Statistics In Medicine, 2021-04-11 00:00:00.0; , .
EPub date: 2021-04-11 00:00:00.0.
PMID: 33843085
Related Citations

Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring.
Authors: Cho Y. , Ghosh D. .
Source: Communications In Statistics: Theory And Methods, 2021; 50(1), p. 216-236.
EPub date: 2019-07-15 00:00:00.0.
PMID: 33716388
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ANALYSIS OF REGRESSION DISCONTINUITY DESIGNS USING CENSORED DATA.
Authors: Cho Y. , Hu C. , Ghosh D. .
Source: Journal Of Statistical Research, 2021; 55(1), p. 225-248.
EPub date: 2021-09-03 00:00:00.0.
PMID: 35755402
Related Citations

Statin use and head and neck squamous cell carcinoma outcomes.
Authors: Getz K.R. , Bellile E. , Zarins K.R. , Rullman C. , Chinn S.B. , Taylor J.M.G. , Rozek L.S. , Wolf G.T. , Mondul A.M. .
Source: International Journal Of Cancer, 2020-12-15 00:00:00.0; , .
EPub date: 2020-12-15 00:00:00.0.
PMID: 33320960
Related Citations

A stacked approach for chained equations multiple imputation incorporating the substantive model.
Authors: Beesley L.J. , Taylor J.M.G. .
Source: Biometrics, 2020-09-13 00:00:00.0; , .
EPub date: 2020-09-13 00:00:00.0.
PMID: 32920819
Related Citations

Quantifying the incremental value of deep learning: Application to lung nodule detection.
Authors: Warsavage T. , Xing F. , Barón A.E. , Feser W.J. , Hirsch E. , Miller Y.E. , Malkoski S. , Wolf H.J. , Wilson D.O. , Ghosh D. .
Source: Plos One, 2020; 15(4), p. e0231468.
EPub date: 2020-04-14 00:00:00.0.
PMID: 32287288
Related Citations

A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker.
Authors: Suresh K. , Taylor J.M.G. , Tsodikov A. .
Source: Biostatistics (oxford, England), 2019-12-10 00:00:00.0; , .
EPub date: 2019-12-10 00:00:00.0.
PMID: 31820798
Related Citations

Synthetic data method to incorporate external information into a current study.
Authors: Gu T. , Taylor J.M.G. , Cheng W. , Mukherjee B. .
Source: The Canadian Journal Of Statistics = Revue Canadienne De Statistique, 2019 Dec; 47(4), p. 580-603.
EPub date: 2019-06-26 00:00:00.0.
PMID: 32773922
Related Citations

A utility approach to individualized optimal dose selection using biomarkers.
Authors: Li P. , Taylor J.M.G. , Kong S. , Jolly S. , Schipper M.J. .
Source: Biometrical Journal. Biometrische Zeitschrift, 2019-11-06 00:00:00.0; , .
EPub date: 2019-11-06 00:00:00.0.
PMID: 31692022
Related Citations

Accounting for established predictors with the multistep elastic net.
Authors: Chase E.C. , Boonstra P.S. .
Source: Statistics In Medicine, 2019-07-17 00:00:00.0; , .
EPub date: 2019-07-17 00:00:00.0.
PMID: 31313344
Related Citations

Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information.
Authors: Cheng W. , Taylor J.M.G. , Gu T. , Tomlins S.A. , Mukherjee B. .
Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2019 Jan; 68(1), p. 121-139.
EPub date: 2018-08-13 00:00:00.0.
PMID: 31105344
Related Citations

Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability.
Authors: Estes J.P. , Mukherjee B. , Taylor J.M.G. .
Source: Statistics In Biosciences, 2018 Dec; 10(3), p. 568-586.
EPub date: 2018-05-14 00:00:00.0.
PMID: 31123532
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Individualized survival prediction for patients with oropharyngeal cancer in the human papillomavirus era.
Authors: Beesley L.J. , Hawkins P.G. , Amlani L.M. , Bellile E.L. , Casper K.A. , Chinn S.B. , Eisbruch A. , Mierzwa M.L. , Spector M.E. , Wolf G.T. , et al. .
Source: Cancer, 2018-10-06 00:00:00.0; , .
EPub date: 2018-10-06 00:00:00.0.
PMID: 30291798
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Incorporating historical models with adaptive Bayesian updates.
Authors: Boonstra P.S. , Barbaro R.P. .
Source: Biostatistics (oxford, England), 2018-09-21 00:00:00.0; , .
EPub date: 2018-09-21 00:00:00.0.
PMID: 30247557
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Prognostic Value of FDG-PET/CT Metabolic Parameters in Metastatic Radioiodine-Refractory Differentiated Thyroid Cancer.
Authors: Manohar P.M. , Beesley L.J. , Bellile E.L. , Worden F.P. , Avram A.M. .
Source: Clinical Nuclear Medicine, 2018 Sep; 43(9), p. 641-647.
PMID: 30015659
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Redefining Perineural Invasion: Integration of Biology With Clinical Outcome.
Authors: Schmitd L.B. , Beesley L.J. , Russo N. , Bellile E.L. , Inglehart R.C. , Liu M. , Romanowicz G. , Wolf G.T. , Taylor J.M.G. , D'Silva N.J. .
Source: Neoplasia (new York, N.y.), 2018 Jul; 20(7), p. 657-667.
EPub date: 2018-05-23 00:00:00.0.
PMID: 29800815
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Improving estimation and prediction in linear regression incorporating external information from an established reduced model.
Authors: Cheng W. , Taylor J.M.G. , Vokonas P.S. , Park S.K. , Mukherjee B. .
Source: Statistics In Medicine, 2018-01-24 00:00:00.0; , .
EPub date: 2018-01-24 00:00:00.0.
PMID: 29365342
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Estimating the Optimal Personalized Treatment Strategy Based on Selected Variables to Prolong Survival via Random Survival Forest with Weighted Bootstrap.
Authors: Shen J. , Wang L. , Daignault S. , Spratt D.E. , Morgan T.M. , Taylor J.M.G. .
Source: Journal Of Biopharmaceutical Statistics, 2018; 28(2), p. 362-381.
EPub date: 2017-10-25 00:00:00.0.
PMID: 28934002
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Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.
Authors: Suresh K. , Taylor J.M.G. , Spratt D.E. , Daignault S. , Tsodikov A. .
Source: Biometrical Journal. Biometrische Zeitschrift, 2017 Nov; 59(6), p. 1277-1300.
EPub date: 2017-05-16 00:00:00.0.
PMID: 28508545
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Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model.
Authors: Cho Y. , Hu C. , Ghosh D. .
Source: Statistics In Medicine, 2017-10-10 00:00:00.0; , .
EPub date: 2017-10-10 00:00:00.0.
PMID: 29023972
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Links between causal effects and causal association for surrogacy evaluation in a gaussian setting.
Authors: Conlon A. , Taylor J. , Li Y. , Diaz-Ordaz K. , Elliott M. .
Source: Statistics In Medicine, 2017-08-08 00:00:00.0; , .
EPub date: 2017-08-08 00:00:00.0.
PMID: 28786131
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Surrogacy assessment using principal stratification and a Gaussian copula model.
Authors: Conlon A. , Taylor J. , Elliott M.R. .
Source: Statistical Methods In Medical Research, 2017 Feb; 26(1), p. 88-107.
PMID: 24947559
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Increasing efficiency for estimating treatment-biomarker interactions with historical data.
Authors: Boonstra P.S. , Taylor J.M. , Mukherjee B. .
Source: Statistical Methods In Medical Research, 2016 Dec; 25(6), p. 2959-2971.
EPub date: 2014-05-21 00:00:00.0.
PMID: 24855118
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Individualized Risk Prediction Of Outcomes For Oral Cavity Cancer Patients
Authors: Prince V. , Bellile E.L. , Sun Y. , Wolf G.T. , Hoban C.W. , Shuman A.G. , Taylor J.M. .
Source: Oral Oncology, 2016 Dec; 63, p. 66-73.
PMID: 27939002
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Estimation Of The Optimal Regime In Treatment Of Prostate Cancer Recurrence From Observational Data Using Flexible Weighting Models
Authors: Shen J. , Wang L. , Taylor J.M. .
Source: Biometrics, 2016-11-28 00:00:00.0; , .
PMID: 27893926
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A modified risk set approach to biomarker evaluation studies.
Authors: Ghosh D. .
Source: Statistics In Biosciences, 2016 Oct; 8(2), p. 395-406.
EPub date: 2016-08-22 00:00:00.0.
PMID: 28989545
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