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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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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