Grant Number:	1R01CA129102-01A2 Interpret this number
Primary Investigator:	Taylor, Jeremy
Organization:	University Of Michigan At Ann Arbor
Project Title:	Statistical Methods for Cancer Biomarkers
Fiscal Year:	2009

DESCRIPTION (provided by applicant): Biomarkers play an important role in medical research, and their use is being advocated in the detection, diagnosis, staging, treatment and management of a variety of diseases, including cancer. Using novel genomic and proteomic technologies, new biomarkers are constantly being discovered. Biomarkers are viewed by some as the most critical component in moving forward translational research in cancer. They are central to the critical path, articulated by the FDA, for enhancing drug development. There are several areas where statistical consideration would enhance the utility of biomarkers. First, many researchers have suggested that a single biomarker will be insufficient for prediction and that a panel of biomarkers will be necessary. This leads to the question of how to combine the biomarkers in an "optimal" manner. Second, many of the candidate biomarkers might be used as surrogate endpoints in randomized trials. Finally, for many biomarkers assumptions of monotonicity are a-priori known, e.g. increasing levels of biomarker are associated with increased disease risk. It would be valuable to have available statistical procedures that make use of this knowledge to improve the utility of the biomarkers. In this grant, we will be developing several new statistical modeling procedures for biomarker data in cancer studies. They are summarized in the following aims: Specific Aim 1: Development of semiparametric and nonparametric multivariate isotonic regression modelling procedures for biomarkers. (a) Two-stage estimation procedures for bivariate isotonic regression models and attendant nonparametric and semiparametric profile likelihood ratio inference procedures. (b) Isotonic estimation methods with ordered categorical covariates for censored data. Specific Aim 2: Development of statistical methods for the analysis of surrogate endpoints in a single-trial and multiple-trial framework. (a) Counterfactual-based modelling approach to the analysis of surrogate endpoints. (b) Shrinkage estimation approaches to the analysis of surrogate endpoints. (c) Semi-competing risks methodology for the analysis of surrogate endpoints. Specific Aim 3: Development of hybrid model averaging methods and attendant projection-based framework for combining biomarkers to optimize predictive accuracy. PUBLIC HEALTH RELEVANCE: Statistical Methods for Cancer Biomarkers. Biomarkers are measurements that can be taken from patient, for example from a simple blood test. Biomarkers do not measure how the patients feel or symptoms they have, never-the-less they may be very useful to give early detection of a disease, or to monitor how the treatment is working, or to assess whether a new treatment if effective. The aim of this grant is to develop valid methods of analyzing datasets of biomarkers, that get the most information out of the data.




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
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Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.
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Source: Pharmaceutical Statistics, 2024-02-23 00:00:00.0; , .
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PMID: 38400582
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Robust data integration from multiple external sources for generalized linear models with binary outcomes.
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Source: Biometrics, 2024-01-29 00:00:00.0; 80(1), .
PMID: 38364808
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Surrogacy validation for time-to-event outcomes with illness-death frailty models.
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Source: Biometrical Journal. Biometrische Zeitschrift, 2023-09-29 00:00:00.0; , p. e2200324.
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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.
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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.
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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. .
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Data integration: exploiting ratios of parameter estimates from a reduced external model.
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Source: Biometrika, 2023 Mar; 110(1), p. 119-134.
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Survival prediction models: an introduction to discrete-time modeling.
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MIAMI: Mutual Information-based Analysis of Multiplex Imaging data.
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Utility based approach in individualized optimal dose selection using machine learning methods.
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Utility based approach in individualized optimal dose selection using machine learning methods.
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The association between inflammatory biomarkers and statin use among patients with head and neck squamous cell carcinoma.
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Sufficient Dimension Reduction: An Information-Theoretic Viewpoint.
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Profiling Parkinson's disease cognitive phenotypes via resting-state magnetoencephalography.
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Multiple imputation with missing data indicators.
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A novel approach to understanding Parkinsonian cognitive decline using minimum spanning trees, edge cutting, and magnetoencephalography.
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Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm.
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Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.
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Accounting for not-at-random missingness through imputation stacking.
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Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.
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A meta-inference framework to integrate multiple external models into a current study.
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A copula-based approach for dynamic prediction of survival with a binary time-dependent covariate.
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Evaluation of predictive model performance of an existing model in the presence of missing data.
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Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring.
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Quantifying the incremental value of deep learning: Application to lung nodule detection.
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A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker.
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Accounting for established predictors with the multistep elastic net.
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Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information.
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Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability.
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Individualized survival prediction for patients with oropharyngeal cancer in the human papillomavirus era.
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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. .
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Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.
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Links between causal effects and causal association for surrogacy evaluation in a gaussian setting.
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Surrogacy assessment using principal stratification and a Gaussian copula model.
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Increasing efficiency for estimating treatment-biomarker interactions with historical data.
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A modified risk set approach to biomarker evaluation studies.
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Source: Statistics In Biosciences, 2016 Oct; 8(2), p. 395-406.
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Permutation Testing for Treatment-Covariate Interactions and Subgroup Identification.
Authors: Foster J.C. , Nan B. , Shen L. , Kaciroti N. , Taylor J.M. .
Source: Statistics In Biosciences, 2016 Jun; 8(1), p. 77-98.
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