Jeremy M G Taylor

University of Michigan, Ann Arbor, Michigan, United States

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Publications (175)714.53 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: It has been postulated that gastroesophageal reflux plays a role in the etiology of head and neck squamous cell carcinomas (HNSCC) and contributes to complications after surgery or during radiotherapy. Antacid medications are commonly used in patients with HNSCC for the management of acid reflux; however, their relationship with outcomes has not been well studied. Associations between histamine receptor-2 antagonists (H2RA) and proton pump inhibitors (PPI) use and treatment outcomes were determined in 596 patients with previously untreated HNSCC enrolled in our SPORE epidemiology program from 2003 to 2008 (median follow-up 55 months). Comprehensive clinical information was entered prospectively in our database. Risk strata were created on the basis of possible confounding prognostic variables (age, demographics, socioeconomics, tumor stage, primary site, smoking status, HPV16 status, and treatment modality); correlations within risk strata were analyzed in a multivariable model. Patients taking antacid medications had significantly better overall survival (OS; PPI alone: P < 0.001; H2RA alone, P = 0.0479; both PPI + H2RA, P = 0.0133). Using multivariable Cox models and adjusting for significant prognostic covariates, both PPIs and H2RAs used were significant prognostic factors for OS, but only H2RAs use for recurrence-free survival in HPV16-positive oropharyngeal patients. We found significant associations between the use of H2RAs and PPIs, alone or in combination, and various clinical characteristics. The findings in this large cohort study indicate that routine use of antacid medications may have significant therapeutic benefit in patients with HNSCC. The reasons for this association remain an active area of investigation and could lead to identification of new treatment and prevention approaches with agents that have minimal toxicities. Cancer Prev Res; 7(12); 1258-69. ©2014 AACR. ©2014 American Association for Cancer Research.
    Cancer Prevention Research 12/2014; 7(12):1258-69. · 5.27 Impact Factor
  • Jared C Foster, Jeremy M G Taylor, Niko Kaciroti, Bin Nan
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    ABSTRACT: We consider the use of randomized clinical trial (RCT) data to identify simple treatment regimes based on some subset of the covariate space, [Formula: see text]. The optimal subset, [Formula: see text], is selected by maximizing the expected outcome under a treat-if-in-[Formula: see text] regime, and is restricted to be a simple, as it is desirable that treatment decisions be made with only a limited amount of patient information required. We consider a two-stage procedure. In stage 1, non-parametric regression is used to estimate treatment effects for each subject, and in stage 2 these treatment effect estimates are used to systematically evaluate many subgroups of a simple, prespecified form to identify [Formula: see text]. The proposed methods were found to perform favorably compared with two existing methods in simulations, and were applied to prehypertension data from an RCT. © Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.
    Biostatistics 11/2014; · 2.24 Impact Factor
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    ABSTRACT: Because of the time and expense required to obtain clinical outcomes of interest, such as functional limitations or death, clinical trials often focus the effects of treatment on earlier and more easily obtained surrogate markers. Preliminary work to define surrogates focused on the fraction of a treatment effect "explained" by a marker in a regression model, but as notions of causality have been formalized in the statistical setting, formal definitions of high-quality surrogate markers have been developed in the causal inference framework, using either the "causal effect" or "causal association" settings. In the causal effect setting, high-quality surrogate markers have a large fraction of the total treatment effect explained by the effect of the treatment on the marker net of the treatment on the outcome. In the causal association setting, high-quality surrogate markers have large treatment effects on the outcome when there are large treatment effects on the marker, and small effects on the outcome when there are small effects on the marker. A particularly important feature of a surrogate marker is that the direction of a treatment effect be the same for both the marker and the outcome. Settings in which the marker and outcome are positively associated but the marker and outcome have beneficial and harmful or harmful and beneficial treatment effects, respectively, have been referred to as "surrogate paradoxes". If this outcome always occurs, it is not problematic; however, as correlations among the outcome, marker, and their treatment effects weaken, it may occur for some trials and not for others, leading to potentially incorrect conclusions, and real-life examples that shortened thousands of lives are unfortunately available. We propose measures for assessing the risk of the surrogate paradox using the meta-analytic causal association framework, which allows us to focus on the probability that a given treatment will yield treatment effect in different directions between the marker and the outcome, and to determine the size of a beneficial effect of the treatment on the marker required to minimize the risk of a harmful effect of the treatment on the outcome. We provide simulations and consider two applications.
    Biostatistics 09/2014; · 2.24 Impact Factor
  • Jeremy M. G. Taylor, Wenting Cheng, Jared C. Foster
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    ABSTRACT: A recent article (Zhang et al., 2012, Biometrics 168, 1010–1018) compares regression based and inverse probability based methods of estimating an optimal treatment regime and shows for a small number of covariates that inverse probability weighted methods are more robust to model misspecification than regression methods. We demonstrate that using models that fit the data better reduces the concern about non-robustness for the regression methods. We extend the simulation study of Zhang et al. (2012, Biometrics 168, 1010–1018), also considering the situation of a larger number of covariates, and show that incorporating random forests into both regression and inverse probability weighted based methods improves their properties.
    Biometrics 09/2014; · 1.52 Impact Factor
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    ABSTRACT: Selection of dose for cancer patients treated with radiation therapy (RT) must balance the increased efficacy with the increased toxicity associated with higher dose. Historically, a single dose has been selected for a population of patients (e.g., all stage III non-small cell lung cancer). However, the availability of new biologic markers for toxicity and efficacy allows the possibility of selecting a more personalized dose. We consider the use of statistical models for toxicity and efficacy as a function of RT dose and biomarkers to select an optimal dose for an individual patient, defined as the dose that maximizes the probability of efficacy minus the sum of weighted toxicity probabilities. This function can be shown to be equal to the expected value of the utility derived from a particular family of bivariate outcome utility matrices. We show that if dose is linearly related to the probability of toxicity and efficacy, then any marker that only acts additively with dose cannot improve efficacy, without also increasing toxicity. Using a dataset of lung cancer patients treated with RT, we illustrate this approach and compare it to non-marker-based dose selection. Because typical metrics used in evaluating new markers (e.g., area under the ROC curve) do not directly address the ability of a marker to improve efficacy at a fixed probability of toxicity, we utilize a simulation study to assess the effects of marker-based dose selection on toxicity and efficacy outcomes. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 08/2014; · 2.04 Impact Factor
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    ABSTRACT: With challenges in data harmonization and environmental heterogeneity across various data sources, meta-analysis of gene–environment interaction studies can often involve subtle statistical issues. In this paper, we study the effect of environmental covariate heterogeneity (within and between cohorts) on two approaches for fixed-effect meta-analysis: the standard inverse-variance weighted meta-analysis and a meta-regression approach. Akin to the results in Simmonds and Higgins (), we obtain analytic efficiency results for both methods under certain assumptions. The relative efficiency of the two methods depends on the ratio of within versus between cohort variability of the environmental covariate. We propose to use an adaptively weighted estimator (AWE), between meta-analysis and meta-regression, for the interaction parameter. The AWE retains full efficiency of the joint analysis using individual level data under certain natural assumptions. Lin and Zeng (, b) showed that a multivariate inverse-variance weighted estimator retains full efficiency as joint analysis using individual level data, if the estimates with full covariance matrices for all the common parameters are pooled across all studies. We show consistency of our work with Lin and Zeng (, b). Without sacrificing much efficiency, the AWE uses only univariate summary statistics from each study, and bypasses issues with sharing individual level data or full covariance matrices across studies. We compare the performance of the methods both analytically and numerically. The methods are illustrated through meta-analysis of interaction between Single Nucleotide Polymorphisms in FTO gene and body mass index on high-density lipoprotein cholesterol data from a set of eight studies of type 2 diabetes.
    Genetic Epidemiology 05/2014; · 2.95 Impact Factor
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    ABSTRACT: BACKGROUND Proinflammatory cytokine levels may be associated with cancer stage, recurrence, and survival. The objective of this study was to determine whether cytokine levels were associated with dietary patterns and fat-soluble micronutrients in patients with previously untreated head and neck squamous cell carcinoma (HNSCC).METHODS This was a cross-sectional study of 160 patients with newly diagnosed HNSCC who completed pretreatment food frequency questionnaires (FFQs) and health surveys. Dietary patterns were derived from FFQs using principal component analysis. Pretreatment serum levels of the proinflammatory cytokines interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and interferon gamma (IFN-γ) were measured using an enzyme-linked immunosorbent assay, and serum carotenoid and tocopherol levels were measured by high-performance liquid chromatography. Multivariable ordinal logistic regression models examined associations between cytokines and quartiles of reported and serum dietary variables.RESULTSThree dietary patterns emerged: whole foods, Western, and convenience foods. In multivariable analyses, higher whole foods pattern scores were significantly associated with lower levels of IL-6, TNF-α, and IFN-γ (P ≤ .001, P = .008, and P = .03, respectively). Significant inverse associations were reported between IL-6, TNF-α, and IFN-γ levels and quartiles of total reported carotenoid intake (P = .006, P = .04, and P = .04, respectively). There was an inverse association between IFN-γ levels and serum α-tocopherol levels (P = .03).CONCLUSIONS Consuming a pretreatment diet rich in vegetables, fruit, fish, poultry, and whole grains may be associated with lower proinflammatory cytokine levels in patients with HNSCC. Cancer 2014. © 2014 American Cancer Society.
    Cancer 05/2014; · 5.20 Impact Factor
  • Yongseok Park, John D Kalbfleisch, Jeremy M G Taylor
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    ABSTRACT: In this paper, we consider the problem of constructing confidence intervals (CIs) for G independent normal population means subject to linear ordering constraints. For this problem, CIs based on asymptotic distributions, likelihood ratio tests and bootstraps do not have good properties particularly when some of the population means are close to each other. We propose a new method based on defining intermediate random variables that are related to the original observations and using the CIs of the means of these intermediate random variables to restrict the original CIs from the separate groups. The coverage rates of the intervals are shown to exceed, but be close to, the nominal level for two groups, when the ratio of the variances is assumed known. Simulation studies show that the proposed CIs have coverage rates close to nominal levels with reduced average widths. Data on half-lives of an antibiotic are analyzed to illustrate the method.
    Statistica Sinica 01/2014; 24(1):429-445. · 1.23 Impact Factor
  • Anna S C Conlon, Jeremy M G Taylor, Michael R Elliott
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    ABSTRACT: In clinical trials, a surrogate outcome variable (S) can be measured before the outcome of interest (T) and may provide early information regarding the treatment (Z) effect on T. Using the principal surrogacy framework introduced by Frangakis and Rubin (2002. Principal stratification in causal inference. Biometrics 58, 21-29), we consider an approach that has a causal interpretation and develop a Bayesian estimation strategy for surrogate validation when the joint distribution of potential surrogate and outcome measures is multivariate normal. From the joint conditional distribution of the potential outcomes of T, given the potential outcomes of S, we propose surrogacy validation measures from this model. As the model is not fully identifiable from the data, we propose some reasonable prior distributions and assumptions that can be placed on weakly identified parameters to aid in estimation. We explore the relationship between our surrogacy measures and the surrogacy measures proposed by Prentice (1989. Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine 8, 431-440). The method is applied to data from a macular degeneration study and an ovarian cancer study.
    Biostatistics 11/2013; · 2.24 Impact Factor
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    ABSTRACT: The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.
    Nature 10/2013; 502(7471):317-20. · 42.35 Impact Factor
  • Lili Zhao, Dai Feng, Emily L Bellile, Jeremy M G Taylor
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    ABSTRACT: In this paper, we develop a Bayesian approach to estimate a Cox proportional hazards model that allows a threshold in the regression coefficient, when some fraction of subjects are not susceptible to the event of interest. A data augmentation scheme with latent binary cure indicators is adopted to simplify the Markov chain Monte Carlo implementation. Given the binary cure indicators, the Cox cure model reduces to a standard Cox model and a logistic regression model. Furthermore, the threshold detection problem reverts to a threshold problem in a regular Cox model. The baseline cumulative hazard for the Cox model is formulated non-parametrically using counting processes with a gamma process prior. Simulation studies demonstrate that the method provides accurate point and interval estimates. Application to a data set of oropharynx cancer patients suggests a significant threshold in age at diagnosis such that the effect of gender on disease-specific survival changes after the threshold. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 09/2013; · 2.04 Impact Factor
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    ABSTRACT: Purpose/Objectives: To determine whether improved monitoring through close follow-up with a nurse practitioner (NP) could enhance treatment compliance and decrease frequency of hospitalizations.Design: Retrospective chart review.Setting: An academic National Cancer Institute-designated comprehensive cancer center.Sample: 151 patients aged 45-65 years diagnosed with stage III or IV oropharyngeal cancer.Methods: Patients were nonrandomized to one of two groups: a prechemotherapy clinic group and a weekly NP-led clinic group. After examination of descriptive statistics, multiple linear and logistic regressions were used to compare groups across patient outcomes.Main Research Variables: Hospitalization, chemotherapy dose deviations, and chemotherapy treatment completion.Findings: The average number of visits during traditional treatment was three and, after initiation of the NP-led clinic, the number was six. The hospitalization rate was 28% in the traditional clinic group compared to 12% in the NP-led group. The rate of chemotherapy dose deviations was 48% in the traditional clinic group compared to 6% in the NP-led clinic group. Forty-six percent of patients in the traditional clinic group received the full seven scheduled doses of chemotherapy compared to 90% of patients seen in the NP-led clinic group.Conclusions: A weekly NP-led symptom management clinic reduces rates of hospitalization and chemotherapy dose deviations and increases chemotherapy completion in patients receiving intensive chemoradiotherapy for oropharyngeal cancer.Implications for Nursing: Patients receiving chemoradiotherapy benefit from close monitoring for toxicities by NPs to successfully complete their treatment and avoid hospitalization.Knowledge Translation: Early interventions to manage toxicities in patients with head and neck cancer can improve outcomes. NPs are in a key position to manage these toxicities and, when symptoms are controlled, costs are reduced.
    Oncology Nursing Forum 09/2013; · 1.91 Impact Factor
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    ABSTRACT: For patients who were previously treated for prostate cancer, salvage hormone therapy is frequently given when the longitudinal marker prostate-specific antigen begins to rise during follow-up. Because the treatment is given by indication, estimating the effect of the hormone therapy is challenging. In a previous paper we described two methods for estimating the treatment effect, called two-stage and sequential stratification. The two-stage method involved modeling the longitudinal and survival data. The sequential stratification method involves contrasts within matched sets of people, where each matched set includes people who did and did not receive hormone therapy. In this paper, we evaluate the properties of these two methods and compare and contrast them with the marginal structural model methodology. The marginal structural model methodology involves a weighted survival analysis, where the weights are derived from models for the time of hormone therapy. We highlight the different conditional and marginal interpretations of the quantities being estimated by the three methods. Using simulations that mimic the prostate cancer setting, we evaluate bias, efficiency, and accuracy of estimated standard errors and robustness to modeling assumptions. The results show differences between the methods in terms of the quantities being estimated and in efficiency. We also demonstrate how the results of a randomized trial of salvage hormone therapy are strongly influenced by the design of the study and discuss how the findings from using the three methodologies can be used to infer the results of a trial. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 07/2013; 33(2). · 2.04 Impact Factor
  • Jared C Foster, Jeremy M G Taylor, Bin Nan
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    ABSTRACT: We consider the problem of variable selection for monotone single-index models. A single-index model assumes that the expectation of the outcome is an unknown function of a linear combination of covariates. Assuming monotonicity of the unknown function is often reasonable and allows for more straightforward inference. We present an adaptive LASSO penalized least squares approach to estimating the index parameter and the unknown function in these models for continuous outcome. Monotone function estimates are achieved using the pooled adjacent violators algorithm, followed by kernel regression. In the iterative estimation process, a linear approximation to the unknown function is used, therefore reducing the situation to that of linear regression and allowing for the use of standard LASSO algorithms, such as coordinate descent. Results of a simulation study indicate that the proposed methods perform well under a variety of circumstances and that an assumption of monotonicity, when appropriate, noticeably improves performance. The proposed methods are applied to data from a randomized clinical trial for the treatment of a critical illness in the intensive care unit. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 05/2013; · 2.04 Impact Factor
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    ABSTRACT: Summary Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this article, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.
    Biometrics 02/2013; · 1.52 Impact Factor
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    ABSTRACT: Head and neck squamous cell carcinoma (HNSCC) is the eighth most commonly diagnosed cancer in the United States. The risk of developing HNSCC increases with exposure to tobacco, alcohol and infection with human papilloma virus (HPV). HPV-associated HNSCCs have a distinct risk profile and improved prognosis compared to cancers associated with tobacco and alcohol exposure. Epigenetic changes are an important mechanism in carcinogenic progression, but how these changes differ between viral- and chemical-induced cancers remains unknown. CpG methylation at 1505 CpG sites across 807 genes in 68 well-annotated HNSCC tumor samples from the University of Michigan Head and Neck SPORE patient population were quantified using the Illumina Goldengate Methylation Cancer Panel. Unsupervised hierarchical clustering based on methylation identified 6 distinct tumor clusters, which significantly differed by age, HPV status, and three year survival. Weighted linear modeling was used to identify differentially methylated genes based on epidemiological characteristics. Consistent with previous in vitro findings by our group, methylation of sites in the CCNA1 promoter was found to be higher in HPV(+) tumors, which was validated in an additional sample set of 128 tumors. After adjusting for cancer site, stage, age, gender, alcohol consumption, and smoking status, HPV status was found to be a significant predictor for DNA methylation at an additional 11 genes, including CASP8 and SYBL1. These findings provide insight into the epigenetic regulation of viral vs. chemical carcinogenesis and could provide novel targets for development of individualized therapeutic and prevention regimens based on environmental exposures.
    PLoS ONE 01/2013; 8(1):e54742. · 3.53 Impact Factor
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    ABSTRACT: BACKGROUND: Health behaviors have been shown to be associated with recurrence risk and survival rates in cancer patients and are also associated with Interleukin-6 levels, but few epidemiologic studies have investigated the relationship of health behaviors and Interleukin-6 among cancer populations. The purpose of the study is to look at the relationship between five health behaviors: smoking, alcohol problems, body mass index (a marker of nutritional status), physical activity, and sleep and pretreatment Interleukin-6 levels in persons with head and neck cancer. METHODS: Patients (N=409) were recruited in otolaryngology clinic waiting rooms and invited to complete written surveys. A medical record audit was also conducted. Descriptive statistics and multivariate analyses were conducted to determine which health behaviors were associated with higher Interleukin-6 levels controlling for demographic and clinical variables among newly diagnosed head and neck cancer patients. RESULTS: While smoking, alcohol problems, body mass index, physical activity, and sleep were associated with Interleukin-6 levels in bivariate analysis, only smoking (current and former) and decreased sleep were independent predictors of higher Interleukin-6 levels in multivariate regression analysis. Covariates associated with higher Interleukin-6 levels were age and higher tumor stage, while comorbidities were marginally significant. CONCLUSION: Health behaviors, particularly smoking and sleep disturbances, are associated with higher Interleukin-6 levels among head and neck cancer patients. Impact: Treating health behavior problems, especially smoking and sleep disturbances, may be beneficial to decreasing Interleukin-6 levels which could have a beneficial effect on overall cancer treatment outcomes.
    Cancer Epidemiology Biomarkers & Prevention 01/2013; · 4.56 Impact Factor
  • Biometrics 01/2013; 69(1). · 1.52 Impact Factor
  • Philip S Boonstra, Jeremy M G Taylor, Bhramar Mukherjee
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    ABSTRACT: With advancement in genomic technologies, it is common that two high-dimensional datasets are available, both measuring the same underlying biological phenomenon with different techniques. We consider predicting a continuous outcome Y using X, a set of p markers which is the best available measure of the underlying biological process. This same biological process may also be measured by W, coming from a prior technology but correlated with X. On a moderately sized sample, we have (Y,X,W), and on a larger sample we have (Y,W). We utilize the data on W to boost the prediction of Y by X. When p is large and the subsample containing X is small, this is a p>n situation. When p is small, this is akin to the classical measurement error problem; however, ours is not the typical goal of calibrating W for use in future studies. We propose to shrink the regression coefficients β of Y on X toward different targets that use information derived from W in the larger dataset. We compare these proposals with the classical ridge regression of Y on X, which does not use W. We also unify all of these methods as targeted ridge estimators. Finally, we propose a hybrid estimator which is a linear combination of multiple estimators of β. With an optimal choice of weights, the hybrid estimator balances efficiency and robustness in a data-adaptive way to theoretically yield a smaller prediction error than any of its constituents. The methods, including a fully Bayesian alternative, are evaluated via simulation studies. We also apply them to a gene-expression dataset. mRNA expression measured via quantitative real-time polymerase chain reaction is used to predict survival time in lung cancer patients, with auxiliary information from microarray technology available on a larger sample.
    Biostatistics 10/2012; · 2.24 Impact Factor
  • Jin Zhang, Thomas M Braun, Jeremy M G Taylor
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    ABSTRACT: The use of the continual reassessment method (CRM) and other model-based approaches to design Phase I clinical trials has increased owing to the ability of the CRM to identify the maximum tolerated dose better than the 3 + 3 method. However, the CRM can be sensitive to the variance selected for the prior distribution of the model parameter, especially when a small number of patients are enrolled. Although methods have emerged to adaptively select skeletons and to calibrate the prior variance only at the beginning of a trial, there has not been any approach developed to adaptively calibrate the prior variance throughout a trial. We propose three systematic approaches to adaptively calibrate the prior variance during a trial and compare them via simulation with methods proposed to calibrate the variance at the beginning of a trial. Copyright © 2012 John Wiley & Sons, Ltd.
    Statistics in Medicine 09/2012; · 2.04 Impact Factor

Publication Stats

6k Citations
714.53 Total Impact Points


  • 2000–2014
    • University of Michigan
      • • Department of Biostatistics
      • • Department of Otolaryngology - Head and Neck Surgery
      • • Department of Internal Medicine
      • • Department of Surgery
      Ann Arbor, Michigan, United States
  • 2010–2012
    • Pennsylvania State University
      • Department of Statistics
      University Park, MD, United States
  • 2011
    • Duke University
      • Department of Statistical Science
      Durham, NC, United States
  • 2006–2010
    • The University of Arizona
      • Division of Epidemiology and Biostatistics
      Tucson, AZ, United States
    • Johnson & Johnson
      New Brunswick, New Jersey, United States
  • 2009
    • Mayo Foundation for Medical Education and Research
      • Division of Biomedical Statistics and Informatics
      Scottsdale, AZ, United States
    • Unité Inserm U1077
      Caen, Lower Normandy, France
  • 2000–2009
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States
  • 2008
    • Memorial Sloan-Kettering Cancer Center
      • Epidemiology & Biostatistics Group
      New York City, NY, United States
  • 1991–2008
    • County of Los Angeles Public Health
      Los Angeles, California, United States
  • 2007
    • Université Victor Segalen Bordeaux 2
      Burdeos, Aquitaine, France
    • University of Melbourne
      Melbourne, Victoria, Australia
  • 1987–2007
    • University of California, Los Angeles
      • • Department of Biostatistics
      • • Department of Radiation Oncology
      Los Angeles, California, United States
  • 1986
    • University of Southern California
      • Division of Biostatistics
      Los Angeles, California, United States