Edward John Bedrick
- Professor, Public Health
- Professor, Statistics-GIDP
- Associate Director, Statistical Consulting
- Professor, BIO5 Institute
- Ph.D. Statistics
- University of Minnesota, Minnesota, United States
- Univ of Arizona (2016 - Ongoing)
- University of Colorado Anschutz Medical Campus (2014 - 2015)
- University of New Mexico, Albuquerque, New Mexico (1982 - 2013)
Bayesian MethodsGeneral Statistical Methodology
Linear ModelsStatistical TheoryBayesian Methods
Theory Of Linear ModelsBIOS 687 (Fall 2020)
Theory Of Linear ModelsEPID 687 (Fall 2020)
Theory Of Linear ModelsSTAT 687 (Fall 2020)
Master's ReportBIOS 909 (Summer I 2020)
Biostatistics SeminarBIOS 696S (Spring 2020)
Independent StudyBIOS 699 (Spring 2020)
Master's ReportBIOS 909 (Spring 2020)
Bayesian Stat Thry+AppliBIOS 574B (Fall 2019)
Bayesian Stat Thry+AppliECON 574B (Fall 2019)
Bayesian Stat Thry+AppliSTAT 574B (Fall 2019)
Biostatistics SeminarBIOS 696S (Fall 2019)
Biostatistics SeminarBIOS 696S (Spring 2019)
Biostatistics SeminarBIOS 696S (Fall 2018)
Theory Of Linear ModelsBIOS 687 (Fall 2018)
Theory Of Linear ModelsSTAT 687 (Fall 2018)
Biostatistics SeminarBIOS 696S (Spring 2018)
Master's ReportBIOS 909 (Spring 2018)
Bayesian Stat Thry+AppliECON 574B (Fall 2017)
Bayesian Stat Thry+AppliSTAT 574B (Fall 2017)
Biostatistics SeminarBIOS 696S (Fall 2017)
Master's ReportBIOS 909 (Fall 2017)
Master's ReportCPH 909 (Summer I 2017)
Independent StudyCPH 699 (Spring 2017)
Theory Of Linear ModelsCPH 687 (Fall 2016)
Theory Of Linear ModelsSTAT 687 (Fall 2016)
- Ahmed, R. A., Bedrick, E. J., Ng, V., Plitt, J., Cahir, T., Hughes, P. G., & Hughes, K. (2019). Advanced closed-loop communication training: the blindfolded resuscitation. BMJ Simulation and Technology Enhanced Learning. doi:10.1136/bmjstel-2019-000498
- Bedrick, E. J. (2019). Delays in Coccidioidomycosis Diagnosis and Associated Healthcare Utilization, Tucson, Arizona, USA. Emerg Infect Disv.
- Bedrick, E. J. (2019). PrEP Knowledge and Attitudes Among Adults Attending Public Health Clinics in Southern Arizona. J Community Health.
- Huang, S., Hu, C., Bell, M. L., Billheimer, D. D., Guerra, S., Roe, D., Vasquez, M., & Bedrick, E. J. (2018). Regularized continuous-time Markov model via elastic net. Biometrics.
- Bedrick, E. J. (2018). Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities. BMC Med Genomics.
- Dodd, A. B., Ling, J. M., Bedrick, E. J., Meier, T. B., & Mayer, A. R. (2018). Spatial distribution bias in subject-specific abnormalities analyses. Brain imaging and behavior, 12(6), 1828-1834.More infoThe neuroimaging community has seen a renewed interest in algorithms that provide a location-independent summary of subject-specific abnormalities (SSA) to assess individual lesion load. More recently, these methods have been extended to assess whether multiple individuals within the same cohort exhibit extrema in the same spatial location (e.g., voxel or region of interest). However, the statistical validity of this approach has not been rigorously established. The current study evaluated the potential for a spatial bias in the distribution of SSA using several common z-transformation algorithms (leave-one-out [LOO]; independent sample [IDS]; Enhanced Z-Score Microstructural Assessment of Pathology [EZ-MAP]; distribution-corrected z-scores [DisCo-Z]) using both simulated data and DTI data from 50 healthy controls. Results indicated that methods which z-transformed data based on statistical moments from a reference group (LOO, DisCo-Z) led to bias in the spatial location of extrema for the comparison group. In contrast, methods that z-transformed data using an independent third group (EZ-MAP, IDS) resulted in no spatial bias. Importantly, none of the methods exhibited bias when results were summed across all individual elements. The spatial bias is primarily driven by sampling error, in which differences in the mean and standard deviation of the untransformed data have a higher probability of producing extrema in the same spatial location for the comparison but not reference group. In conclusion, evaluating SSA overlap within cohorts should be either be avoided in deference to established group-wise comparisons or performed only when data is available from an independent third group.
- Hockett, C. W., Bedrick, E. J., Zeitler, P., Crume, T. L., Daniels, S., & Dabelea, D. (2018). Exposure to Diabetes in Utero Is Associated with Earlier Pubertal Timing and Faster Pubertal Growth in the Offspring: The EPOCH Study. The Journal of pediatrics.More infoTo examine the associations of in utero exposure to maternal diabetes with surrogate measures of offspring pubertal timing (age at peak height velocity [APHV]) and speed of pubertal growth (peak height velocity [PHV]).
- Huang, S., Hu, C., Bell, M. L., Billheimer, D., Guerra, S., Roe, D., Vasquez, M. M., & Bedrick, E. J. (2018). Regularized continuous-time Markov Model via elastic net. Biometrics, 74(3), 1045-1054.More infoContinuous-time Markov models are commonly used to analyze longitudinal transitions between multiple disease states in panel data, where participants' disease states are only observed at multiple time points, and the exact state paths between observations are unknown. However, when covariate effects are incorporated and allowed to vary for different transitions, the number of potential parameters to estimate can become large even when the number of covariates is moderate, and traditional maximum likelihood estimation and subset model selection procedures can easily become unstable due to overfitting. We propose a novel regularized continuous-time Markov model with the elastic net penalty, which is capable of simultaneous variable selection and estimation for large number of parameters. We derive an efficient coordinate descent algorithm to solve the penalized optimization problem, which is fully automatic and data driven. We further consider an extension where one of the states is death, and time of death is exactly known but the state path leading to death is unknown. The proposed method is extensively evaluated in a simulation study, and demonstrated in an application to real-world data on airflow limitation state transitions.
- Li, H., Fan, J., Vitali, F., Berghout, J., Aberasturi, D., Li, J., Wilson, L., Chiu, W., Pumarejo, M., Han, J., Kenost, C., Koripella, P. C., Pouladi, N., Billheimer, D., Bedrick, E. J., & Lussier, Y. A. (2018). Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities. BMC medical genomics, 11(Suppl 6), 112.More infoForty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets.
- Whitaker, M. E., Nair, V., Sinari, S., Dherange, P. A., Natarajan, B., Trutter, L., Brittain, E. L., Hemnes, A. R., Austin, E. D., Patel, K., Black, S. M., Garcia, J. G., Yuan Md PhD, J. X., Vanderpool, R. R., Rischard, F., Makino, A., Bedrick, E. J., & Desai, A. A. (2018). Diabetes Mellitus Associates with Increased Right Ventricular Afterload and Remodeling in Pulmonary Arterial Hypertension. The American journal of medicine, 131(6), 702.e7-702.e13.More infoDiabetes mellitus is associated with left ventricular hypertrophy and dysfunction. Parallel studies have also reported associations between diabetes mellitus and right ventricular dysfunction and reduced survival in patients with pulmonary arterial hypertension. However, the impact of diabetes mellitus on the pulmonary vasculature has not been well characterized. We hypothesized that diabetes mellitus and hyperglycemia could specifically influence right ventricular afterload and remodeling in patients with Group I pulmonary arterial hypertension, providing a link to their known susceptibility to right ventricular dysfunction.
- Bedrick, E. J. (2017). An Evaluation of Z-Transform Algorithms for Identifying Subject-Specific Abnormalities in Neuroimaging Data. Human Brain Mapping.
- Bedrick, E. J. (2017). Childhood hematologic cancer and residential proximity to oil and gas development. PLOS ONE.
- Patt, Y. Z., Murad, W., Fekrazad, M. H., Baron, A. D., Bansal, P., Boumber, Y., Steinberg, K., Lee, S. J., Bedrick, E., Du, R., & Lee, F. C. (2017). INST OX-05-024: first line gemcitabine, oxaliplatin, and erlotinib for primary hepatocellular carcinoma and bile duct cancers: a multicenter Phase II trial. Cancer medicine, 6(9), 2042-2051.More infoHepatocellular Carcinoma (HCC) incidence is increasing in the USA. Gemcitabine (G) and oxaliplatin (O) are active in HCC and biliary duct cancer (BDC). Erlotinib (E) is an EGFR tyrosine kinase inhibitor (TKI) with known activity against both. We sought to evaluate the efficacy of the combination G+O+E. Patients with either of the two diagnosis were treated in a phase II trial. Simons 2 stage design was used. A disease-control rate (DCR), complete response (CR) + partial response (PR)+ stable disease (SD) at 24 weeks of ≤20% and >40% (P0 and P1 of 0.2 and 0.4, respectively) were set as undesirable (null) and desirable results. 26 HCC and 7 BDC patients were accrued. In HCC, 1 PR, 10 SD, and 9 PDs were seen. DCR in HCC was 42%. Among seven (7) patients with BDC, one patient was not evaluable; one achieved a long lasting PR, and five patients had SD and DCR was 86%. Median overall survival (OS) times and progression-free survivals (PFS) were 196 and 149 days in HCC and 238 days and not reached in BDC. PFS at 26 weeks in HCC was 41% and at 21 weeks in BDC was 60%. Grade 3 toxicities in >5% of patients were fatigue (12.9%), neutropenia (9.6%), thrombocytopenia (9.6%), and diarrhea (6.4%). G+O+E exceeded both preset P0a and P1 of the primary objective with a PFS of 41% at 26 weeks for HCC and preliminary BDC data may warrant further investigations.
- Amini, A., Yeh, N., Jones, B. L., Bedrick, E., Vinogradskiy, Y., Rusthoven, C. G., Amini, A., Purcell, W. T., Karam, S. D., Kavanagh, B. D., Guntupalli, S. R., & Fisher, C. M. (2016). Perioperative Mortality in Nonelderly Adult Patients With Cancer: A Population-based Study Evaluating Health Care Disparities in the United States According to Insurance Status. American journal of clinical oncology.More infoThe purpose of this study was to evaluate whether insurance status predicts for perioperative mortality (death within 30 d of cancer-directed surgery) for the 20 most common surgically treated cancers.
- Bedrick, E. J., & Hund, L. (2016). An approach for quantifying small effects in regression models. Statistical methods in medical research.More infoWe develop a novel approach for quantifying small effects in regression models. Our method is based on variation in the mean function, in contrast to methods that focus on regression coefficients. Our idea applies in diverse settings such as testing for a negligible trend and quantifying differences in regression functions across strata. Straightforward Bayesian methods are proposed for inference. Four examples are used to illustrate the ideas.
- White, A., Cronquist, A., Bedrick, E. J., & Scallan, E. (2016). Food Source Prediction of Shiga Toxin-Producing Escherichia coli Outbreaks Using Demographic and Outbreak Characteristics, United States, 1998-2014. Foodborne pathogens and disease, 13(10), 527-534.More infoFoodborne illness is a continuing public health problem in the United States. Although outbreak-associated illnesses represent a fraction of all foodborne illnesses, foodborne outbreak investigations provide critical information on the pathogens, foods, and food-pathogen pairs causing illness. Therefore, identification of a food source in an outbreak investigation is key to impacting food safety.
- Bedrick, E. J. (2019, April). Bayes for Personalized Medicine. Midas Conference Vina Del Mar Chile. Oracle, AZ.
- Bedrick, E. J. (2019, April). Double Double Toil and Trouble: Biases, Fallacies and Paradoxes that Will Turn Your Statistical Analysis to Rubble. Division Infectious Diseases Grand Rounds. UofA: Dept of Medicine - ID Division.
- Bedrick, E. J. (2019, June). Data Reduction Prior to Inference. Research Seminar. Mayo Clinic Minnesota.
- Bedrick, E. J. (2019, November). Data Reduction Prior to Inference. Research Seminar. UNAM Juriquilla Mexico.
- Bedrick, E. J. (2018, April). An Introduction to Bayesian Statistics in the Health Sciences. BIO5 Conference. UofA: BIO5.
- Bedrick, E. J. (2018, April). Bayes for N=1 Experiments. TRIPODs Biosphere Conference. Oracle, AZ.
- Bedrick, E. J. (2018, March). Bayes for N=1 Experiments. Tripods RG7 Working Group Seminar. UofA.
- Bedrick, E. J. (2018, March). Data Reduction Prior to Inference. Dept Epi and Biostat Seminar. Tucson.
- Bedrick, E. J. (2018, March). Intro to Bayesian Statistics. Seminar UofA College of Business. Tucson.
- Bedrick, E. J. (2018, May). Double Double Toil and Trouble: Biases, Fallacies and Paradoxes that Will Turn Your Statistical Analysis to Rubble. Dept Medicine Grand Rounds. UofA: Dept of Medicine(Arthritis).
- Bedrick, E. J. (2018, November). Double, Double, Toil and Trouble: etc (see earlier entry). OB/GYN Grand Rounds. UofA.
- Bedrick, E. J. (2018, October). Data Reduction Prior to Inference. Department Seminar Univ Nevada. Dept of Math Sciences: University of Nevada Reno.
- Roach, M., Collier, K., Hill, C., Briggs, G., Packard, S., Garcia, R., Dylan, K., Bedrick, E. J., & Griffin, S. (2018, Jan). Enhanced Surveillance of Heat-Related Illness in Pinal County. ISDS Annual Conference Proceedings 2018.
- Ahmed, R. A., Bedrick, E. J., Ng, V., Plitt, J., Cahir, T., Hughes, P. G., & Hughes, K. (2020, January). Crisis resource management training: the blindfold code exercise. International Meeting on Simulation in Healthcare. San Diego, CA.
- Ramadan, F., Cupini, C., Bedrick, E. J., Burstyn, I., & Reiss, B. (2018, April). Characterization of PurpleAir Monitors: A Method-Comparison Study. SESHA/SIA International High Technology ESH Symposium and Exhibition. Scottsdale, AZ, USA.
- Nair, V., Whitaker, M. E., Sinari, S., Natarajan, B., Trutter, L., Dherange, P., Brittain, E. L., Hemnes, A. R., Austin, E. D., Patel, K., Kadakia, A., Rischard, F., Garcia, J. G., Yuan, J., Makino, A., Black, S., Bedrick, E. J., & Desai, A. (2017, May). Effects of diabetes mellitus on pulmonary vascular stiffness and right ventricular remodeling. American Thoracic Society Conference. Washington, D.C.: American Thoracic Society.
- Whitaker, M. E., Nair, V., Sinari, S., Natarajan, B., Trutter, L., Aystin, E. D., Hemnes, A. R., Brittain, E. L., Dherange, P., Patel, K., Rischard, F., Yuan, J., Makino, A., Bedrick, E. J., & Desai, A. (2016, November). Effects of insulin resistance on pulmonary vascular stiffness and right ventricular remodeling.. American College of Physicians - Arizona Chapter Scientific Meeting. Phoenix, AZ: American College of Physicians - Arizona Chapter Scientific Meeting.