Basic Statistics: For medical and social science students

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The instructor will facilitate a learning and skill-building environment, drawing on personal experiences and the expertise of others in the field. Case studies will illustrate the basis of Public Health jurisprudence at the national level.

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This course describes the major components of the U. Topics include primary, secondary and tertiary levels of care; quality assessment; epidemiology; and politics of healthcare. Intermediate Biostatistics builds upon the material learned in Introduction to Biostatistics. Specifically, the course will focus on single-outcome, multiple-predictor methods: multiple linear regression for continuous outcomes, logistic regression for binary outcomes, and the Cox proportional hazards model for time-to-event outcomes.

This course covers epidemiologic methods used in observational epidemiologic studies including the design, conduct and interpretation of observational studies in human populations with a focus on analytic cross-sectional, case-control studies and cohort studies. Key issues related to statistical approaches, validity of measures of exposure and disease and sources of potential errors in interpreting epidemiologic studies will be addressed.

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This course is an introduction to GIS and the collection, maintenance and analysis of spatial data for health. It combines practical ArcGIS skills with study of the theory and applications of spatial data and spatial analysis in general and specifically as it relates to population health. Students will learn to design an evidence-based and culturally appropriate global health research project or program. PH is an independent study research seminar based on individual student meetings with the instructor and other faculty mentors. The course focuses on completion of a health services research or health policy paper and oral presentation, often in conjunction with MPH program Culminating Experience requirements.

Papers require health services research methodological and study design skills or the conceptual and analytical skills needed for public health history or health policy analyses. Learning objectives include applying health services research methods to a public health, clinical policy or public policy problem or debate, describing factors underlying geographic or provider variations in medical practice or health outcomes, using quality measurement, quality improvement, patient safety or epidemiologic research techniques, conducting risk adjustment for evaluation of medical or behavioral health interventions, and addressing critical issues in social determinants of health or social epidemiology.

Enrollment requires prior consent of the instructor. This course focuses on methodological issues regarding the design, implementation, analysis and interpretation of surveys and questionnaires in Public Health research. Various types of self-report data will be discussed, including knowledge, attitudes, behaviors and patient-reported outcomes.

Issues will include formatting and layout, wording of items and response scales, multilingual translations, sampling, timing of assessments, interviewer training, participant recruitment, data analysis and respondent and staff burden. This course focuses on qualitative research design, sampling, data management, analysis and report writing. Methods covered include cognitive interviewing for survey construction, individual and group interview methodologies, participant observation, writing and using field notes, cognitive tasks such as decision modeling, domain analysis and the use of mapping techniques in qualitative research.

Data analysis instruction includes thematic analyses and code development, consensus and network analyses and an overview of qualitative data management programs. This case-based course provides student with knowledge of the issues surrounding the ethical conduct of research including making ethical choices in the face of conflicts, and gaining a familiarity with the regulations governing human subjects research.

This course covers advanced decision-analytic methods useful in medical decision modeling.

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Included are the probabilistic theory of hazard rates and modeling of age-dependent mortality, Markov modeling, stochastic tree modeling, techniques for multi-way sensitivity analysis such as probabilistic sensitivity analysis and information-value analysis and software of stochastic tree modeling. Medical decision-analytic literature is reviewed and theoretical underpinnings of models are explored. A project using decision analysis software is required.

This course provides an introduction to the health needs of women and children and the services designed to meet these needs. The course provides students with a comprehensive knowledge base with respect to federal funding and other public programs addressing MCH. The course is designed to provide professionals with the skills for collecting, analyzing and communicating information on public health policy issues using approaches that would be useful in the policymaking arena.

Students will learn what policy is; who the policymakers are in public health; who the actors are that are affected by Public Health policy; and the major influences in determining what policy gets implemented, including the science underlying policy proposals. Advanced Global Public Health will provide an in depth exploration of the current approaches to eradicating long-term social and economic inequalities in health outcomes around the world. We will begin with a review of the current state of global health, highlighting the areas of major gains since , discourse on global health governance, and current trends and emerging health challenges e.

We will then directly examine the diverse strategies that have been used to improve health outcomes in low- and middle-income countries. These strategies range from biomedical interventions e. Drawing on detailed case studies, we will explore a the nature and structure of global health interventions, b the creation of successful partnerships for sustaining health outcomes, and c the importance of data collection and analysis for monitoring the effectiveness of program interventions.

Students must identify a faculty preceptor, develop a syllabus with the preceptor and receive the approval of MPH Curriculum Committee. Diseases and conditions will be examined in order to discern the epidemiology of the disease and how cultural influences can impact both the rise of diseases as public health issues and their subsequent decline in incidence with a view toward prevention of future outbreaks.

The course will cover selected topics in cardiovascular disease with critical analysis of the current epidemiologic literature. Students will have the opportunity to study methodological issues, contemporary findings and recommendations for future research. Click on the link above for instructions and approval forms.

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Student presents a seminar and submits a paper on the project. This course introduces epidemiology and its uses for population health research. Concepts include measures of disease occurrence, common sources and types of data, important study designs, sources of error in epidemiologic studies and epidemiologic methods. This course introduces principles of biostatistics and applications of statistical methods in health and medical research. Concepts include descriptive statistics, basic probability, probability distributions, estimation, hypothesis testing, correlation and simple linear regression.

This course introduces probability as the theoretical framework underlying statistical methods. Concepts include random variables, discrete and continuous probability distributions, multivariate distributions, and random variable transformations. This course provides a thorough working introduction to the statistical programming language SAS.

Research methodology for medical students pdf

Concepts focus on practical issues relating to data management, statistical data processing and SAS programming. This course provides a thorough working introduction to the statistical programming language R. Concepts focus on practical issues including: installing and configuring the RStudio development environment; loading and managing data in R; accessing R packages; writing R functions; writing R scripts; debugging and profiling R scripts; organizing and commenting R code; and developing dynamic analysis reports using R MarkDown.

Topics in biostatistical data analysis will provide relevant working examples. The purpose of this course is two-fold: 1 To elaborate on concepts first introduced in introductory epi and demonstrate to students how these concepts are frequently applied in biomedical literature; and 2 To provide students an overview of the physiology, pathophysiology, and epidemiology of prevalent diseases in the United States. This course provides an intermediate-level treatment of linear and logistic regression models, including model estimation and inference, model building and diagnostics, and interpretation of results in the context of epidemiologic and clinical studies.

The focus is on practical application of regression models for data analysis. The course uses R statistical software for data analysis. This course introduces statistical inference concepts and applied statistical techniques for data analysis in a mathematical framework. Concepts include point and interval estimation, maximum likelihood, large sample theory, hypothesis testing, bootstrap methods, analysis of variance, linear regression, analysis of categorical data and Bayesian methods. This course introduces students to the principles and practice of good health measurement, with a central focus on the methodology of developing of a patient-reported outcomes PRO instrument and related assessment tools.

This course covers practical aspects of conducting a population-based research study. Concepts include determining a study budget, setting a timeline, identifying study team members, setting a strategy for recruitment and retention, developing a data collection protocol and monitoring data collection to ensure quality control and quality assurance. This course builds on material learned in previous Biostatistics and Epidemiology courses. Concepts are applied to the design, implementation, analysis and interpretation of observational epidemiologic studies cross-sectional, case-control and cohort.

Students enrolled in an MPH degree program must have the consent of the instructor. This course covers modern approaches to the analysis of correlated response data arising from longitudinal studies commonly encountered in medical research and clinical trials. Concepts include marginal and mixed-effects regression models for continuous and discrete outcomes measured repeatedly over time, model building techniques, robust inference procedures and problems associated with missing data.

All modeling and numerical analysis will be done using SAS. This courses provides an introduction to the fundamental concepts and methods developed for analysis of survival data for which incompleteness, including censoring, is a primary feature. Classic non-parametric estimation approaches will be discussed, as will semi-parametric and parametric hazard regression modeling techniques that allow incorporation of covariates.

Analysis examples using both R and SAS will be discussed. This course provides students with a basic knowledge of the potential implications of missing data on their data analyses as well as potential solutions. A major focus of the course is multiple imputation including discussions of the general framework, different models and algorithms, and the basic theory. Statistical programming is performed in R. This course equips students with key principles and practical skills to analyze genetic data.

Topics range from linkage analysis using pedigree data to machine learning techniques using next-generation sequencing data. This course prepares students for collaboration and communication with scientists of various disciplines, emphasizing analytical tools, verbal and written communication skills and presentation skills.