Courses

This course will provide the necessary background and applied training in qualitative methods for health services research, including in-depth and semi-structured interviews, focus groups and participant observation. Students will learn a variety of qualitative data analysis strategies, how to use qualitative data analysis software and how to obtain inter-coder reliability. Students will gain hands-on experience with data analysis including: data management, transcription, coding and synthesizing qualitative data. Students will also gain skills in the presentation and communication of qualitative data through writing up their study methods and results to prepare high quality manuscripts and grant applications. Through class participation, reading assignments, developing data collection instruments, practicing data collection methods and critiquing each other’s instruments and analyses, students will gain the necessary skills to prepare and conduct, as well as write grant proposals involving, qualitative research methods and analysis in health services research.

This course provides an overview of methods for conducting health services and outcomes research. It is intended to complement students’ training in epidemiology and biostatistics and to help prepare students to conduct independent research. Topics include: identifying appropriate conceptual models, conducting systematic literature reviews, basic survey design and implementation, approaches to using mixed methods and navigating popular data sets.

This course establishes understanding of the concepts of descriptive and inferential statistics and the application of statistical methods in the medical and health fields. Course topics include: descriptive statistics, basic probability concepts, probability distributions, estimation, hypothesis testing, correlation and simple linear regression.

This course introduces descriptive and analytic epidemiology and how to apply these methods to the study of health services and outcomes research. Key epidemiological concepts such as association, bias and confounding will be covered, as well as the main epidemiologic study designs. Topics include overview of research design, basic measurement of health services data, defining and measuring appropriate health outcomes, constructing research questions, conducting univariate analyses and interpreting results. Practical computer-lab sessions will provide training in importing data into statistical software, cleaning data, creating new variables, descriptive analysis and univariate analysis.

This course examines selected topics in health economics that have major implications for healthcare delivery, healthcare financing and clinical and public health research. Essential economic theories and methods for exploring each topic will be discussed along with examples drawn from the existing research literature on the application of these theories and methods. Specific topics include: consumer behavior and health demand with a special focus on the analysis of secondary data, principles of price and quality competition, principles of health insurance and methods for economic evaluations.

This course builds upon the material learned in Introduction to Biostatistics and complements the Quantitative Methods in HSOR sequence. Specifically, the course will focus on multivariable methods of analysis for epidemiologic and clinical studies including correlation, linear regression, logistic regression and Cox proportional hazards regression.

This seminar provides an overview of core ethical concepts relevant to scientists conducting health services research. Ethical research topics include: working with large data sets, obtaining Institutional Review Board approval, quality improvement vs. research projects, vulnerable populations and community-based participatory research, informed consent, conflicts of interest, patient safety, using electronic health records as a data source and research misconduct and authorship dilemmas. The ethics seminar is held in person over three days during the Winter residency.

This course provides the student with an opportunity to learn and practice more advanced epidemiologic methods in the context of health services and outcomes research. The following topics will be included: epidemiologic research strategies and how to apply these to various research questions, review of univariate analysis (e.g., odds ratios, chi-sq tests), issues in effect estimation (precision, selection bias, misclassification bias), methods for dealing with covariates (stratified analysis, effective modification, confounding), introduction to multivariate analysis and interpreting research findings to guide decision-making. Features practical computer-lab sessions in Stata.

This course is an intensive, hands-on course that covers preparing, peer reviewing, revising and submitting manuscripts for publication. Students will be expected to prepare and hand in written work for each class and to participate actively in class discussion. Students will prepare an article, respond to two peer review cycles and be ready to submit to a journal by the conclusion of the course.

This course consists of lectures and discussions on a variety of topics related to grant writing, including development of various components of a grant proposal and identifying potential funders.

The Capstone Project serves as a culminating experience and summative product of students’ experiences in the master’s program. It provides an opportunity for students to demonstrate their research development, data gathering and management, analysis, interpretation and dissemination skills. The Capstone Project is conducted as an independent study. Students spend their first several quarters in the master's program developing an original and independent research proposal under the guidance of faculty advisers. Students gather data and complete analysis before enrolling in HSR 500. While enrolled in HSR 500, students draft their project manuscript, receive feedback from advisers and prepare a final manuscript that is suitable for publication in a peer-reviewed journal.

This course has three objectives:

• To improve students' knowledge of current health policy issues
• To introduce students to the policy making process and the roles of various policy actors
• To prepare students to participate in the policy process

Students will develop (with guidance) a course of study that advances their knowledge in a particular area related to health services or outcomes research.

Students will develop (with guidance) a course of study that advances their knowledge in a particular area related to health services or outcomes research.

This course provides an overview of social, economic and political inequities in the United States and their impact on the health of the poor, uninsured, elderly, racial and ethnic minorities, migrants, gendered and sexual groups, rural residents, people with mental and physical disabilities and other vulnerable and socially disadvantaged populations. Past and current policies and trends in health/medical care programs and services at the local and national levels will be discussed. Class topics and discussions include: social and income inequalities; access to (or lack of) healthcare, including preventive services and other social resources; roles of government and the legislative process; quality care; legal and ethical issues; and implications of the Affordable Care Act and recent Supreme Court rulings, among others.

This course covers the quantitative analytic techniques intended to inform decision makers at the bedside as well as at the policy-making level. Topics include probability, Bayes’ theorem, sensitivity and specificity of diagnostic tests, Bayesian decision analysis, utility assessment, cost-effectiveness analysis and expert systems.

This course focuses on methodological issues regarding the design, implementation, analysis and interpretation of surveys and questionnaires in health research. Various types of self-reported data will be discussed, including knowledge, attitudes, behaviors and patient-reported outcomes, such as general health status, pain and fatigue. Design 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 covers advanced decision-analytic methods useful in medical decision modeling. 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 introduces commonly used designs for clinical trails, methods for randomization, blinding and sample size determination, choice of controls, collaborative/multicenter trial requirements and operational issues, data management and data quality issues, interim analysis methods, critical review of clinical trial results and statistical techniques for analyzing data.