Comparative Effectiveness & Implementation Research Training Program Course Descriptions
In NYU School of Medicine’s Comparative Effectiveness and Implementation Research Training Program, training in both programs offered—the MS in clinical investigation, with a concentration in comparative effectiveness and implementation research; and advanced certificate—is built around five foundational courses.
Early Summer Courses
The early summer courses are selectives. Students choose one of the following courses.
This course provides a comprehensive introduction to dissemination and implementation (D&I) science research to enable students to develop core competencies in D&I science. The course enhances students’ ability to conceptualize and think through D&I research problems and apply theory and employ approaches to improve implementation outcomes with increasing independence. Students complete outside readings throughout the course and are expected to successfully apply an implementation science framework to a prepared research question by the end of the course.
Health Communication Strategies to Engage Patients and the Public: Methods to Design and Evaluate Effective Multimedia Tools (3 Credits)
This course’s objectives are to learn models of patient–physician communication and methods to improve patient involvement in decision making. Topics include the foundations of developing decision aids, particularly issues around literacy, numeracy, preference elicitation, and tailoring of information. Issues regarding development of web-based tools and evaluation and implementation of decision aids are discussed. Lectures are accompanied by labs that involve students developing a decision aid. Evaluation is based on class participation, problem sets, and class project/presentation.
Certificate students have a third selective course for the fall: Advanced Methods in Observational Data Analysis, Biostatistics Part III. Master’s students are required to complete all fall courses.
This course trains students to conduct a systematic literature review, considered by many investigators to be the highest level of evidence for answering clinical questions. This graduate‐level course is comprised of didactic classroom sessions and lectures on the topic as well as the hands-on conducting of a systematic review on a topic. Students are taught how to perform each step in a review and apply it to a topic of interest that they either choose at the beginning of the class, or have provided to them. Lab sessions focus on practical aspects of meta-analysis. Analyses are performed using RevMan software, which is available as a free download. At the completion of this course, students should be able to formulate key questions for a systematic review, organize a literature search, identify which literature databases to search, abstract relevant information from studies in a systematic manner, rate the scientific quality of each study, create evidence tables and summary tables, summarize the studies’ findings, and interpret findings. The final deliverable for the course will be a systematic literature review presentation.
The purpose of this course is to introduce concepts and techniques used in the economic evaluation of healthcare interventions and develop a specific research question; apply best practices of model building; and conduct analysis and interpret results. This course focuses on the methods of cost-effectiveness analysis. Decision analysis in general, and cost-effectiveness analysis in particular, is an approach to help decision makers systematically and simultaneously compare a full range of options, apply all relevant evidence (what is known and unknown), consider consequences of action (or inaction), and incorporate patient preferences. This course provides an understanding of the foundations of decision analysis and cost-effectiveness analysis, with sufficient detail regarding the mechanics and methodologies to prepare students to both interpret and critique the literature of cost-effectiveness analysis and construct these analyses themselves.
This course builds on prior training in introductory biostatistics and epidemiology to extend understanding of core concepts and methods by providing applied training in the conduct of secondary data analysis studies. Using an existing data source, students identify a research question; define a causal model, specific aims, and hypotheses; gain experience in management and conditioning of the data; conduct stratified analyses to assess effect modification and confounding; implement the backward elimination method of model building using logistic regression to obtain multivariable results; and interpret results with respect to the strength and precision of estimates, selection and information bias, and confoundedness, missingness, and generalizability. Students are trained in and use SAS or STATA for all data cleaning, conditioning, and analysis. In addition, advanced topics in statistics, such as the use of propensity scores to address confounding and use of mixed models for clustered data, are introduced.