Curriculum

Recorded Online Lectures
Recorded online lectures (ROLs), available 24/7 throughout the program, are used to deliver the educational material for each module. In the first year, a foundation course will introduce medical statistics and epidemiology, covering basic concepts in descriptive statistics, inferential statistics and study design. In the second year, the curriculum will focus on advanced epidemiology, including causal inference and advanced study design, as well as developing advanced concepts in biostatistics. More details on the modules may be found below.

Biostatistics – This course addresses how to organize, summarize and display quantitative data; and the applied use of statistical software (Stata).

Epidemiology – This course covers the basic principles and methods of epidemiology, including disease (outcome) measures, measures of association, study design options, bias, confounding and effect modification.

Biostatistical Computing – This course focuses on the basics of Stata, including learning key commands, creating do-files, organizing data for analysis, and checking for errors. More advanced lectures will focus on using Stata for regression, survival analysis and generating publication-quality figures and tables.

Research Ethics – This course reviews some common challenges in the conduct and review of biomedical human subjects research, including the evolution of ethical codes and regulations, the responsibility of physicians as investigators, the preparation of research protocol applications and informed consent documents, as well as the challenges of conducting research involving children and adolescents.

Leadership in Medicine – This course examines different aspects of working and leading a team. Lectures discuss the need to manage a group of people effectively, pilot successful collaborations within and outside a group, navigate the complexities of the institution and manage the inevitable conflicts that arise in a clinical research environment.

Applied Regression – This course provides an understanding of the basic principles and uses of linear and logistic regression models for clinical research.

Survival Analysis – This course provides instruction to describe time-to-event data and compare groups with a time-to-event outcome, interpret the coefficients and control for confounding using a Cox proportional hazards model, interpret interaction terms and incorporate time varying covariates in a Cox model as well as assess the proportional hazards assumption.

Correlated Outcomes – This course covers methods to analyze longitudinal data, including the use of linear regression models. Topics will include polynomial trends for time (e.g., linear or quadratic) and linear mixed-effects models. Students will be able to understand the types of missing data that occur in longitudinal and cross-sectional analyses as well as understand the assumptions associated with each analytic approach.

Causal Design – Causal inference is an overarching objective of most forms of medical and epidemiological investigation. They will develop a deeper understanding of observational approaches, especially from the perspective of overcoming the problem of confounding. Students will develop approaches toward identifying confounders, especially via the use of directed acyclic graphs.

Electives

Students will have a choice of electives during the summer months (July and August) of each year. For first-year students may choose either clinical trials or advanced quantitative methods; second-year students may choose either drug development and safety, or genetic epidemiology.

Webinars

The live webinars are designed to complement the learning objectives of the ROLs, providing students with the opportunity to interact directly with HMS faculty.

Mentored Scientific Writing

During the two-year program, students will develop skills in the art of scientific communication. Each student will benefit from the input of a dedicated mentoring committee consisting of a primary Portuguese mentor and an HMS advisor. The task for year one students is to prepare an individual research proposal, and the task for year two is to prepare an original manuscript. It is expected that this manuscript will be submitted for publication in a peer-reviewed medical journal.

Enrolled students will be randomly allocated to teams of approximately five students each. Each team will work on four assignments during the program.

PTCSRT I –

Pitch Your Study
Your team is invited to pitch a study for consideration by a panel. We ask you to provide a presentation for either a clinical trial or an observational study of your team’s choosing. The focus should be on generating a novel research question using an innovative approach that is feasible. Be sure to include information on your specific aims (research question/hypothesis/significance), background, preliminary data (address feasibility, sample size estimation, potential collaborations), research approach/methods, and discussion for your study idea.

Framingham Heart Study
Using the Framingham Heart Study dataset your team is invited to develop a research question, perform the analyses and report your findings for consideration by a faculty panel. We suggest that you include the following components: Background, Research question and hypothesis, Methods (including Study population, Exposure, Outcome, Potential confounders, Any subgroups, Statistical analysis), Results, Interpretation and conclusions.

PTCSRT II –

The SEEK Study
The goal of this team assignment is a post-hoc analysis of the Screening and Early Evaluation of Kidney Disease (SEEK) Study. The task is to come up with a research question, hypothesis and analyses that your team will present to a faculty panel.

Secondary Analysis
The goal of this assignment is to perform a post-hoc analysis of a specific database which will be provided. You will then come up with a research question and hypothesis and perform analyses that you will present via a webinar presentation. You will have a good deal of flexibility in your choice of research questions.