The Harvard Medical School Global Clinical Scholars Research Training (GCSRT) program curriculum is designed to enable scholars to develop knowledge and sharpen skills in clinical research.

Foundation, Elective and Concentration Courses

The courses allow scholars to obtain a broad base of knowledge in clinical research and then overlay specialized information in their concentration of either Advanced Epidemiology or Clinical Trials. Both concentrations require scholars to take the foundation courses and their choice of elective courses.

  • Foundation Courses 
  • Electives
  • Concentrations
Capstone Projects

Training in planning and writing a clinical research proposal will be accomplished through the capstone project. Scholars will develop a research question and begin writing their proposals prior to the second workshop. Feedback on the draft proposal will be provided by faculty, and will be reviewed by peers as well as faculty before scholars submit their final versions for ranking by a faculty panel. The scholars authoring the “Top 10” proposals will be invited to present their studies via webinars, and all program participants will be invited to attend. The “Top 3” of these presentations will present at the final workshop in Boston.

  • Foundation Courses

    Introduction to Biostatistics

    This course provides a thorough introduction to the most commonly used biostatistics techniques for clinical research. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; and issues of power and sample size in study designs. There is an introduction to simple linear regression and survival analysis.

    Introduction to Epidemiology

    This introductory course in epidemiology presents an overview but not a detailed discussion of the basic methods of epidemiology and their applications to clinical research. Lectures explore such basic principles of epidemiology as the importance of measurement, including types of outcome measures and measures of association; diverse array of study designs available in clinical research, including cross-sectional studies, cohort studies, case-control studies and experimental designs; types of potential biases, including selection bias and measurement bias; confounding and methods for its avoidance and control; and effect modification.

    Biostatistical Computing

    The ability to import data into a statistical package from a database or excel spreadsheet is considered essential in clinical research. Introductory lectures will consist of teaching the basic functions of the Stata program, including learning key commands, creating a do-file, getting data into the shape needed for analysis and checking for errors. More advanced lectures will focus on using Stata for regression and survival analysis. Lastly, there are lectures on developing polished manuscript-ready tables and figures.

    Ethics and Regulation

    This course reviews some common challenges in the conduct and review of biomedical human subjects research. Lectures examine the history and evolution of ethical codes and regulations; the role and responsibility of physicians as investigators; the preparation of research protocol applications and informed consent documents; and the challenges of conducting research involving children and adolescents.

    Applied Regression

    The course covers sampling distributions; one and two sample tests for means and proportions; correlation and basic linear and multiple regression model building. Initially, lectures will explore general concepts in linear regression and consider residual analysis and data transformations. Lectures will address multiple linear regression, including consideration of confounding and effect modification. Model building will be emphasized. Lastly, several lectures will explore topics in logistical regression including, 2x2 Tables and stratification, model building and assessment of goodness of fit and smoothing and generalized additive models.

    Survival Analysis

    It builds on the basic concepts of survival analysis discussed in Introduction to Biostatistics, including hazard functions, survival functions, types of censoring and truncation, Kaplan-Meier estimates, log-rank tests and their generalization. The course introduces statistical models and methods useful for analyzing univariate and multivariate failure time data. After completing this course, students will be able 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. Lastly, students will learn how to complete a sample size calculation for a survival study.

    Longitudinal and Correlated Data

    A longitudinal study refers to an investigation where outcomes and possibly treatments or exposures are collected at multiple follow-up times. a longitudinal study generally yields multiple or “repeated” measurements on each subject, which may correlate over time. With correlated outcomes, it is useful to understand the strength and pattern of correlations. Characterizing correlation can be approached using mixed-effects models or generalized estimating equations (GEE). 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. At the end of the course, students will be able to interpret the results from a multilevel model and understand how to incorporate multiple random effects into the model. Students will be able to understand the types of missing data that occur in longitudinal and cross-sectional analysis as well as understand the assumptions associated with each analysis approach.

    Causal Design

    Causal inference is an overarching objective of most forms of medical and epidemiological investigation. Key questions usually consist of whether an intervention works and the extent of the benefit and whether it causes harm. While a randomized controlled trial design is considered the most powerful way to infer causality, such studies may not be possible or feasible and an observational approach may be necessary to attain causal inference. At the end of the course, students will have a deeper understanding of observational approaches, especially from the perspective of overcoming the problem of confounding. Students will be able to define confounding and develop approaches toward identifying confounders. DAGs, as a structural approach to identifying confounders, will be highlighted. Other topics will include the rules of D-separation and conditioning on common effects. Propensity scores will be introduced. The differences between randomized trials and observational studies are considered and quasi-experimental designs introduced.

  • Electives

    Each elective has an affiliated team assignment. Students will be divided into temporary teams within electives and asked to make a presentation.

    Drug Development

    Seminar topics include: How are Drugs Discovered and Developed, Case Study of the Pre-clinical Stages of Drug Development, Moving a Compound through the Drug Development Process, Good Manufacturing Practices--a Global Perspective and Overview of Diagnostic Device Development. Entirely webinar based, this elective consists of weekly webinar discussions (generally held at 9 am Eastern Time) by experts from academia, industry and government who have years of hands-on experience with large and small pharmaceutical, biotechnology and related organizations. There are no individual homework assignments and students have the opportunity to interact with the faculty in real time.

    Secondary Analysis of Clinical Trials

    Secondary analysis involves the use of existing data to conduct research beyond the primary question which the original study was designed to answer. This course covers topics commonly encountered in such research, including subgroup analysis, meta-analysis, non-linear relationships and longitudinal data analysis. Relevant statistical methods will be presented and the capabilities of Stata for such analyses will be emphasized. Common mistakes and ways to avoid them will be highlighted. The pre-recorded lectures in this elective are supported by interactive webinars and have an associated individual assignment (quiz).

    Survey Design

    This course covers the crafting of survey questions, the design of surveys and different sampling procedures that are used in practice. Longstanding basic principles of survey design are covered. Statistical aspects of analyzing complex survey data will be featured, including the effects of different design features on bias and variance. Different methods of variance estimation for stratified and clustered samples will be compared, the handling of survey weights will be discussed and the capabilities of Stata for such analyses will be emphasized. This elective consists of a blend of recorded online lectures and interactive webinars. The recorded lectures have an associated individual assignment (quiz).

  • Concentrations

    Advanced Quantitative Methods of Epidemiology

    Instruction on the application of advanced quantitative methods as they pertain to T4 translational research. Topics include an overview of comparative and cost-effectiveness research, meta-analysis, quasi-experimental designs including instrumental variables and marginal structural modeling, propensity scores and time-series analysis.

    Principles and Practice of Clinical Trials

    Course content includes lectures on study design and implementation including different designs, endpoints, study protocol, study population, recruitment, baseline assessment, randomization, stratification and blinding. Other key issues addressed include data analysis and sample size and power, treatment regimens and follow-up procedures and monitoring and interim analysis plans.