Program Overview

The Harvard Medical School (HMS) China Clinical Research training (CLIMB CSRT) program is a 12–month blended-learning program designed to provide participants with deep knowledge of the practice of clinical research. HMS CLIMB CSRT will develop this expertise through an understanding of the generation, analysis, interpretation and presentation of clinical research data for publication. The goal of this program is for participants to attain fundamental knowledge in four key areas that comprise clinical research mastery–epidemiology, biostatistics, programming and scientific communication.

The program curriculum consists of approximately 70 recorded online lectures, including seven foundation courses. Lectures are supported by live interactive webinars and face-to-face workshops. Throughout the program, scholars are expected to develop and present two team-based assignments (research proposal and ethics) and a research capstone project to give each scholar the experience of submitting a research grant proposal.

    Program Benefits
    • Access to senior faculty from Harvard Medical School, Harvard Business School and other Harvard University schools, health care experts, published researchers and accomplished leaders in information technology, quality management and process improvement from Harvard-affiliated hospitals and institutes
    • A blend of online and live teaching
    • Team-based learning
    • Convenient 24/7 access to recorded online lectures
    • Certificate of Completion and eligibility to become Associate Members of the Harvard Medical School and Harvard University Alumni Association (upon successful completion of the program)
    Workshop Dates

    Workshop 1 – Shanghai | August 17–20, 2020 (Live Virtual) 

    Workshop 2 – Shanghai | February 1–4, 2021

    Workshop 3 – Boston | August 9–12, 2021 

    • Curriculum

      Program Outline
      • 12–month certificate program
      • 70 online lectures in modules
      • 16 live interactive webinars led by expert faculty
      • Two team assignments to present study design and solve an ethical issue in subject consent
      • Individual capstone proposal – scholars will develop a research question and draft a research proposal
      • The choice of three electives: Survey Design, Secondary Analysis of Clinical Trials or Advanced Biostatistics
      Foundation Modules

      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.

      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.

      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.

      Scientific Communication

      Publishing in peer-reviewed journals and obtaining independent grant funding are critical for success in clinical research. The China-CSRT program places special emphasis on developing skills in writing and the presentation of research data. The module offers students several unique opportunities to develop such skills. Lecture topics will facilitate the development of both writing and presentation skills.

      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

      This course 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.

      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 are introduced.

      Electives

      Advanced Biostatistics: Correlated Outcomes

      This course covers topics in Advanced Biostatistics: Correlated Outcomes. 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.

      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. This elective contains pre-recorded lectures, a quiz, and an individual assignment.

      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 recorded online lectures, a quiz and an individual assignment.

      Team Projects

      Pitch Your Study Design

      This exercise requires teams to develop a study idea using a provided dataset (n=5,000). Team representatives will present their study design by webinar and receive live feedback from faculty experts in clinical research.

      Ethics and Consent Form Evaluation

      This exercise requires teams to analyze consent forms while acting as an IRB. Team representatives will present their critique of the consent forms by webinar and receive live feedback from expert IRB faculty.

      Capstone

      The capstone project is designed to give each scholar the experience of submitting a research grant proposal to a funding agency. Scholars will develop a research question and begin writing their proposals after the second workshop. Feedback on the draft proposal will be provided by faculty for scholars to review before they submit their final versions for ranking by a faculty panel. The top four capstone projects will be invited to present in the final workshop.

      Course Objectives

      At the completion of the program, students will be equipped to:

      • Interpret and carry out both observational and experimental clinical research.
      • Program utilizing elementary and advanced Stata programming constructs.
      • Analyze, interpret, and present clinical research data.
      • Write a clear and impactful capstone proposal.
      • Communicate scientific data with clarity and confidence.
      Workshops

      The China-CSRT program includes three residential workshops, two in Shanghai, China, and one in Boston, MA, USA. The workshops will be a mix of didactic and practical exercises.

      Workshop 1
      Introduction to Epidemiology – Heather Baer
      Lecture 1: Introduction and Outcome Measures
      Lecture 2: Measures of Association

      Introduction to Biostatistics Module – Brian Healy
      Lecture 1: Introduction
      Lecture 2: Estimation
      Lecture 3: Hypothesis Testing

      Biostatistical Computing – Kenneth B. Christopher
      Stata Workshop Lecture 1
      Stata Workshop Lecture 2
      Stata Workshop Lecture 3
      Stata Workshop Lecture 4

      Introduction to Epidemiology (continued) – Heather Baer
      Lecture 3: Study Design: Randomized Controlled Trials (RCTs)
      Lecture 4: Study Design: Cohort Studies
      Lecture 5: Study Design: Case-Control Studies
      Lecture 6: Threats to Validity: Bias and Confounding
      Lecture 7: Matching and Effect Modification
      Lecture 8: Regression in Epidemiology

      Introduction to Biostatistics Module (continued) – Brian Healy
      Lecture 4: Sample Size and Study Design
      Lecture 5: Nonparametric Test
      Lecture 6: Analysis of Proportions

      Biostatistical Computing (continued) – Kenneth B. Christopher
      Stata Workshop Lecture 5
      Stata Workshop Lecture 6
      Stata Workshop Lecture 7
      Stata Workshop Lecture 8

      Introduction to Biostatistics Module (continued) – Brian Healy
      Lecture 7: Linear Regression and Correlation
      Lecture 8: Multiple Regression
      Lecture 9: Logistic Regression
      Lecture 10: Survival Analysis

      Electives
      Secondary Analysis – Brian Claggett
      Lecture 1: Introduction
      Lecture 2: Non-Normal Data
      Lecture 3: Non-Normal Data (continued) and Introduction to Subgroup Analysis
      Lecture 4: Meta-Analysis
      Lecture 5: Longitudinal Data
      Lecture 6: Survival Analysis
      Lecture 7: Missing Data
      Lecture 8: Nonlinear Relationships
      Lecture 9: Risk Models
      Lecture 10: Matching and Propensity Scores

      Survey Design – Chase Harrison
      Lecture 1: Introduction to Survey Research
      Lecture 2: Selecting a Survey Mode
      Lecture 3: Survey Questions: Constructs and Measures
      Lecture 4: Qualitative and Semistructured Approaches to Questionnaire Design
      Lecture 5: Practical Issues in Designing a Survey Sample
      Lecture 6: Statistical Analysis for Survey Research – Brian Healy

      Advanced Biostatistics: Correlated Outcomes – Garrett Fitzmaurice & Brian Healy
      Lecture 1: Longitudinal Data Analysis – Introduction and Overview
      Lecture 2: Longitudinal Data – Basic Concepts
      Lecture 3: Linear Regression Models for Longitudinal Data – Part I
      Lecture 4: Linear Regression Models for Longitudinal Data – Part II
      Lecture 5: Multilevel Models
      Lecture 6: Missing Data
      Lecture 7: Repeated Measures with Dichotomous Outcomes – GEE
      Lecture 8: Generalized Linear Mixed–Effects Model

      Workshop 2

      Ethics and Regulation – Susan Kornetsky
      Lecture 1: An Investigator's Responsibility for Protection of Research Subjects
      Lecture 2: Preparing Research Protocol Applications and Informed Consent Documents
      Lecture 3: Research Involving Children and Adolescents
      Lecture 4: The Internet and Social Media: Research Ethical Issues

      Applied Regression – David Wypij
      Lecture 1: Linear Regression – General Concepts
      Lecture 2: Residual Analysis and Data Transformations
      Lecture 3: Multiple Linear Regression, Confounding and Effect Modification
      Lecture 4: Case Study – Circulatory Arrest Study
      Lecture 5: Model Building

      Causal Design – Jessica Paulus
      Lecture 1: Confounding
      Lecture 2: Introduction to Directed Acyclic Graphs (DAGs)
      Lecture 3: Propensity scores

      Applied Regression continued – David Wypij
      Lecture 6: Logistic Regression – 2x2 Tables and Stratification
      Lecture 7: Introduction to Logistic Regression Modeling
      Lecture 8: Multiple Logistic Regression, Confounding and Effect, Modification Using the Circulatory Arrest Study Data
      Lecture 9: Model Building and Assessment of Goodness of Fit
      Lecture 10: Smoothing and generalized additive models

      Causal Design continued – Jessica Paulus
      Lecture 4: Instrumental Variables
      Lecture 5: Misclassification
      Lecture 6: Experimental vs. Observational Studies

      Survival Analysis – Brian Healy
      Lecture 1: Introduction and Group Comparisons
      Lecture 2: Cox Proportional Hazards Regression Models: Part I
      Lecture 3: Cox Proportional Hazards Regression Models: Part II
      Lecture 4: Study Design

      Scientific Communication – Kenneth B. Christopher & Danielle Baer
      Lecture 1: How to Give an Engaging Presentation
      Lecture 2: Preparation
      Lecture 3: Supportive Media
      Lecture 4: Giving Your Presentation

      Casual Design continued – Jessica Paulus
      Lecture 7: Case Control Studies
      Lecture 8: Case Crossover Studies
      Lecture 9: Selection Bias

       

    • Who Should Apply

      This program will benefit Chinese physicians at the fellowship trainee level who have experience in research but may lack formal training in clinical research methods.

      Candidates for the program should indicate any doctoral-level degree (for example, MD, PhD, MBBS, MBChB, DNP, MSN, DMD, DDC, PharmD) or master’s–level degree (for example, MBA, MPH, MSc).

    • Faculty

      Course Director

      Kenneth Christopher, MD, SM
      Faculty Director
      Postgraduate Medical Education, Harvard Medical School

      Capstone Director

      Jamie Robertson, PhD, MPH
      Assistant Director of Simulation-Based Learning Neil and Elise Wallace STRATUS Center for Medical Simulation Brigham and Women’s Hospital
      Instructor in Emergency Medicine Harvard Medical School

      Online Curriculum Faculty

      Danielle Baer, RD, MRes
      HEE/NIHR Clinical Doctoral Fellow and Critical Care Dietitian
      Guy’s and St Thomas’ NHS Foundation Trust

      Heather J. Baer, ScD
      Associate Epidemiologist
      Brigham and Women’s Hospital
      Assistant Professor of Medicine
      Harvard Medical School
      Assistant Professor of Epidemiology
      Harvard T.H. Chan School of Public Health

      Brian L. Claggett, PhD
      Instructor in Medicine
      Harvard Medical School
      Chief Statistician
      Cardiac Imaging Core Laboratory and Clinical Trials Endpoints Center
      Brigham and Women's Hospital

      Garrett Fitzmaurice, ScD
      Professor of Psychiatry (Biostatistics)
      Harvard Medical School
      Professor, Department of Biostatistics
      Harvard T.H. Chan School of Public Health
      Director of the Laboratory for Psychiatric Biostatistics
      McLean Hospital

      Chase H. Harrison, PhD
      Associate Director
      Harvard Program on Survey Research and Preceptor in Survey Methods, Department of Government
      Harvard University

      Brian Healy, PhD
      Assistant Professor of Neurology
      Harvard Medical School

      Jessica Paulus, ScD
      Assistant Professor of Medicine, Sackler School of Graduate Biomedical Sciences, Tufts University
      Associate Director, Tufts Clinical and Translational Science MS/PhD Graduate Program

      David Wypij, PhD
      Director of Graduate Studies and Senior Lecturer on Biostatistics Harvard T.H. Chan School of Public Health Associate Professor of Pediatrics Harvard Medical School Director Statistics and Data Coordinating Center Department of Cardiology Children's Hospital Boston

       

      Workshop Faculty

      **based upon previous year's program**

      Andrew L. Beam, PhD
      Instructor, Department of Biomedical Informatics
      Harvard Medical School

      Jeffrey M. Drazen, MD
      Distinguished Parker B. Francis Professor of Medicine
      Harvard Medical School
      Pulmonary Division, Brigham and Women's Hospital
      Editor-in-Chief, New England Journal of Medicine

      Vanessa Garcia-Larsen, PhD, MSc
      Assistant Professor
      Johns Hopkins Bloomberg School of Public Health

      Anthony L. Komaroff, MD
      Simcox-Clifford-Higby Professor of Medicine
      Harvard Medical School
      Editor in Chief, Harvard Health Publications

      Miguel Hernan, MD, MPH, ScM, DrPH
      Professor of Epidemiology
      Harvard T.H. Chan School of Public Health

      Susan Z. Kornetsky, MPH
      Senior Director of Clinical Research Compliance
      Boston Children's Hospital

      Melvyn Menezes, MBA, PhD
      Associate Professor of Marketing
      Boston University

      Nilesh Mehta, MD
      Associate Professor in Anaesthesia
      Harvard Medical School
      Director, Critical Care Nutrition
      Associate Medical Director, MSICU
      Director, Quality and Outcomes
      Children’s Hospital Boston

      Gregg S. Meyer, MD, MSc
      Chief Clinical Officer
      Partners Healthcare

      Sadeq Ali Quraishi, MD
      Assistant Professor of Anaesthesia
      Massachusetts General Hospital

      V. Kasturi Rangan
      Malcolm P. McNair Professor of Marketing
      Co-chairman, Social Enterprise Initiative
      Harvard Business School

      Joseph Rhatigan, MD
      Assistant Professor of Medicine
      Harvard Medical School
      Associate Chief of the Division of Global Health Equity and Director
      Hiatt Global Health Equity Residency Program
      Brigham and Women’s Hospital

      Rebecca Rolland, MS, MA, EdD
      Adjunct Lecturer on Education
      Harvard University

      Jamie M Robertson, PhD, MPH
      Assistant Director of Simulation-Based Learning
      Neil and Elise Wallace STRATUS Center for Medical Simulation
      Brigham and Women’s Hospital
      Instructor in Emergency Medicine
      Harvard Medical School

      Ajay K. Singh, MBBS, FRCP, MBA
      Senior Associate Dean for Postgraduate Medical Education
      Harvard Medical School

      Douglas S. Smink, MD, MPH
      Associate Professor
      Harvard Medical School
      Program Director, General Surgery Residency
      Associate Chair of Education, Department of Surgery
      Brigham and Women’s Hospital

      Djøra Soeteman, PhD
      Research Scientist
      Center for Health Decision Science
      Harvard T.H. Chan School of Public Health

      Jennifer P. Stevens, MD, MS
      Assistant Professor of Medicine
      Harvard Medical School
      Director, Center for Healthcare Delivery Science
      Beth Israel Deaconess Medical Center

      Jessica Lasky-Su, PhD
      Associate Professor of Medicine
      Harvard Medical School

      Adam Wright, PhD
      Associate Professor of Biomedical Informatics
      Harvard Medical School
      Associate Professor of Medicine
      Brigham and Women’s Hospital

    • Admissions

      The following documents are required to apply for the program:

      • Online Application
      • Current Curriculum Vitae/Résumé
      • Personal Statement
      • Letter of Recommendation (from a department/division head, director, chair or supervisor)

      Please contact your sponsoring institution for additional admissions information.

       

      Tuition

      Tuition for the CLIMB CSRT program is $18,000 USD

      Tuition includes fees for the three workshops and all online lecture material. Fees do not include books, supplies or travel expenses.