The Harvard Medical School China Leadership in Medicine Bridge (CLIMB CSRT) program

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.

Workshop Dates

Workshop 1 – Shanghai | April 23–26, 2019 

Workshop 2 – Shanghai | October 19–22, 2019

Workshop 3 – Boston | April 21–24, 2020 (Tue–Fri)

  • Program Overview

    Program Structure

    • 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

    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)
  • Curriculum

    PROGRAM OVERVIEW

    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.

    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

    Workshop 3

  • Workshop Faculty

    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


    Saqeq A. Quraishi, MD, MHA, MMSc
    Assistant Professor of Anesthesia
    Harvard Medical School


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


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


    Jessica Su, PhD
    Associate Professor of Medicine
    Harvard Medical School


    Joseph Rhatigian, 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


    RECORDED ONLINE LECTURE 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

  • Who Are We Looking For?

    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.

    Application Information:

    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)

    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).

  • Admissions

    To earn a "Certificate of Completion", CLIMB-CSRT students must fulfill all of the program’s academic and attendance requirements listed below:

    ACADEMIC REQUIREMENTS

    Recorded Online Lectures
    The curriculum provides a strong foundation of knowledge and skills across the broad spectrum of hospital leadership. Students are required to complete and pass all related individual course-related assignments and watch all recorded online lectures and videos.

    Submission of Course-Related Assignments
    Students are required to submit all assignments, typically quizzes, on time and to achieve a passing score (a minimum score of 70% unless otherwise noted).

    Team Assignments
    Students are teams at the beginning of the year. Each student must actively contribute to all three team assignments.

    Examinations
    The program requires students to pass multiple–choice exams to demonstrate proficiency in learning the presented material.

    Capstone Proposal
    The program requires the capstone proposal to be submitted by the appointed deadlines noted in the timeline provided at the beginning of the year. A “pass” must be schieved on the final submission.

    ATTENDANCE REQUIREMENTS

    Attendance at Workshops
    Attendance at workshops is a required feature of this program. If a student cannot attend a workshop, a petition must be submitted to the Education Committee for review and approval as soon as possible prior to a workshop.

    Attendance at Webinars and Team Presentations
    The program requires students to attend scheduled live webinars. However, if a student cannot attend the live webinar due to occasional scheduling conflicts, it is possible to review the recording of the webinar. Students are expected to attend a minimum of 75% of all live webinars.

    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.