• Length:
    3 Weeks
  • Effort:
    4–5 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Introductory
  • Language:
    English
  • Video Transcript:
    English

Prerequisites

Introduction course (CERTaIN.1x) recommended, but not required.

About this course

In this course, experts will discuss the options a researcher must consider when embarking on clinical research. What research design should I choose? How do I start the process of getting my research approved? How will I analyze the data I collect? These are all important questions that a researcher faces.

We will discuss the key decisions a researcher needs to make when preparing for and conducting research, as well as tools for data analysis. You will learn what a pragmatic clinical trial is and how to calculate power and sample size for your study. You will also be exposed to more complex study designs sometimes used in pragmatic clinical trials, such as Bayesian and adaptive designs.

This course includes the following 11 lectures: 
  1. Overview of Design Options for Pragmatic Clinical Trials
  2. Outcome Measures in Clinical Trials
  3. Non-inferiority Trials
  4. Basic Analytic Methods
  5. Basic Power and Sample Size Calculations
  6. SMART: Adaptive Treatment Strategies
  7. Introduction to Bayesian Methods
  8. Bayesian Designs
  9. Quasi-Experiment in Health Services Research
  10. Adaptive Trial Design
  11. Logistics of Clinical Trials
This course is intended for anyone interested in comparative effectiveness research (CER) and patient-centered outcomes research (PCOR) methods.

This course is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality.

What you'll learn

  • Describe pragmatic clinical trial designs
  • Identify and classify clinical trial designs
  • Describe the statistical methods used to analyze pragmatic clinical trial data 
Overview of Design Options for Pragmatic Clinical Trials
  • Types of trial designs including randomized clinical trials
  • Sources of errors that could lead to erroneous trial results
Outcome Measures in Clinical Trials
  • Measuring health status and disease
  • Characteristics of a good outcome measure
  • Types of outcome measures in clinical trials
Non-inferiority Trials
  • Non-inferiority trial designs and how they differ from other designs
  • Interpretation and reporting of results
Basic Analytic Methods
  • Matching research questions with statistical analysis methods
  • Interpreting results
  • Distinguishing intent to treat from per protocol analyses
  • Problems with missing data
Basic Power and Sample Size Calculations
  • Relevant issues to estimate sample size in clinical trials
  • Interpreting study power
  • Calculating sample size using online calculators
SMART: Adaptive Treatment Strategies
  • Research questions which can benefit from an adaptive design
  • Analytic methods for adaptive designs
  • Interpreting results of adaptive designs
Introduction to Bayesian Methods
  • Statistical inference
  • Comparison of frequentist and Bayesian approaches and inference
Bayesian Designs
  • Bayesian method and adaptive design introduction
  • Adaptive randomization and predictive probability
  • Bayesian design trial interpretation
  • Software tools for conducting Bayesian designs
Quasi-Experiment in Health Services Research
  • Need for quasi-experiments and their limitations
  • Difference-in-Differences (DID) estimators and models
Adaptive Trial Design
  • Limitations of randomized traditional trials
  • Common adaptive approaches and their challenges
  • Covariate and outcome adaptive randomization
Logistics of Clinical Trials
  • Assessing impact, feasibility and funding
  • Scientific review process
  • Study initiation, continuing review and audits
  • Working with sponsors

Meet your instructors

Maria E. Suarez-Almazor, MD, PhD
Barnts Family Distinguished Professor
The University of Texas MD Anderson Cancer Center
Barry R. Davis, MD, PhD
Professor and Interim Chair
The University of Texas Health Science Center at Houston, School of Public Health

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