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Designing and Analyzing Clinical Trials in R

In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.

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4 Hours15 Videos48 Exercises4,123 Learners4000 XP

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Course Description

Clinical trials are scientific experiments that are conducted to assess whether treatments are effective and safe. They are used by a variety of organizations, including pharmaceutical companies for drug development. Biostatisticians play a key role in ensuring the success of a clinical trial. In this course you will gain an overview of the important principles and a practical introduction to commonly used statistical analyses. This course would be valuable for data analysts, medical students, clinicians, medical researchers and others interested in learning about the design and analysis of clinical trials.

  1. 1

    Principles

    Free

    In this chapter you will be introduced to the important principles of clinical trials.

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    Fundamentals
    50 xp
    Data exploration
    100 xp
    Patient characteristics
    100 xp
    Types of data and endpoints
    50 xp
    Exploration of continuous endpoints
    100 xp
    Exploration of binary endpoints
    100 xp
    Exploration of composite endpoints
    100 xp
    Basic statistical analysis
    50 xp
    Comparing means
    100 xp
    Comparing distributions
    100 xp
    Comparing proportions
    100 xp
  2. 2

    Trial Designs

    In this chapter you will be introduced to randomization methods and different types of trial designs.

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    Randomization methods
    50 xp
    Simple randomization
    100 xp
    Block randomization
    100 xp
    Stratified randomization
    100 xp
    Crossover, factorial and cluster randomized trials
    50 xp
    Factorial designs: exploring data
    100 xp
    Factorial designs: generating odds ratios
    100 xp
    Equivalence and non-inferiority trials
    50 xp
    Equivalence trials
    100 xp
    Interpreting results from equivalence trials
    50 xp
    Bioequivalence trials
    50 xp
    Concentration profiles
    100 xp
    Pharmacokinetic parameters
    100 xp
  3. 3

    Sample Size and Power

    By the end of this chapter you will be able to calculate the numbers of patients needed for a clinical trial under a range of scenarios.

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    Introduction to sample size and power
    50 xp
    Sample size for comparing means
    100 xp
    Sample size and treatment difference
    100 xp
    Sample size for comparing proportions
    100 xp
    Sample size adjustments
    50 xp
    Sample size for unequal groups
    100 xp
    Sample size for one-sided tests
    100 xp
    Interim analyses and stopping rules
    50 xp
    Stopping rules
    100 xp
    Sample size adjustments for interim analyses
    100 xp
    Sample size for alternative trial designs
    50 xp
    Sample size for equivalence binary outcomes
    100 xp
    Sample size for equivalence continuous outcomes
    100 xp
  4. 4

    Statistical Analysis

    In this chapter we will explore additional statistical techniques that are commonly used to analyze data from clinical trials.

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    Regression analysis
    50 xp
    Multiple linear regression
    100 xp
    Model prediction
    100 xp
    Multiple logistic regression
    100 xp
    Analysis sets, subgroups and interactions
    50 xp
    Analysis sets
    100 xp
    Interaction
    100 xp
    Multiplicity of data
    50 xp
    Subgroups
    100 xp
    Composite endpoints
    100 xp
    Conclusion
    50 xp

Datasets

Acupuncture datasetFact datasetPK dataset

Collaborators

dcamposliz
David Campos
richie
Richie Cotton
shoninouye
Shon Inouye

Prerequisites

Introduction to Statistics in R
Tamuno Alfred Headshot

Tamuno Alfred

PhD. Biostatistician

Tamuno Alfred is a biostatistician with experience in the pharmaceutical industry and in academia. She has worked on several clinical trials and epidemiological studies. Tamuno has an MSc in Medical Statistics and a PhD in Genetic Epidemiology.
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