onlincourse

Statistics in Clinical Trials for the Non-Statistician

Discover the crucial role statistics plays in clinical trials and enhance your understanding of clinical trial data and analysis.

Duration

3 weeks

Weekly study

6 hours

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How it works

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Gain statistical knowledge and skills to advance clinical research

Whatever your role in clinical research, this online course from the University of Birmingham will build your statistical knowledge and skills directly applicable to clinical trials.

In just three weeks, you’ll gain the confidence to use what you’ve learned to enhance your enjoyment, engagement and participation within a trial team and improve the quality of future trials, better informing your clinical practice.

Summarise data generated in a clinical trial

You’ll begin by learning how to meaningfully summarise the vast amount of data collected in a trial and how to compare health outcomes across patients, to make formal comparisons between different treatments.

Draw meaningful inferences from trial findings

You’ll learn how statistical tests and models can assess whether any observed difference between two treatments is likely to be real or due to chance, and how confidence intervals enable us to draw more appropriate inferences about the magnitude of potential treatment effects.

Ensure precise and accurate results – even when things go wrong

You’ll learn how crucial sample size calculations are to guarantee precise estimates of treatment effect and how we ensure accurate findings when problems such as non-compliance to treatment and missing data occur.

Utilise appraisal skills to interpret published research and improve the reporting of future studies

Finally, you’ll learn how to recognise poor analyses or interpretation of results in published reports of trials, improving your critical appraisal skills and better informing your own future trials.

Week 1
Summarising trial data and estimating treatment effects
- Introduction to the course
  We start by setting the scene, with a brief reminder of the randomised controlled trial, the gold standard study design to evaluate the effectiveness of interventions in healthcare, and the role of statistics within such studies.
- Collecting and summarising data
  Clinical trials collect a huge amount of data. During this activity, we will learn about the different types of data commonly collected in trials and how we can best summarise these data to make sense of them.
- Estimating the effectiveness of an intervention
  Next, we consider ways to measure the impact of different interventions on health outcomes, and estimate how effective one intervention is compared to another.
Week 2
Confidence intervals, hypothesis testing and sample size
- Confidence intervals
  An estimate of treatment effect comes from a single sample drawn from the population. A different sample would lead to a different estimate. So what range of values could the true treatment effect plausibly take?
- Hypothesis testing
  We now consider hypothesis testing, used to decide whether the data observed in our trial supports a particular hypothesis. For example, are two interventions equally effective, or is one superior to the other?
- Transformations and non-parametric data
  A lot of the statistical methods we have covered so far assume that data follows a normal distribution. What can we do when this is not the case?
- Determining the sample size for a trial
  How do we determine how many participants to recruit to a trial and why does sample size matter?
Week 3
Time-to-event data, missing data/non-compliance, advanced topics
- Time-to-event data in trials
  Interest often lies in the time taken until a specific event for each participant, such as resolution of symptoms, death, or recurrence of disease. Such data requires alternative statistical methods.
- Dealing with missing data and non-compliance in trials
  Common problems that affect virtually all clinical trials are missing data and non-compliance with planned interventions. What are the implications of such problems and how are they best dealt with?
- Statistical analysis plans and publication recommendations
  Statistical analyses need to be planned in advance and reported transparently and accurately. Published guidelines exist for analysis plans, protocol and result papers.
- Advanced topics
  The course ends with an introduction to subgroup analyses and Bayesian statistical methods, and common problems seen in published trial analyses.