Clinical trials are used within a range of health fields as we seek to understand the level of benefit (and harm) from different interventions (such as pharmaceuticals, patient therapies, and health promotion campaigns). A well-designed clinical trial has a clear strategy for aspects such as patient recruitment, the timing and dose of interventions, and the scheduling of data collection.

Session 1 - Introduction to Clinical Trials

• Types of Clinical Trials - Drugs, Devices, Health Prevention, Complementary or Alternative Medicine, Surgery and Skill-Dependent Therapies

• Objectives and Outcomes - determining what to measure

Session 2 - Validity and Reliability

• Validity - the relationship between the measured variable and the underlying construct

• Relability - if we measure the same thing (maybe under different conditions) will we obtain the same measurements

• Methods for assessing validity and reliability

Session 3 - Introduction to Study Design

• Experimental versus observational studies

• Ethics

• Choice of study cohort

• Clinical trial design

• Superiority versus non-inferiority

Session 4 - Treatment Allocation

• Placebos

• Replication

• Randomization

• Blocking

• Stratification

• Adaptive allocation

• Minimization

• Groups of unequal sizes

Session 5 - Factiorial and Cross-over Designs

• Treatment interactions

• Factorial designs

• Cross-over designs

• Analysis of data from different designs

• Sample size calculations

Session 6 - Diagnostic Tests

• Choosing the threshold for a diagnostic measure

• Classification tables

• Odds and Relative Risk

• Receiver Operating Characteristic (ROC) curves

• Factoring in the cost of making different kinds of mistakes

• Generalizability

Session 7 - Missing Values

• Response rates

• Missing Completely At Random, Missing At Random, Missing Not At Random

• Understanding and addressing the reasons for missing data

• Multiple imputation

Session 8 - Computer Exercise Session (Basic exercises in Excel)

• Treatment allocation (randomization, minimization)

• Study design (factorial designs, cross-over designs, sample size calculations)

• Diagnostic tests

Consists of 7 PowerPoint sessions + 1 computer exercise session

No advanced level of knowledge is required in this workshop. Participants should have a basic knowledge of statistics (t-tests and P-values).

Also while not essential, participants might benefit from some previous experience with clinical trials.

These workshops are designed for a broad range of participants, researchers from various application fields who only spend a small portion of their time doing statistics (but researchers who when they do statistics want to be doing statistics well). Communicating complex statistical ideas to a non-statistical audience requires significant skill. Equations for example are vital for statisticians who want to describe statistical models accurately but succinctly, however these equations can be a stumbling block for a larger audience who are not used to working with complex equations. We think one sign of a good statistician is that they know the pictures that go on behind the equations, and in our workshops we focus on the pictures rather than the equations.

Working with this broad audience we teach complex statistical theory (we find it heart-breaking when excellent research studies are undone by a poor understanding of underlying statistical theory), but we teach this theory using a number of practical real-world examples. We are also a strong believer in the use of humour in a training context, where humour means that participants are more likely to relax in a training context, to engage with the training content and the presenter, and are more comfortable to ask the questions that they really want to ask about the content as a result.

Each workshop will be capped at 20 participants.