Objectives	To be able to:  Develop a working knowledge of clinical trial and medical statistics  Develop the capacity to critically interpret medical statistics, as presented in any format
Key Concepts	Distinguish types of outcomes (continuous, binary, time-to-event) and identify the commonly associated analysis methods (linear regression, logistic regression, Kaplan-Meier techniques, proportional hazards model, etc) Interpret key clinical trial and epidemiology summary statistics (such as response rate, hazard ratio, odds ratio, etc) Recognise different measures of variability (standard error, confidence intervals) Interpret a P value Describe concepts of sample size calculation (type I error, power, targeted effect) Explain the role of randomisation and describe the different randomisation methods (block, minimisation, stratification) Explain the link between a trial objective, an end point and a statistical test Recognise the difference between types of data (clinical trials, observational studies, real-world data such as registries or electronic medical records, case studies) Describe concepts of clinical trial designs (comparative v non-comparative, blinding, superiority v non-inferiority, intention-to-treat v per-protocol analysis, confirmatory v exploratory, basket designs, platform designs) Identify sources of bias and describe the impact on study outcome
Skills	Demonstrate the ability to:  Apply statistical concepts in research questions  Perform basic analyses of research data  Correctly present the results of clinical research  Assess critically the scientific value of data being presented, and to deduce knowledge from such information  Perform calculation of sample size of a standard design  Discuss and interact with statistical and data science professionals  Report and understand the difference between relative treatment effect (eg, a hazard ratio) and absolute benefits (eg, a 10% difference in overall survival at 5 years)