ÐÂÏã¸ÛÁùºÏ²Ê¿ª½±½á¹û

This module will cover the key topics of statistical inference, hypothesis testing and statistical modelling. In particular, the most important statistical distributions used in clinical trials (e.g. normal, binomial, Poisson, exponential distributions) will be studied and will act as the basis for studying hypothesis testing (which will include one-sided and two-sided tests).Ìý

You will learn how to specify the main statistical models (i.e. write down the equation that describes the model and its underlying assumptions) used in clinical trials (e.g. Ancova, repeated measures mixed models, logistic regression, Cox proportional hazard), how to interpret their results and how all these models can be linked together into a unified framework.Ìý

Finally, you will learn the basics of Bayesian models for clinical trials.Ìý

The aim of this module is to provide you with theoretical knowledge of the foundations of statistical science and in particular the modelling process, using linear, generalised linear and time-to-event models. You will be able to select an appropriate model to use for a given analysis, assess model fit, and interpret the results from statistical modelling.Ìý

At the end of the module, you will be able to:Ìý

1. Use the general principles of inference to perform estimation on parameters of distributionÌý

2. Perform appropriate statistical tests for a range of different analysesÌý

3. Specify statistical models that are used in clinical trialsÌý

4. Interpret the results of statistical models applied to clinical trials dataÌý

This module is compulsory for students on the Statistics for Clinical Trials route of the MSc Clinical Trials.Ìý

Altman, Douglas G. Practical statistics for medical research. CRC press, 1990.Ìý

Chow, Shein-Chung, and Jen-pei Liu. Design and analysis of clinical trials: concepts and methodologies . Vol. 507. John Wiley & Sons, 2008.Ìý

Friedman, Lawrence M., et al. Fundamentals of clinical trials. Springer, 2015.Ìý