Clinical Statistical Methods

Details

Title

Clinical Statistical Methods

Type

Stage One

Module code

HBI109

Requirement

Compulsory

Module objective

By the end of this module the Clinical Scientist in HSST will be able to analyse, synthesise and apply their expert knowledge, skills and experience with respect to the range of statistical techniques relevant to Clinical Bioinformatics and closely related specialisms. They will understand the design, implementation and analysis of data from a wide knowledge and skills base, i.e. clinical trials relevant to their practice. They will be able to: (i) rationally design an experiment to test a clinically relevant hypothesis and collect, verify and analyse appropriate clinical experimental data; and (ii) actively participate in clinical trial processes, independently analysing clinical trials data and interpreting the results. The Clinical Scientist in HSST will be able to demonstrate a critical understanding of current research and how clinical statistics contribute to innovative and new approaches within their area of specialisation. The Clinical Scientist in HSST should also understand and use complex computer statistical routines in stand-alone programmes (e.g. dedicated statistical software packages/programming languages). The Clinical Scientist in HSST will also be expected to consistently demonstrate the attitudes and behaviours necessary for the role of a CCS.

Knowledge and understanding

By the end of this module the Clinical Scientist in HSST will be able to analyse, synthesise, evaluate and critically apply their expert knowledge of Clinical Statistics, including:

Statistical methods:

• measurement of uncertainty;
• experimental design;
• data description and cleaning;
• normalisation and scaling;
• principles, calculation and interpretation of probability, risk ratios, odds ratios;
• statistical significance and confidence intervals;
• multiple testing correction;
• differences between groups;
• type I and type II errors;
• sensitivity and specificity, positive and negative predictive value of a test;
• false discovery rates;
• role and basic principles of meta-analysis;
• analysis of repeated measures and time series data;
• role and principles of multivariate

Clinical trials:

• design and randomisation;
• trial protocols, including adverse effects and evidence of benefit;
• ethical guidelines;
• patient recruitment and consent;
• calculation of power – sampling size;
• Kaplan Meier survival curves;
• Cox regression;
• hazard ratios;
• intention-to-treat analysis;
• left and right censoring;
• clinical end point coding;
• confounding variables and causility

Ethical and governance frameworks:

• the appropriate ethics approval process;
• professional standards;
• safeguarding personal data, confidentiality, privacy,

Technical and clinical skills

By the end of this module the Clinical Scientist in HSST will have a critical understanding of current evidence and its application to the performance and mastery of a range of technical skills and will be able to:

• Work within the appropriate ethical approval to design an experiment to test a specific hypothesis related to their specialism.
• Identify appropriate sample numbers and data to be collected.
• Clean and analyse the data, choosing the most appropriate methods.
• Present a critical appraisal of the results to colleagues.
• Critically appraise a completed or current clinical trial.
• Choose appropriate statistical packages/programming languages to analyse data.
• Interpret the outcome of an analysis with respect to different clinical end points and produce a report and explain the findings to a range of healthcare professionals.

By the end of this module the Clinical Scientist in HSST will be expected to critically reflect and apply in practice a range of clinical and communication skills with respect to experiment design and statistics. They will communicate effectively with clinicians, academics and other healthcare professionals as well as patients and the public, if appropriate and will be able to:

• Lead the design and execution of statistical experiments.
• Communicate their findings to clinical and non-clinical colleagues and the wider community.
• Justify the choice of statistical modelling tools and validation methods.
• Recommend data collection and cleaning strategies to healthcare professionals.
• Contribute to clinical trials design in liaison with an appropriate clinical trials unit.
• Advise clinical colleagues as to the number of samples required for a statistical study.
• Lead and be accountable for the outcomes of analyses from self and team.