|Title||Medical Statistics for Medical Physics|
By the end of this module the Clinical Scientist in HSST will be able to apply their knowledge, skills and experience with respect to the range of statistical techniques relevant to Medical Physics and closely related disciplines. They will be expected to be able to design a study protocol and critically interpret and present results. The Clinical Scientist in HSST will be able to demonstrate a critical understanding of current research and how medical statistics contribute to innovative and new approaches within their area of specialisation.
They will be expected to critically evaluate their own response to both normal and complex situations, consistently using the professional attributes and insights required of a Clinical Scientist in HSST.
Knowledge and understanding
The Clinical Scientist in HSST will have the knowledge and comprehension of a variety of statistical tests appropriate to Medical Physics. They will be expected to know how these tests may be used to describe, analyse and summarise results. The Clinical Scientist in HSST should understand and use complex computer statistical routines (e.g. as found in Excel) and stand-alone programmes (e.g. dedicated statistical software packages). Specifically, the Clinical Scientist in HSST should be familiar with and able to select the appropriate techniques relating to:
- Measurement uncertainty.
- Statistical methods for validating and comparing screening tests.
- Receiver Operating Characteristic (ROC) curves and comparison.
- Limitations of regression models in cross-calibration.
- Survival analysis.
- Collection and use of epidemiological data.
- Design and interpretation of cross-sectional case control and cohort studies.
- Principles, calculation and interpretation of odds ratios and risk ratios.
- Definition of incidence, prevalence, mortality rates and standardised mortality rates.
- Principles of disease registration.
- Design and analysis of clinical trials.
- Choice of outcome measure.
- Role and basic principles of meta-analysis and interim analyses.
- Analysis of repeated measures and time series data.
- Factor analysis and principal components analysis.
Technical and clinical skills
By the end of this module the Clinical Scientist in HSST will reflect on their own abilities and demonstrate a critical understanding of current research while demonstrating the ability to apply, adapt and master the following skills and techniques and will (using simulated data where necessary):
- Evaluate uncertainties associated with laboratory or clinical measurements.
- Design a study to compare two diagnostic techniques and analyse the results.
- Design and interpret the results from a cross-sectional case control or cohort study.
- Compare the design and role of phase I–IV clinical trials.
- Perform a calculation of the sample size required for the trial for a given expected improvement in outcome.
- Analyse and present the results of the trial.
- Critically evaluate a meta-analysis.
- Undertake the analysis of repeated measured and time series data.
- Apply factor analysis and principal components analysis to a data set.
By the end of this module the Clinical Scientist in HSST will be expected to critically reflect and apply in practice their scientific skills and clinical knowledge in a variety of clinical situations and will be able to:
- Work with and communicate effectively with relevant clinicians and other healthcare professionals.
- Advise clinical colleagues on the impact of uncertainties in a clinical measurement.
- Advise and communicate effectively the outcome of statistical analysis of clinical trial data with the patient and the public as determined by the scope of practice.
Attitudes and behaviours
By the end of this module the Clinical Scientist in HSST will be expected to evaluate their own response to both normal and complex situations. They will be expected to consistently demonstrate the professional attributes and insights required of a Consultant Clinical Scientist working within the limits of professional competence, referring as appropriate to senior staff, and will:
- Analyse and present data objectively, observing appropriate ethical standards.
- Be aware of the possibility of bias, and take appropriate steps to minimise this.
- Adapt communication skills while working with a range of stakeholders, including patients, clinical and technical staff.