Learning Outcomes:
At the end of this module students will be able to:
a) critically appraise the quality of data collected using PROMs and QOL instruments, specifically the validity, reliability and internal consistency reliability of instruments using measures of validity, Kappa statistics and Cronbach's alpha;
b) assess normative data for endpoints measured using PROMs and QOL instruments;
c) apply data reduction techniques e.g. Principal Component Analysis (PCA) and Factor Analysis (FA) to suitable datasets;
d) identify and construct latent variables in datasets;
e) use clustering techniques in SPSS for the analysis of PROMs, QOL and QOC data;
f) assess the impact of missing data on analysis and interpretation of results, and methods used to adjust for missing data;
g) apply geographical and spatial software to suitable data
They will understand the purpose, assumptions and implementation of disease modelling, and be aware of the contributions of basic sciences and genomic analyses in unravelling transmission patterns and dynamics.
Indicative Module Content:
Design and assessment of PROMs, measures of QOL and QOC
Techniques for data reduction and extraction
Identification and construction of latent variables
Analysis of data using cluster techniques
Geographic and spatial distribution of disease occurrence
Disease modelling and projections
The contribution of basic science to analysis of population health