PHPS41110

This module builds on epidemiological knowledge and skills acquired in a basic epidemiology course such as PHPS40010 (Principles of Epidemiology). The emphasis in the module is on a thorough understanding of different types of data incorporated into epidemiological and clinical research, with specific focus on:

a) Patent Reported Outcomes data, including the development and uses of Patient Reported Outcome Measures (PROMs) and Quality of Life (QOL) measures for the subjective reporting and objective assessment of a broad range of health, disease and disability states. These include assessments of: General health status; Psychological wellbeing; Social health and social support;
Physical handicap and disability; Mental health states; Mental status; and Pain and illness assessments;
b) Quality of Care (QOC) data, incorporating assessments of the Donabedian components of quality of care;
c) Aggregate data used to identify geographic distribution and spatial clustering of disease;
d) The data requirements and applications of disease modelling and disease projections; and
e) The integration of data from basic science and epidemiology to identify causal associations and transmission patterns.

Analytical techniques to be used will include:
a) Assessment of the quality of data generated 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) Establishment of population normative data for endpoints measured using PROMs and QOL instruments;
c) Data reduction techniques e.g. Principal Component Analysis (PCA) and Factor Analysis (FA);
d) Methods for identification and construction of latent variables in datasets;
e) Use of Clustering techniques for the analysis of PROMs, QOL and QOC data;
f) Impact of missing data on analysis and interpretation of results, and methods used to adjust for missing data;
g) Orientation to geographical and spatial software.