Analysis of Repeated Measurements

Problems and advantages with dependent observations. Summary measures (Area under curve (AUC), coefficient of slope, min/max value). Covariance and correlation. Adjustment for baseline measurement. Variance components. Linear mixed effect models. Use of relevant software (Stata)

After completing the course, the student should be able to: • Understand the nature of dependency in clustered and repeated measurements, and how this dependency alters the approach to statistical analysis and modeling; as well as the consequences of not taking this information into account • Identify clusters and potential dependency by inspecting the design and/or viewing the resulting data set • Understand the principles of experimental design in which experimental factors vary both between and within clusters • Perform simple, descriptive analyses such as obtaining sample covariance and correlation, and corresponding graphical plots such as scatter plots, to illuminate key features of data with clustered and/or repeated observations • Perform and interpret variance component estimation, and adjustment for baseline value in randomized trials using analysis-of-covariance • Perform and interpret linear mixed regression models with random intercept, with random intercept and random slopes for covariates; using appropriate software • Understand the special nature of observations made along the time axis, including the possibility of autoregressive residuals

Lectures and guided excercises. Course information will be published at http://folk.ntnu.no/eiriksko/KLMED8008/KLMED8008v16.html before the course starts.