Repeated Measurements Analysis
Repeated measurements analysis, and in particular longitudinal data analysis, is one of the two most frequently used types of analysis in my field (Biostatistics) – the other being survival analysis. Starting from this year I will be teaching in my university a new course on regression model for repeated measurements data that is primarily focused on applied statisticians, epidemiologists and clinicians. In general, this type of audience often finds this topic quite difficult, mainly due to the fact that one has to carefully consider the two levels of such data, namely, how to model longitudinal evolutions, and how to model correlations. On top of that, many of the researchers following this course have been primarily exposed to SPSS, making the transition to R that I will be using in the course somewhat more demanding.
Based on the considerations mentioned above, when I was developing the course I was thinking of ways to facilitate both the understanding of the key concepts of repeated measurements analysis, and how to effectively explain the use of R to analyze such data. The answer to both questions was to utilize the great capabilities of shiny. I have created an app the replays all analyses done in the course – a snapshot shown below.
The students can select a chapter and a section, see the code used in that section in the ‘Code’ tab, and examine the output in the ‘Output’ tab. The slides of the course are also integrated in the app, and can been seen in the ‘Slides’ tab. The ‘Help’ tab explains the basic usage of the main functions used in the selected chapter. To further enhance understanding of some key concepts, such as how random effects capture correlations and how longitudinal evolutions are affected by the levels of baseline covariates, the app allows to interactively change values for some parameters that control these features. The app also includes four practicals aimed at Chapter 2 that introduces marginal models for continuous data, Chapter 3 that explains linear mixed effects models, Chapter 4 the presents the framework of generalized estimating equations, and Chapter 5 the presents generalized linear mixed effects models, respectively. Chapter 6 focuses on explaining the issues with incomplete data in longitudinal studies. For each practical the students may reveal the answer to specific questions they have trouble solving, or download a whole R markdown report with a detailed explanation of the solutions.
The app is available in my dedicated GitHub repository for this course, and can be invoked using the command (assuming that you have the aforementioned packages installed):