Abstract
BackgroundGraphical techniques can provide visually compelling insights into complex data patterns. In this paper we present a type of lasagne plot showing changes in categorical variables for participants measured at regular intervals over time and propose statistical models to estimate distributions of marginal and transitional probabilities.MethodsThe plot uses stacked bars to show the distribution of categorical variables at each time interval, with different colours to depict different categories and changes in colours showing trajectories of participants over time. The models are based on nominal logistic regression which is appropriate for both ordinal and nominal categorical variables. To illustrate the plots and models we analyse data on smoking status, body mass index (BMI) and physical activity level from a longitudinal study on women’s health. To estimate marginal distributions we fit survey wave as an explanatory variable whereas for transitional distributions we fit status of participants (e.g. smoking status) at previous surveys.ResultsFor the illustrative data the marginal models showed BMI increasing, physical activity decreasing and smoking decreasing linearly over time at the population level. The plots and transition models showed smoking status to be highly predictable for individuals whereas BMI was only moderately predictable and physical activity was virtually unpredictable. Most of the predictive power was obtained from participant status at the previous survey. Predicted probabilities from the models mostly agreed with observed probabilities indicating adequate goodness-of-fit.ConclusionsThe proposed form of lasagne plot provides a simple visual aid to show transitions in categorical variables over time in longitudinal studies. The suggested models complement the plot and allow formal testing and estimation of marginal and transitional distributions. These simple tools can provide valuable insights into categorical data on individuals measured at regular intervals over time.
Highlights
Graphical techniques can provide visually compelling insights into complex data patterns
In this paper we describe a form of lasagne plot for showing changes in categorical variables for participants in longitudinal studies
Smoking status is categorised as never smoker, current smoker, or ex-smoker; body mass index (BMI) is categorised as healthy or underweight (BMI ≤ 25.0), overweight (25.0 < BMI ≤ 30.0), or obese (BMI > 30.0); and physical activity is categorised as low/ sedentary, moderate activity or high activity [5]
Summary
Graphical techniques can provide visually compelling insights into complex data patterns. With the increasing interest in longitudinal and lifecourse studies, it is desirable to develop graphical techniques for visualising and exploring complex patterns within groups of participants over the course of a study. A well-known method for graphically displaying longitudinal data is the spaghetti plot [1] where individual subject’s measurements of a repeated outcome are shown chronologically over time. This graphical method is simple and effective at showing changes in a variable for individuals. It is only appropriate for continuous data and small sample sizes. Plotting a large number of trajectories can lead to multiple intersecting lines that fail to show important patterns in the data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
