Visualising and modelling changes in categorical variables in longitudinal studies

BMC Medical Research Methodology

Annette Dobson¹  Gita D Mishra¹  Richard Hockey¹  Mark Jones² 

[1] Centre for Longitudinal and Life Course Research, School of Population Health, University of Queensland, Brisbane, Australia;Public Health Building, Herston Road Herston, Brisbane, Qld, 4006, Australia

关键词: Transition probabilities;    Nominal regression;    Marginal distribution;    Longitudinal studies;    Graphical methods;    Categorical variables;   

Others  :  866409
DOI  :  10.1186/1471-2288-14-32

 received in 2013-11-18, accepted in 2014-02-21,  发布年份 2014

【 摘 要 】

Background

Graphical 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.

Methods

The 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.

Results

For 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.

Conclusions

The 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.

【 授权许可】

   
2014 Jones et al.; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Hedeker D, Gibbons R: Longitudinal data analysis. Hoboken, New Jersey: John Wiley and Sons; 2006.
• [2]Swihart B, Caffo B, James B, Strand M, Schwartz B, Punjabi N: Lasagna plots: a saucy alternative to spaghetti plots. Epidemiology 2010, 21:621-625.
• [3]Wilkinson L, Friendly M: The history of the cluster heat map. Am Stat 2009, 63:179-184.
• [4]Dobson AJ, Barnett A: An Introduction to Generalized Linear Models. 3rd edition. Boca Raton, Florida: Chapman & Hall/CRC; 2008.
• [5]Lee C, Dobson AJ, Brown WJ, Bryson L, Byles J, Warner-Smith P, Young AF: Cohort profile: the Australian Longitudinal Study on Women's Health. Int J Epidemiol 2005, 34:987-991.
• [6]Brown WJ, Trost SG: Life transitions and changing physical activity patterns in young women. Am J Prev Med 2003, 25:140-143.
• [7]Ware J, Lipsitz S, Speizer F: Issues in the analysis of repeated categorical outcomes. Stat Med 1988, 7:95-107.
• [8]Long J: Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage Publications; 1997.
• [9]Friendly M: Mosaic displays for multi-way contingency tables. J Am Stat Assoc 1994, 89:190-200.
• [10]Kosara R: Parallel sets: interactive exploration and visual analysis of categorical data. Trans on Visualization and Comput Graph 2006, 12:1-12.
• [11]Schmidt M: Der Einsatz von sankey-diagrammen im stoffstrommanagement. Beitraege der Hochschule Pforzheim 2006. Nr. 124