%0 Journal Article
%T Visualising disease progression on multiple variables with vector plots and path plots
%A Stanley E Lazic
%A Sarah L Mason
%A Andrew W Michell
%A Roger A Barker
%J BMC Medical Research Methodology
%D 2009
%I BioMed Central
%R 10.1186/1471-2288-9-32
%X A variety of vector plots and a path plot have been developed for this purpose, and data from a longitudinal Huntington's disease study are used to illustrate the utility of these graphical methods for exploratory data analysis.Initial and final values for three outcome variables can be easily visualised per patient, along with the change in these variables over time. In addition to the disease trajectory, the path individual patients take from initial to final observation can be traced. Categorical variables can be coded with different types of vectors or paths (e.g. different colours, line types, line thickness) and separate panels can be used to include further categorical or continuous variables, allowing clear visualisation of further information for each individual. In addition, summary statistics such as mean vectors, bivariate interquartile ranges and convex polygons can be included to assist in interpreting trajectories, comparing groups, and detecting multivariate outliers.Vector and path plots are useful graphical methods for exploratory data analysis when individual-level information on multiple variables over time is desired, and they have several advantages over plotting each variable separately.Clinical studies typically measure multiple outcomes on patients as well as record information on patient characteristics such as age, sex, genotype, disease severity, and age of onset. Many such studies are longitudinal, where initial or baseline values are obtained, and then patients are followed over time to observe how the disease progresses. Often the research question involves a comparison of two or more groups, such as an experimental and control group, or a comparison of progression between subgroups of patients with the disease. Numerous methods are available to analyse multiple observations on subjects over time, such as repeated measures ANOVA, multivariate ANOVA, derived-variable or summary-measure analysis (e.g. slopes, intercepts, area under the c
%U http://www.biomedcentral.com/1471-2288/9/32