Dataset Information

Visualising disease progression on multiple variables with vector plots and path plots.

ABSTRACT:

Background

It is often desirable to observe how a disease progresses over time in individual patients, rather than graphing group averages; and since multiple outcomes are typically recorded on each patient, it would be advantageous to visualise disease progression on multiple variables simultaneously.

Methods

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.

Results

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.

Conclusion

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.

SUBMITTER: Lazic SE

PROVIDER: S-EPMC2693505 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Publications

Visualising disease progression on multiple variables with vector plots and path plots.

Lazic Stanley E SE Mason Sarah L SL Michell Andrew W AW Barker Roger A RA

BMC medical research methodology 20090527

<h4>Background</h4>It is often desirable to observe how a disease progresses over time in individual patients, rather than graphing group averages; and since multiple outcomes are typically recorded on each patient, it would be advantageous to visualise disease progression on multiple variables simultaneously.<h4>Methods</h4>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 ...[more]

PMID: 19473528

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Project description: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.

Project description:BackgroundAccurate volume measurement is important in the management of patients with congestive heart failure or renal insufficiency. A bioimpedance analyser can estimate total body water in litres and has been widely used in clinical practice due to its non-invasiveness and ease of results interpretation. To change impedance data to volumetric data, bioimpedance analysers use equations derived from data from healthy subjects, which may not apply to patients with other conditions. Bioelectrical impedance vector analysis (BIVA) was developed to overcome the dependence on those equations by constructing vector plots using raw impedance data. BIVA requires normal reference plots for the proper interpretation of individual vectors. The aim of this study was to construct normal reference vector plots of bioelectrical impedance for Koreans.MethodsBioelectrical impedance measurements were collected from apparently healthy subjects screened according to a comprehensive physical examination and medical history performed by trained physicians. Reference vector contours were plotted on the RXc graph using the probability density function of the bivariate normal distribution. We further compared them with those of other ethnic groups.ResultsA total of 242 healthy subjects aged 22 to 83 were recruited (137 men and 105 women) between December 2015 and November 2016. The centers of the tolerance ellipses were 306.3 Ω/m and 34.9 Ω/m for men and 425.6 Ω/m and 39.7 Ω/m for women. The ellipses were wider for women than for men. The confidence ellipses for Koreans were located between those for Americans and Spaniards without overlap for both genders.ConclusionThis study presented gender-specific normal reference BIVA plots and corresponding tolerance and confidence ellipses on the RXc graph, which is important for the interpretation of BIA-reported volume status in patients with congestive heart failure or renal insufficiency. There were noticeable differences in reference ellipses with regard to gender and ethnic groups.

Dataset Information

Visualising disease progression on multiple variables with vector plots and path plots.

Background

Methods

Results

Conclusion

Publications

Visualising disease progression on multiple variables with vector plots and path plots.

Similar Datasets

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