HRQoLplot: Spider plot of the dimensions of the Short Form-36 Health...

Description Usage Arguments Details Author(s) References See Also Examples

View source: R/HRQoLplot.R

Description

This function creates a spider plot with the 8 health realted quality of life dimensions provided by the Short Form-36 Health Survey.

Usage

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HRQoLplot(data,legend=FALSE,title="Short Form-36 Health Survey",
  dimlabel.cex=NULL,legend.cex=1,linewidth=3,title.cex=1,lty=1)

Arguments

data

a data frame with each column relative to the observations of each SF-36 dimension. The columns of the data frame must be introduced in the following order:

  1. column -> Physical Functioning

  2. column -> Role Physical

  3. column -> Body Pain

  4. column -> General Health

  5. column -> Vitality

  6. column -> Social Functioning

  7. column -> Role Emotional

  8. column -> Mental Health

legend

logical parameter, if TRUE the legend with the name of the rows of the data will appear. Default FALSE.

title

the title of the plot. Default "Short Form-36 Health Survey".

dimlabel.cex

font size magnification for the labels of the dimension in the plot. If NULL, the font size is fixed at text()'s default. Default NULL.

legend.cex

font size of legend text(). Default 1.

linewidth

the width of the lines of the plot. Default 3.

title.cex

the font size of the title. Default 1.

lty

the line type of the plot and the legend. Default 1.

Details

The Short Form-36 Health Survey is a commonly used technique to measure the Health Related Quality of Life (HRQoL) in chronich diseases. It was developed within the Medical Outcomes Study (Ware et al. (1993)). It measures generic HRQoL concepts and provides an objective way to measure HRQoL from the patients point of view by scoring standardized responses to standardized questions. The validity and reability of this instrument has been broadly tested (Stansfeld et al. (1997)). The SF-36 has 36 items, with different answer options. It was constructed to respresent eight health dimensions, which are physical functioning (PF), role physical (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Each item is assigned to a unique helath dimension. Each of the multi-item dimensions contains two to ten items. The first four dimensions are mainly physical, whereas the last four measure mental aspects of HRQoL. The resulting raw scores are tipically transformed to standardized scale scores from 0 to 100, where a higher score indicates a better health status.

Arostegui et al. (2013) proposed a recoding methodology for the Short Form-36 Health Survey (SF-36) dimensions in order to apply a beta-binomial distribution. The HRQoLplot function plots the SF-36 dimensions scores in a spider plot. Each axis of the plot refers to an expecific SF-36 dimension. Hence, the order of the dimensions in the data frame object of the function has been stablished as it has been explained in Arguments section. Each observation of the data frame, the value of each observation in all the dimensions, is drawn with a line of a different color in the plot. The plot shows the name of each dimension and the maximum number of scores each dimension can obtain in each axis of the plot.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

This function depends on the function radarchart of the package fmsb created by Minato Nakazawa.

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2013): On the recoding of continuous and bounded indexes to a binomial form: an application to quality-of-life scores, Journal of Applied Statistics, 40, 563-583

See Also

As it is said in the author section, the function depends on the function radarchart of the package fmsb

Examples

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set.seed(5)
# We insert the columns in the order that has been determined:
n <- c(20,4,9,20,20,8,3,13)
k=3
p=runif(8,0,1)
phi <- runif(8,1,3)
dat <- data.frame(
  PF=rBB(k,n[1],p[1],phi[1]),
  RP=rBB(k,n[2],p[2],phi[2]),
  BP=rBB(k,n[3],p[3],phi[3]),
  GH=rBB(k,n[4],p[4],phi[4]),
  VT=rBB(k,n[5],p[5],phi[5]),
  SF=rBB(k,n[6],p[6],phi[6]),
  RE=rBB(k,n[7],p[7],phi[7]),
  MH=rBB(k,n[8],p[8],phi[8]))

rownames(dat) <- c("ID1", "ID2", "ID3")
HRQoLplot(dat,TRUE)

Example output



PROreg documentation built on July 1, 2020, 7:02 p.m.

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