Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

vas5

Visual analog scale (VAS) data


Description

In the original data 368 patients, measured at 18 times after treatment with one of 7 drug treatments (including placebo), plus a baseline measure (time=0) and one or more pre-baseline measures (time=-1). Here for illustration we will ignore the repeated measure nature of the data and we shall use data from time 5 only (364 observations). The VAS scale response variable, Y, is assumed to be distributed as BEINF(mu,sigma,nu,tau) where any of the distributional parameters mu, sigma, nu and tau are modelled as a constant or as a function of the treatment,

Usage

data(vas5)

Format

A data frame with 364 observations on the following 3 variables.

patient

a factor indicationg the patient

treat

the treatment factor with levels 1 2 3 4 5 6 7

vas

the response variable

Details

The Visual analog scale is used to measure pain and quality of life. For example patients are required to indicate in a scale from 0 to 100 the amount of discomfort they have. This can be easily translated to a value from 0 to 1 and consequently analyzed using the beta distribution. Unfortunately if 0's or 100's are recorded the beta distribution is not appropriate since the values 0 and 1 are not allowed in the definition of the beta distribution. Note that the inflated beta distribution allows values at 0 and 1. This is a mixed distribution (continuous and discrete) having four parameters, nu for modelling the probability at zero p(Y=0) relative to p(0<Y<1), tau for modelling the probability at one p(Y=1) relative to p(0<Y<1), and mu and sigma for modelling the between values, $0<Y<1$, using a beta distributed variable BE(mu,sigma) with mean mu and variance sigma*mu*(1-mu).

Source

The data were provided by Dr. Peter Lane

Examples

data(vas5)

gamlss.data

Data for Generalised Additive Models for Location Scale and Shape

v6.0-1
GPL-2 | GPL-3
Authors
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob Rigby, Fernanda De Bastiani
Initial release
2021-03-18

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.