aep: The Hospital Stay Data

In gamlss.data: Data for Generalised Additive Models for Location Scale and Shape

The Hospital Stay Data

Description

The data, 1383 observations, are from a study at the Hospital del Mar, Barcelona during the years 1988 and 1990, Gange et al. (1996).

Usage

data(aep)

Format

A data frame with 1383 observations on the following 8 variables.

los

the total number of days patients spent in hospital: a discrete vector

noinap

the number of inappropriate days spent in hospital: a discrete vector

loglos

the log(los/10): a numeric vector

sex

the gender of patient: a factor with levels 1=male, 2=female

ward

the type of ward in the hospital: a factor with levels 1=medical 2=surgical, 3=others

year

the specific year 1988 or 1990: a factor with levels 88 and 90

age

the age of the patient subtracted from 55: a numeric vector

y

the response variable a matrix with 2 columns, the first is noinap the second is equal to (los-noinap)

Details

Gange et al. (1996) used a logistic regression model for the number of inappropriate days (noinap) out of the total number of days spent in hospital (los), with binomial and beta binomial errors and found that the later provided a better fit to the data. They modelled both the mean and the dispersion of the beta binomial distribution (BB) as functions of explanatory variables

Source

Gange, S. J. Munoz, A. Saez, M. and Alonso, J. (1996)

References

Gange, S. J. Munoz, A. Saez, M. and Alonso, J. (1996) Use of the beta-binomial distribution to model the effect of policy changes on appropriateness of hospital stays. Appl. Statist, 45, 371–382

Examples

data(aep)
attach(aep)
pro<-noinap/los
plot(ward,pro)
rm(pro)
detach(aep)

gamlss.data documentation built on May 29, 2024, 10:46 a.m.