On this R-data statistics page, you will find information about the medpar data set which pertains to medpar. The medpar data set is found in the COUNT R package. You can load the medpar data set in R by issuing the following command at the console data("medpar"). This will load the data into a variable called medpar. If R says the medpar data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the medpar R data set. The size of this file is about 41,059 bytes.

medpar

Description

The US national Medicare inpatient hospital database is referred to as the Medpar data, which is prepared yearly from hospital filing records. Medpar files for each state are also prepared. The full Medpar data consists of 115 variables. The national Medpar has some 14 million records, with one record for each hospilitiztion. The data in the medpar file comes from 1991 Medicare files for the state of Arizona. The data are limited to only one diagnostic group (DRG 112). Patient data have been randomly selected from the original data.

Usage

data(medpar)

Format

A data frame with 1495 observations on the following 10 variables.

los

length of hospital stay

hmo

Patient belongs to a Health Maintenance Organization, binary

white

Patient identifies themselves as Caucasian, binary

died

Patient died, binary

age80

Patient age 80 and over, binary

type

Type of admission, categorical

type1

Elective admission, binary

type2

Urgent admission,binary

type3

Elective admission, binary

provnum

Provider ID

Details

medpar is saved as a data frame. Count models use los as response variable. 0 counts are structurally excluded

Source

1991 National Medpar data, National Health Economics & Research Co.

References

Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC first used in Hardin, JW and JM Hilbe (2001, 2007), Generalized Linear Models and Extensions, Stata Press

Examples

library(MASS)
library(msme)
data(medpar)
glmp <- glm(los ~ hmo + white + factor(type), family=poisson, data=medpar)
summary(glmp)
exp(coef(glmp))
nb2 <- nbinomial(los ~ hmo + white + factor(type), data=medpar)
summary(nb2)
exp(coef(nb2))
glmnb <- glm.nb(los ~ hmo + white + factor(type), data=medpar)
summary(glmnb)
exp(coef(glmnb))

--

Dataset imported from https://www.r-project.org.