medpar: US national Medicare inpatient hospital database for Arizona...
In msme: Functions and Datasets for "Methods of Statistical Model Estimation"

Description Usage Format Details Source References Examples

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.

1	data(medpar)

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

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

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

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

data(medpar)
glmp <- glm(los ~ hmo + white + factor(type),
            family = poisson, data = medpar)
summary(glmp)
exp(coef(glmp))

ml.p <- ml_glm(los ~ hmo + white + factor(type),
               family = "poisson",
               link = "log",
               data = medpar)

summary(ml.p)

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