hospcosts: Hospital Costs data

In robmixglm: Robust Generalized Linear Models (GLM) using Mixtures

Hospital Costs data

Description

Data for the analysis in Beath (2018), previously analysed in Marazzi and Yohai (2004), Cantoni and Ronchetti (2006) and Heritier et al (2009). The data is for 100 patients hospitalised at the Centre Hospitalier Universitaire Vaudois in Lausanne, Switzerland for "medical back problems" (APDRG 243).

Usage

hospcosts

Format

A data frame with 100 observations on the following 9 variables.

id

patient id

costs

cost of stay in Swiss francs

los

length of stay in days

adm

admission type, 0 = planned, 1 = emergency

ins

insurance type, 0 = regular, 1 = private

age

age in years

sex

sex, 0 = female, 1 = male

dest

discharge destination, 0 = another health institution, 1 = home

loglos

log of length of stay

Source

Heritier et al (2009)

References

Cantoni, E., & Ronchetti, E. (2006). A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures. Journal of Health Economics, 25(2), 198213. http://doi.org/10.1016/j.jhealeco.2005.04.010

Heritier, S., Cantoni, E., Copt, S. and Victoria-Feser, M-P (2009). Robust Methods in Biostatistics. Wiley.

Marazzi, A., & Yohai, V. J. (2004). Adaptively truncated maximum likelihood regression with asymmetric errors. Journal of Statistical Planning and Inference, 122(12), 271291. http://doi.org/10.1016/j.jspi.2003.06.011

Examples

hospcosts.robustmix <- robmixglm(costs~adm+age+dest+ins+loglos+sex, family = "gamma", 
    data = hospcosts, cores = 1)
summary(hospcosts.robustmix)

robmixglm documentation built on Sept. 30, 2024, 9:34 a.m.