FEprovideR (version 1.1)

fe.prov: Fit logistic fixed-effect model with high-dimensional predictors

Description

fe.prov fits a fixed-effect logistic model using structured profile likelihood algorithm. Standardized readmission ratios (SRRs) are also computed. Go to Github for a tutorial.

Usage

fe.prov(data, Y.char, Z.char, prov.char, tol = 1e-05, null = "median")

Arguments

data

prepared data.frame. Use fe.data.prep to prepare the raw data

Y.char

name of the response variable from data as a character string

Z.char

names of covariates from data as vector of character strings

prov.char

name of provider IDs variable as a character string

tol

tolerance level for convergence. Default is 1e-5

null

use median for null comparison

Value

An object of class fe.prov, which is just a List object with the following named elements:

• beta: a vector of fixed effect estimates

• Obs: a vector of responses for included providers

• Exp: a vector of expected probabilities of readmission within 30 days of discharge

• iter: number of iterations needed for convergence

• beta.max.diff: value of the stopping criterion

• df.prov:

df.prov is a data.frame of provider-level information with the following items:

• Obs: provider-level observed number of readmissions within 30 days

• Exp: expected number of readmissions within 30 days

• SRR: standardized readmission ratios for each hospital

• gamma: a vector of provider effect estimates for included hospitals

References

He, K., Kalbfleisch, J.D., Li, Y. and Li, Y., 2013. Evaluating hospital readmission rates in dialysis facilities; adjusting for hospital effects. Lifetime data analysis, 19(4), pp.490-512.

See Also

fe.data.prep, test.fe.prov, funnel.SRR, confint.fe.prov

Examples

# NOT RUN {
# Name input variables and other parameters
# a small positive number specifying stopping
# criterion of Newton-Raphson algorithm
tol <- 1e-5
Y.char <- 'Y'
prov.char <- 'prov.ID'
Z.char <- paste0('z', 1:3)
data(hospital_prepared) # build in data set
fe.ls <- fe.prov(hospital_prepared, Y.char, Z.char, prov.char, tol) # model fitting

# }