Artificially generated hospital data. Fictional patients at 10 fictional hospitals, with LOS, Age and Date status data Data were generate to learn Generalized Linear Models (GLM) concepts, modelling either Death or LOS.
data(LOS_model)Data frame with five columns
A fictional patient ID number
A factor representing one of ten fictional hospital trusts, for example Trust1
Age in years of each fictional patient
In-hospital length of stay in days. The difference between admission and discharge date in dates
Binary for death status: 0 = survived, 1= died in hospital
Generated by Chris Mainey, Feb-2019
data(LOS_model)
model1 <- glm(Death ~ Age + LOS, data = LOS_model, family = "binomial")
summary(model1)
#>
#> Call:
#> glm(formula = Death ~ Age + LOS, family = "binomial", data = LOS_model)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -2.435539 0.370818 -6.568 5.1e-11 ***
#> Age 0.003602 0.006372 0.565 0.5719
#> LOS 0.127344 0.044359 2.871 0.0041 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 279.78 on 299 degrees of freedom
#> Residual deviance: 267.06 on 297 degrees of freedom
#> AIC: 273.06
#>
#> Number of Fisher Scoring iterations: 4
#>
# Now with an Age, LOS, and Age*LOS interaction.
model2 <- glm(Death ~ Age * LOS, data = LOS_model, family = "binomial")
summary(model2)
#>
#> Call:
#> glm(formula = Death ~ Age * LOS, family = "binomial", data = LOS_model)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -4.516296 0.747036 -6.046 1.49e-09 ***
#> Age 0.042619 0.011875 3.589 0.000332 ***
#> LOS 0.544862 0.135868 4.010 6.07e-05 ***
#> Age:LOS -0.006988 0.001918 -3.643 0.000269 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 279.78 on 299 degrees of freedom
#> Residual deviance: 247.94 on 296 degrees of freedom
#> AIC: 255.94
#>
#> Number of Fisher Scoring iterations: 5
#>