Risk Factors for Infection in Caesarian Births

Description

Data from infection from birth by Caesarian section, classified by Risk (two levels), whether Antibiotics were used (two levels) and whether the Caesarian section was Planned or not. The outcome is Infection (three levels).

Usage

data(Caesar)

Format

A 4-dimensional array resulting from cross-tabulating 4 variables for 251 observations. The variable names and their levels are:

Details

Infection is regarded as the response variable here. There are quite a few 0 cells here, particularly when Risk is absent and the Caesarian section was unplanned. Should these be treated as structural or sampling zeros?

Source

Fahrmeir, L. & Tutz, G. (1994). Multivariate Statistical Modelling Based on Generalized Linear Models New York: Springer Verlag, Table 1.1.

See Also

caesar for the same data recorded as a frequency data frame with other variables.

Examples

data(Caesar)
#display table;  note that there are quite a few 0 cells
structable(Caesar)
require(MASS)

# baseline model, Infection as response
Caesar.mod0 <- loglm(~Infection + (Risk*Antibiotics*Planned), 
                     data=Caesar)

# NB: Pearson chisq cannot be computed due to the 0 cells
Caesar.mod0

mosaic(Caesar.mod0, main="Baseline model")

# Illustrate handling structural zeros
zeros <- 0+ (Caesar >0)
zeros[1,,1,1] <- 1
structable(zeros)

# fit model excluding possible structural zeros
Caesar.mod0s <- loglm(~Infection + (Risk*Antibiotics*Planned), 
                      data=Caesar, 
	                    start=zeros)
Caesar.mod0s

anova(Caesar.mod0, Caesar.mod0s, test="Chisq")

mosaic (Caesar.mod0s)

# what terms to add?
add1(Caesar.mod0, ~.^2, test="Chisq")

# add Association of Infection:Antibiotics
Caesar.mod1 <- update(Caesar.mod0, ~ . + Infection:Antibiotics)
anova(Caesar.mod0, Caesar.mod1, test="Chisq")

mosaic(Caesar.mod1, 
       gp=shading_Friendly, 
       main="Adding Infection:Antibiotics")