Recovery from Elective Herniorrhaphy

Description

A data set on measures of post-operative recovery of 32 patients undergoing an elective herniorrhaphy operation, in relation to pre-operative measures.

Format

A data frame with 32 observations on the following 9 variables.

age

patient age

sex

patient sex, a factor with levels f m

pstat

physical status (ignoring that associated with the operation). A 1-5 scale, with 1=perfect health, 5=very poor health.

build

body build, a 1-5 scale, with 1=emaciated, 2=thin, 3=average, 4=fat, 5=obese.

cardiac

preoperative complications with heart, 1-4 scale, with 1=none, 2=mild, 3=moderate, 4=severe.

resp

preoperative complications with respiration, 1-4 scale, with 1=none, 2=mild, 3=moderate, 4=severe.

leave

condition upon leaving the recovery room, a 1-4 scale, with 1=routine recovery, 2=intensive care for observation overnight, 3=intensive care, with moderate care required, 4=intensive care, with moderate care required.

los

length of stay in hospital after operation (days)

nurse

level of nursing required one week after operation, a 1-5 scale, with 1=intense, 2=heavy, 3=moderate, 4=light, 5=none (?); see Details

Details

leave, nurse and los are outcome measures; the remaining variables are potential predictors of recovery status.

The variable nurse is recorded as 1-4, with remaining (20) entries entered as "-" in both sources. It is not clear whether this means "none" or NA. The former interpretation was used in constructing the R data frame, so nurse==5 for these observations. Using Hernior$nurse[Hernior$nurse==5] <- NA would change to the other interpretation, but render nurse useless in a multivariate analysis.

The ordinal predictors could instead be treated as factors, and there are also potential interactions to be explored.

Source

Mosteller, F. and Tukey, J. W. (1977), Data analysis and regression, Reading, MA: Addison-Wesley. Data Exhibit 8, 567-568. Their source: A study by B. McPeek and J. P. Gilbert of the Harvard Anesthesia Center.

References

Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J. and Ostrowski, E. (1994), A Handbook of Small Data Sets, Number 484, 390-391.

Examples

library(car)
data(Hernior)
str(Hernior)
Hern.mod <- lm(cbind(leave, nurse, los) ~ 
               age + sex +  pstat +  build + cardiac + resp, data=Hernior)
car::Anova(Hern.mod, test="Roy") # actually, all tests are identical

# test overall regression
print(linearHypothesis(Hern.mod, c("age", "sexm", "pstat", "build", "cardiac", "resp")), SSP=FALSE)

# joint test of age, sex & caridac
print(linearHypothesis(Hern.mod, c("age", "sexm", "cardiac")), SSP=FALSE)

# HE plots
clr <- c("red", "darkgray", "blue", "darkgreen", "magenta", "brown", "black")
heplot(Hern.mod, col=clr)
pairs(Hern.mod, col=clr)

## Enhancing the pairs plot ...
# create better variable labels
vlab <- c("LeaveCondition\n(leave)", 
          "NursingCare\n(nurse)", 
          "LengthOfStay\n(los)")
# Add ellipse to test all 5 regressors simultaneously
hyp <- list("Regr" = c("age", "sexm", "pstat", "build", "cardiac", "resp"))
pairs(Hern.mod, hypotheses=hyp, col=clr, var.labels=vlab)

## Views in canonical space for the various predictors
if (require(candisc)) {
	Hern.canL <- candiscList(Hern.mod)
	plot(Hern.canL, term="age")
	plot(Hern.canL, term="sex")
	plot(Hern.canL, term="pstat")  # physical status
}