case2002 {Sleuth3}

Birdkeeping and Lung Cancer

Description

A 1972–1981 health survey in The Hague, Netherlands, discovered an association between keeping pet birds and increased risk of lung cancer. To investigate birdkeeping as a risk factor, researchers conducted a case–control study of patients in 1985 at four hospitals in The Hague (population 450,000). They identified 49 cases of lung cancer among the patients who were registered with a general practice, who were age 65 or younger and who had resided in the city since 1965. They also selected 98 controls from a population of residents having the same general age structure.

Usage

case2002

Format

A data frame with 147 observations on the following 7 variables.

LC

Whether subject has lung cancer

FM

Sex of subject

SS

Socioeconomic status, determined by occupation of the household's principal wage earner

BK

Indicator for birdkeeping (caged birds in the home for more that 6 consecutive months from 5 to 14 years before diagnosis (cases) or examination (control))

AG

Age of subject (in years)

YR

Years of smoking prior to diagnosis or examination

CD

Average rate of smoking (in cigarettes per day)

Source

Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

References

Holst, P.A., Kromhout, D. and Brand, R. (1988). For Debate: Pet Birds as an Independent Risk Factor for Lung Cancer, British Medical Journal 297: 13–21.

Examples

str(case2002)
attach(case2002)
   
## EXPLORATION AND MODEL BUILDING
myCode <- ifelse(BK=="Bird" & LC=="LungCancer","Bird & Cancer", 
  ifelse(BK=="Bird" & LC=="NoCancer","Bird & No Cancer",
  ifelse(BK=="NoBird" & LC=="LungCancer","No Bird & Cancer", "No Bird & No Cancer")))
table(myCode)
if(require(car)){   # Use the car library
scatterplotMatrix(cbind(AG,YR,CD), groups=myCode, diagonal="none",reg.line=FALSE,
  pch=c(15,21,15,21), col=c("dark green","dark green","purple","purple"),
  var.labels=c("Age","Years Smoked","Cigarettes per Day"), cex=1.5) 
}

# Reorder the levels so that the model is for log odds of cancer
LC    <- factor(LC, levels=c("NoCancer","LungCancer"))    
myGlm <- glm(LC ~ FM + SS + AG + YR + CD + BK, family=binomial)
if(require(car)){   # Use the car library
  crPlots(myGlm)  }
# It appears that there's an effect of Years of Smoking and of Bird Keeping
# after accounting for other variables; no obvious effects of other variables

# Logistic regression model building using backward elimination (witholding BK)
myGlm1 <- glm(LC ~ FM + SS + AG + YR + CD, family=binomial)
summary(myGlm1)
myGlm2 <- update(myGlm1, ~ . - SS)        
summary(myGlm2)
myGlm3 <- update(myGlm2, ~ . - CD)   
summary(myGlm3)
myGlm4 <- update(myGlm3, ~ . - FM)   
summary(myGlm4) # Everything left has a small p-value (retain the intercept)


## INFERENCE AND INTERPRETATION
myGlm5 <- update(myGlm4, ~ . + BK)    # Now add bird keeping
summary(myGlm5)
myGlm6 <- update(myGlm5, ~ . + BK:YR + AG:YR) # Try interaction terms
anova(myGlm6,myGlm5) # Drop-in-deviance = 1.61 on 2 d.f.
1 - pchisq(1.61,2)    # p-value = .45: no evidence of interaction
anova(myGlm4,myGlm5)   # Test for bird keeping effect
(1 - pchisq(12.612,1))/2  # 1-sided p-value: 0.0001916391
 
BK <- factor(BK, levels=c("NoBird", "Bird"))  # Make "no bird" the ref level
myGlm5b <- glm(LC ~ AG + YR + BK, family=binomial)   
beta <- myGlm5b$coef  # Extract estimated coefficients
exp(beta[4])   # 3.961248                
exp(confint(myGlm5b,4))   # 1.836764 8.900840  
# Interpretation: The odds of lung cancer for people who kept birds were 
# estimated to be 4 times the odds of lung cancer for people of similar age, sex, 
# smoking history, and socio-economic status who didn't keep birds
# (95% confidence interval for this adjusted odds ratio: 1.8 times to 8.9 times).

# See bestglm library for an alternative variable selection technique. 
 
detach(case2002)