Description Usage Format Source References Examples

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.

1 |

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)

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

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ```
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)
``` |

Sleuth3 documentation built on May 31, 2017, 1:56 a.m.

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