periodontal {DOS2} | R Documentation |

Data from NHANES 2011-2012 containing 441 matched pairs of a daily cigarette smoker and a never smoker, recording the extent of periodontal disease. See Rosenbaum (2017) and Chapter 20 of "Design of Observational Studies", second edition.

`data("periodontal")`

A data frame with 882 observations on the following 12 variables.

`SEQN`

NHANES 2011-2012 sequence number

`female`

=1 for female, 0 for male

`age`

Age in years

`black`

=1 for black, 0 for other

`educf`

Education, in five categories. An ordered factor with levels

`<9`

for less than 9th grade,`9 to 11`

for 9th to 11th grade,`HS/GED`

for high school or GED degree,`SomeCol`

for some college,`College`

for college degree.`income`

Ratio of family income to the poverty level, capped at 5 times the poverty level.

`cigsperday`

Cigarettes smoked per day for daily smokers, 0 for never smokers

`either`

Number of periodonal measurements indicative of periodontal disease.

`neither`

Number of periodonal measurements

`pcteither`

Percent indicative of periodontal disease, =100*either/neither.

`z`

Treatment indicator, 1=daily smoker, 0=never smoker

`mset`

Matched set indicator, 1 to 441.

Excluding wisdom teeth, 6 measurements are taken for each tooth that is present, up to 28 teeth. Following Tomar and Asma (2000), a measurement indicates periodontal disease if either there is a loss of attachment of at least 4mm or a pocket depth of at least 4mm. The first individual has 11 measurements indicative of periodontal disease, out of 106 measurements, so pcteither is 100*11/106 = 10.38 percent. A related data set in DOS2 with bivariate outcome is teeth.

Data are from the National Health and Nutrition Examination Survey 2011-2012 and were used as an example in Rosenbaum (2017). In the second edition of Design of Observational Studies, these data are discussed in Chapter 20, Evidence Factors.

Rosenbaum, P. R. (2015) <https://obsstudies.org/two-r-packages-for-sensitivity-analysis-in-observational-studies/> "Two R packages for sensitivity analysis in observational studies". Observational Studies, 1(1), 1-17.

Rosenbaum, P. R. (2017) <doi:10.1214/17-STS621> "The general structure of evidence factors in observational studies". Statistical Science 32, 514-530.

Tomar, S. L. and Asma, S. (2000) <doi:10.1902/jop.2000.71.5.743> "Smoking attributable periodontitis in the US: Findings from NHANES III". J Periodont 71, 743-751.

"US National Health and Nutrition Examination Survey 2011-2012". www.cdc.gov/nchs/nhanes/index.htm

```
# Figure 1 in Rosenbaum (2017)
data(periodontal)
attach(periodontal)
oldpar<-par()
m<-matrix(1:2,1,2)
layout(m,widths=c(1,2))
boxplot(pcteither[z==1]-pcteither[z==0],ylab="Smoker-Control Difference",
main="(i)",xlab="Matched Pairs",ylim=c(-100,100))
abline(h=0,lty=2)
crosscutplot(cigsperday[z==1],pcteither[z==1]-pcteither[z==0],ylab="Smoker-Control Difference",
xlab="Cigarettes per Day",main="(ii)",ylim=c(-100,100))
abline(h=0,lty=2)
# Sensitivity analysis in Section 2.3 of Rosenbaum (2017)
y<-pcteither[z==1]-pcteither[z==0]
x<-cigsperday[z==1]
senWilcox(y,gamma=2.76)
# The following is the same as sensitivitymw::senmw(y,gamma=2.77,method="p")
sensitivitymult::senm(pcteither,z,mset,gamma=2.77,inner=.5,trim=2)
# The following is the same as sensitivitymw::senmw(y,gamma=3.5,method="p")
sensitivitymult::senm(pcteither,z,mset,gamma=3.5,inner=.5,trim=2)
# Second evidence factor
crosscut(x,y)
crosscut(x,y,gamma=1.6)
# Note, however, that other statistics report greater insensitivity to
# bias by virtue of having larger design sensitivity:
sensitivitymult::senm(pcteither,z,mset,gamma=3.5,inner=1,trim=4)
sensitivitymult::senm(pcteither,z,mset,gamma=4.2,inner=1,trim=4)
senU(y,m1=4,m2=5,m=5,gamma=2.77)
senU(y,m1=6,m2=8,m=8,gamma=2.77)
senU(y,m1=6,m2=8,m=8,gamma=3.5)
detach(periodontal)
par(oldpar)
```

[Package *DOS2* version 0.5.2 Index]