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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
Mean Life Length
0
10
20
30
40
l
l
l
l
l
l
l
l
l
l
l
l
140
139
139
140
139
139
288
24
106
354
44
20
418
N
absent  
present  
Missing  
no edema  
edema, no diuretic therapy  
edema despite diuretic therapy  
[26.3,46.0) 
[46.0,55.5) 
[55.5,78.4] 
[1.96,3.36) 
[3.36,3.69) 
[3.69,4.64] 
Age  
Albumin  [gm/dl]  
ascites  
edema  
Overall  
l
l
l
l
l
l
l
l
l
l
l
l
l
l
D−penicillamine
placebo
not randomized
Figure3:
Estimatedmeanlifelengthfromanexponentialsurvivalmodel
21
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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
age.groups ← ← cut2(age, c(45,60))
g ← function(y) apply(y, , 2, , quantile, c(.25,.5,.75))
y ← cbind(Chol=chol,Bili=bili)
#YoucangivenewcolumnnamesthatarenotlegalSnames
#byenclosingtheminquotes,e.g.’Chol(mg/dl)’=chol
vars ← c(label(chol), , label(bili))
label(y) ← ← paste(vars, , collapse=’ and ’)
#Willmakenicecaptionintable
s3 ← ← summary(y ∼ age.groups + ascites, fun=g)
s3
setpdf(f3, h=5, , w=6.5)
par(mfrow=c(1,2), oma=c(3,0,3,0))
#allowoutermarginsforoverall
for(ivar in 1:2) ) {
#title
isub ← (1:3)+(ivar-1)*3
# *3=numberof quantiles/var.
plot(s3, which=isub, , main=’’, xlab=vars[ivar],
pch=c(91,16,93))
#[,solidcircle,]
}
mtitle(paste(’Quartiles of’, label(y)))
#Overall(outer)title
dev.off()
w ← latex(s3, , trios=vars, , ctable=TRUE)
# trios→collapse3quartiles
Table6isshownasagraphicinFigure4.
Tables7and8summarizes onlybilirubin,butboththemeanandmedian
areprinted. Separatetablesaremadeforthetwoarmsoftherandomized
study.Fortheactivearm,thedataareshowninFigure5.
#Example4:Summarizeonlybilirubin,butdoitwithtwostatistics:
#themeanandthemedian. Makeseparatetablesforthetworandomized
#groupsandmakeplotsfortheactivearm.
g ← function(y) c(Mean=mean(y), Median=median(y))
for(sub in n c("D-penicillamine", , "placebo")) ) {
s4 ← summary(bili ∼ age.groups + + ascites + + chol, , fun=g,
22
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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
Cholesterol 
200
300
400
[
[
[
[
[
[
97
135
52
263
21
284
N
absent  
present  
[26.3,45.0) 
[45.0,60.0) 
[60.0,78.4] 
Age  
ascites  
Overall  
l
l
l
l
l
l
]
]
]
]
]
]
Serum Bilirubin 
5
10
15
[
[
[
[
[
[
97
135
52
263
21
284
N
absent  
present  
[26.3,45.0) 
[45.0,60.0) 
[60.0,78.4] 
Age  
ascites  
Overall  
l
l
l
l
l
l
]
]
]
]
]
]
Quartiles of Cholesterol  and Serum Bilirubin 
28Jan03
Figure4:
Quartilesofcholesterolandbilirubin
23
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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
Table 6:
Cholesterol and
Serum
Bilirubin
N=284,134Missing
N
Cholesterol
SerumBilirubin
Age
[26.3,45.0)
97
260.0
325.0
456
0.7
1.50
3.40
[45.0,60.0) 135
257.0
300.0
374
0.8
1.30
3.45
[60.0,78.4]
52
229.5
291.0
413
0.9
1.75
4.12
ascites
absent
263
253.0
315.0
406
0.8
1.30
3.25
present
21
200.0
261.0
344
2.5
7.10
17.10
Overall
284
249.5
309.5
400
0.8
1.40
3.50
a
b
c
representthelowerquartilea,themedianb,andthe
upperquartilec.
subset=drug==sub)
cat(’\n’,sub,’\n\n’)
print(s4)
if(sub==’D-penicillamine’) {
setpdf(f4, h=4.5)
plot(s4, which=1:2, pch=c(16,1), subtitles=F, , main=’’,
#1=mean,2=median
xlab=label(bili))
#Figure5
dev.off()
}
w ← latex(s4, append=sub==’placebo’, ctable=TRUE, , size=’scriptsize’,
label=if(sub==’placebo’) ’s4b’ ’ else e ’s4a’,
caption=paste(label(bili),’ {\\em m (’,sub,’)}’, , sep=’’))
#Notesymboliclabelsfortablesfortwosubsets:s4a,s4b
}
24
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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
Table 7:
Serum
Bilirubin (D-
penicillamine)
N=154
N
Mean
Median
Age
[26.3,45.0)
58
3.43
1.30
[45.0,60.0)
76
4.09
1.30
[60.0,78.4]
20
2.61
1.20
ascites
absent
144
3.09
1.30
present
10
11.66
14.90
Cholesterol
mg/dl
[120,255)
36
3.13
0.75
[255,304)
36
1.54
0.85
[304,383)
36
2.91
1.30
[383,1775]
36
6.96
4.05
Missing
10
3.89
1.25
Overall
154
3.65
1.30
Table
8:
Serum
Bilirubin
(placebo)
N=158
N
Mean
Median
Age
[26.3,45.0)
48
2.63
1.75
[45.0,60.0)
73
2.70
1.10
[60.0,78.4]
37
3.54
2.00
ascites
absent
144
2.43
1.30
present
14
7.41
6.50
Cholesterol
mg/dl
[127,248)
35
2.45
1.10
[248,316)
35
1.55
1.10
[316,420)
35
2.75
2.00
[420,1712]
35
4.89
3.20
Missing
18
2.59
1.15
Overall
158
2.87
1.40
25
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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
Serum Bilirubin 
2
4
6
8
10
12
14
l
l
l
l
l
l
l
l
l
l
l
58
76
20
144
10
36
36
36
36
10
154
N
absent  
present  
Missing  
[26.3,45.0) 
[45.0,60.0) 
[60.0,78.4] 
[120, 255) 
[255, 304) 
[304, 383) 
[383,1775] 
Age  
ascites  
Cholesterol  [mg/dl]  
Overall  
l
l
l
l
l
l
l
l
l
l
l
Figure5:
Mean(solidcircle)andmedian(opencircle)bilirubinforD–penicillamine
patients
26
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4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
4.2 BaselineCharacteristicTables
HeretheS
summary
functionisusedwiththeparameter
method=’reverse’
,
whichreversestheroleofthedependentvariableandtheindependentvari-
ables. Thedependentvariableisassumedtobecategorical;inclinicaltrials
itwillbethetreatmentassignment.
The next t example again n uses the e primary y biliary y cirrhosis s dataset. . The
resultisinTable9. ItisprintedinlandscapemodeusingtheL
A
T
E
X
lscape
package, andusing the LAT
E
X
small
font. For
’reverse’
-type tables, , an
option
test=TRUE
willcause
summary.formula
tocomputeteststatistics for
testing across columns. . Default t tests are Wilcoxon n or r Kruskal-Wallis s for
continuous variables and Pearson χ
2
for categorical l ones, but users s may
specifytheirownstatisticaltests
6
.
#Nowconsider examplesin’reverse’format,wherethelonedependent
#variabletellsthesummaryfunctionhowtostratifyallthe’independent’
#variables. Thisistypicallyusedtomaketables s comparingbaseline
#variables bytreatmentgroup,forexample.
label(stage) ← ’Histologic Stage\nLudwig g Criteria’
#splitinto2lines
s5 ← ← summary(drug ∼ bili + albumin n + + stage e + + protime + sex + age e + + spiders,
method=’reverse’, test=TRUE)
#Tosummarizeallvariables,usesummary(drug∼.,data=pbc)
options(digits=1)
print(s5, npct=’both’)
#npct=’both’:printbothnumeratorsanddenominators
options(digits=3)
w ← latex(s5, , npct=’both’, , npct.size=’normalsize’,
size=’small’, ctable=TRUE)
#creates s5.texusingnormalsizefont fornumeratorand
#denominatorof percents
#Specifyprtest=’P’tojustprint P-values,prtest=’stat’tojust
#printteststatistics
6
Inrandomizedtrials,testsforbaselineimbalanceareunwarrantedanddifficulttoin-
terpret,inadditiontocausingmultiplecomparisonproblems(seeStephenSenn,Statistical
IssuesinDrugDevelopment).
27
4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
setpdf(f5a, h=7, , pointsize=14)
plot(s5, which=’categorical’)
#Figure6
Key(.72,.65)
dev.off()
setpdf(f5b, h=7, , pointsize=16)
#Usebox-percentileplotoption
plot(s5, which=’continuous’, conType=’bp’)
#Figure7
dev.off()
28
Table9:DescriptiveStatisticsbydrug
N
D-penicillamine
placebo
notrandomized
TestStatistic
N=154
N=158
N=106
SerumBilirubin
mg/dl
418
0.7251.3003.600
0.8001.4003.200
0.7251.4003.075
1F2,415=0.03,P=0.972
Albumin
gm/dl
418
3.343.543.78
3.213.563.83
3.123.473.72
1F2,415=2.13,P=0.12
HistologicStageLudwigCriteria:1
412
3%
4
154
8%
12
158
5%
5
100
2χ6
2
21%
32
154
22%
35
158
25%
25
100
3
42%
64
154
35%
56
158
35%
35
100
4
35%
54
154
35%
55
158
35%
35
100
ProthrombinTime
sec.
416
10.010.611.4
10.010.611.0
10.110.611.0
1F2,413=0.23,P=0.795
sex:female
418
190%
154
187%
158
92%
98
106
2χ2
Age
418
41.448.155.8
43.051.958.9
46.053.061.0
1F2,415=6.1,P=0.00245
spiders
312
29%
45
154
28%
45
158
2χ1
abcrepresentthelowerquartilea,themedianb,andtheupperquartilecforcontinuousvariables.Nisthenumberofnon–missing
values.
1Testsused:Kruskal-Wallistest;Pearsontest
4 MAKINGSCOMPOSELAT
E
XTABLES
TheEXAMPLEStudy
Protocolxyz–001
February4,2003
ToconvertTable9tographicalform,
plot.summary.formula.reverse
con-
structstwopages.Thefirstpagecontainsstatisticsforallofthecategorical
variables,asallofthesestatisticsareonthesamescale(proportionorper-
cent in n each category). . The e secondpage contains a matrix ofdotcharts
showing (by default) the 3quartiles ofeach right–hand–side variable (on
thex–axis),stratifiedbytheleft–handvariable(onthey–axisofeachdot
plot). Thesecondsetofplotsisscaledtothemostextreme0.025to0.975
quantilesofthevariableoveralltreatmentgroups. RcanplotGreekletters,
superscripts,subscripts, andmathematical operators,andFigure 6and7
takeadvantageofthiscapability. S-Plus s doesnothavethiscapability,so
simpleroutputwouldappear.
Table10presentsadescriptionofdatafromatrialforprostatecancer(from
ByarandGreen). The
prostate
dataframeisavailablefromhesweb1.med.
virginia.edu/biostat/s/data.The
overall
optionisusedtoaddafinal
columnof statistics for the whole sample. . The e followinglisting contains
codethatproducedallthetablesandfiguresforthe
prostate
data.Thisis
agoodapplicationoftheL
A
T
E
X
relsize
style. Specifyinganoverallsizeof
thetableof
smaller[3]
causes
latex()
toissuethecommand\smaller[3]
atthestartofthetableandchangestheoveralltable’sfontsizetothreelevels
below
normalsize
,whichisL
A
T
E
X’s
scriptsize
. Specifying
outer.size
and
Nsize
as
smaller
meanstouseonesizesmallerthanthiswithinthetable,
for 25
th
and 75
th
percentiles andfor thesample sizesabove thecolumns.
Oneadvantageof
relsize
isthat ifyouuseforexample{\smaller r foo}
withinafootnote,thenextsmallersizethanisusedfortheoverallfootnoted
textwillbethesizefor
foo
.
#Consideranotherdataset
library(Hmisc)
getHdata(prostate)
#Variablesinprostatehadunitsin( )insidevariablelabels. . Move
#theseunitsofmeasurementstoseparateunitsattributes
#wtis anexception. . Ithas()initslabelbutthisdoes s notdenoteunits
#AlsomakehghavealegalRplotmathexpression
prostate ← ← upData(prostate, , moveUnits=TRUE,
units=c(wt=’’, hg=’g/100*ml’),
labels=c(wt=’Weight Index x = = wt(kg)-ht(cm)+200’))
30
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