Sample description
means=0
values=length(numericlist)
for(i in c(1:values)){
means[i]=mean(numval[,i])
}
sds=0
for(i in c(1:values)){
sds[i]=sd(numval[,i])
}
mins=0
for(i in c(1:values)){
mins[i]=min(numval[,i])
}
maxs=0
for(i in c(1:values)){
maxs[i]=max(numval[,i])
}
numericas=data.frame(variavel=numnames,mean=means,sd=sds,min=mins,max=maxs)
values=length(categoricallist)
tblFun <- function(x){
tbl <- table(x)
res <- cbind(tbl,round(prop.table(tbl)*100,2))
colnames(res) <- c('Count','Percentage')
res
}
## variavel mean sd min max
## 1 mg.kg.h 2.6090909 0.39015149 1.96 3.64
## 2 age 48.0000000 16.11269833 19.00 78.00
## 3 weight 75.3636364 12.56808299 48.00 105.00
## 4 height 1.6581818 0.08336017 1.52 1.80
## 5 bmi 27.3531818 5.53231320 15.85 41.01
## 6 sistolic.basal 132.9090909 20.63725040 100.00 179.00
## 7 diastolic.basal 81.3181818 13.23914241 43.00 106.00
## 8 hr.basal 78.1818182 10.02205360 61.00 104.00
## 9 spo2.basal 98.0454545 2.12641600 92.00 100.00
## 10 tube.diameter 7.3181818 0.47673129 7.00 8.00
## 11 laryngoscopies 1.0909091 0.29424494 1.00 2.00
## 12 cuff.pressure.initial 23.2272727 5.74814105 10.00 35.00
## 13 remifentanil.total 284.9363636 352.30523231 0.00 1192.00
## 14 lidocaine.total 350.7909091 99.87987980 124.00 518.00
## 15 cuff.pressure.final 20.3181818 6.66466420 10.00 30.00
## 16 sistolic.final 127.0909091 20.13267680 87.00 167.00
## 17 diastolic.final 81.5000000 12.24258765 50.00 103.00
## 18 spo2.final 98.4545455 1.50324325 95.00 100.00
## 19 hr.final 77.0909091 11.55075380 58.00 105.00
## 20 extubation.minutes 9.1818182 4.01943331 4.00 20.00
## 21 sistolic.variation -5.8181818 22.06405472 -47.00 37.00
## 22 diastolic.variation 0.1818182 17.53536933 -40.00 47.00
## 23 hr.variation -1.0909091 17.16837927 -46.00 28.00
## 24 cuff.pressure.variation -2.9090909 3.84099554 -11.00 5.00
## 25 spo2.variation 0.4090909 1.96781462 -3.00 6.00
## $ASA
## Count Percentage
## 1 8 36.36
## 2 12 54.55
## 3 2 9.09
## $procedure
## Count Percentage
## Abdominal wall hernia 1 4.55
## breast setorectomy 2 9.09
## Corneal transplantation 7 31.82
## Inguinal hernia repeair 2 9.09
## Laparoscopic cholecistectomy 8 36.36
## Laparoscopy 1 4.55
## trepanation 1 4.55
## $bucking
## Count Percentage
## FALSE 13 59.09
## TRUE 9 40.91
## $dipyrone.2g
## Count Percentage
## TRUE 22 100
## $ondansetron.8mg
## Count Percentage
## FALSE 6 27.27
## TRUE 16 72.73
## $tenoxicam.40mg
## Count Percentage
## FALSE 3 13.64
## TRUE 19 86.36
## $dexamethasone.10mg
## Count Percentage
## FALSE 14 63.64
## TRUE 8 36.36
Main result and analysis
#Sucess:
g1=subset(data,data$bucking==FALSE)
#Failure:
g2=subset(data,data$bucking==TRUE)
test.df=data.frame(responseSequence=!data$bucking,doseSequence=data$mg.kg.h)
plot(mg.kg.h~id,data=data,xlab="Patient number",ylab="Lidocaine mcg/kg.h")
lines(data$mg.kg.h~data$id,data=data)
points(g1$mg.kg.h~g1$id,col="black",bg="white",pch=21)
points(g2$mg.kg.h~g2$id,col="black",bg="black",pch=21)
testPava.df <- preparePava(test.df)
print(testPava.df)
## naiveProbability pavaProbability nEvents nTrials nDoses
## 1 0.000 0.0000000 0 1 1.96
## 2 0.400 0.3846154 2 5 2.24
## 3 0.375 0.3846154 3 8 2.52
## 4 1.000 1.0000000 5 5 2.80
## 5 1.000 1.0000000 1 1 3.08
## 6 1.000 1.0000000 1 1 3.36
## 7 1.000 1.0000000 1 1 3.64
test.boot <- boot(data = test.df, statistic = bootIsotonicRegression, R = 9999, sim = 'parametric', ran.gen = bootIsotonicResample, mle = list(baselinePava = testPava.df, firstDose = 3.64, PROBABILITY.GAMMA = 0.5), baselinePava = testPava.df, PROBABILITY.GAMMA = 0.5)
r2=bootBC.ci(test.boot$t0[3], test.boot$t[, 3])
ici=r2$`2.5% Bias Corrected Lower Bound`
ed50=r2$`Original Statistic` #ED50
print(ed50)
## [1] 2.5725
icm=r2$`97.5% Bias Corrected Upper Bound`
boot.ci(test.boot, type = c('norm', 'basic', 'perc'), conf = 0.95, index = 3)
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 9999 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = test.boot, conf = 0.95, type = c("norm", "basic",
## "perc"), index = 3)
##
## Intervals :
## Level Normal Basic Percentile
## 95% ( 2.372, 2.907 ) ( 2.502, 2.965 ) ( 2.180, 2.643 )
## Calculations and Intervals on Original Scale
Potential confounding factors
Tube diameter
##
## 7 8
## FALSE 9 4
## TRUE 6 3
##
## Fisher's Exact Test for Count Data
##
## data: table(data$bucking, data$tube.diameter)
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1186516 9.6220694
## sample estimates:
## odds ratio
## 1.118965
Laryngoscopy count
##
## 1 2
## FALSE 11 2
## TRUE 9 0
##
## Fisher's Exact Test for Count Data
##
## data: table(data$bucking, data$laryngoscopies)
## p-value = 0.4935
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.000000 7.709768
## sample estimates:
## odds ratio
## 0
Ondansetron 8mg
##
## FALSE TRUE
## FALSE 3 10
## TRUE 3 6
##
## Fisher's Exact Test for Count Data
##
## data: table(data$bucking, data$ondansetron.8mg)
## p-value = 0.655
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.06023885 6.16448649
## sample estimates:
## odds ratio
## 0.6144866
Tenoxicam 40mg
##
## FALSE TRUE
## FALSE 3 10
## TRUE 0 9
##
## Fisher's Exact Test for Count Data
##
## data: table(data$bucking, data$tenoxicam.40mg)
## p-value = 0.2403
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2934045 Inf
## sample estimates:
## odds ratio
## Inf
Dexamethasone 10mg
##
## FALSE TRUE
## FALSE 9 4
## TRUE 5 4
##
## Fisher's Exact Test for Count Data
##
## data: table(data$bucking, data$dexamethasone.10mg)
## p-value = 0.6619
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2185166 14.6338010
## sample estimates:
## odds ratio
## 1.75137
Dipyrone 2g
##
## TRUE
## FALSE 13
## TRUE 9
Numerical
## variavel bucking nobucking pvalue
## 1 remifentanil.total 331.8 ( 0 - 1192 ) 252.49 ( 0 , 1110 ) 0.49
## 2 lidocaine.total 323.6 ( 238.4 - 413 ) 369.62 ( 124 , 518 ) 0.23
## 3 extubation.minutes 8.22 ( 5 - 14 ) 9.85 ( 4 , 20 ) 0.52
## 4 sistolic.variation 1 ( -29 - 37 ) -10.54 ( -47 , 19 ) 0.30
## 5 diastolic.variation 2.11 ( -24 - 17 ) -1.15 ( -40 , 47 ) 0.44
## 6 hr.variation 0.22 ( -16 - 28 ) -2 ( -46 , 27 ) 1.00
## 7 cuff.pressure.variation -4.33 ( -11 - 0 ) -1.92 ( -8 , 5 ) 0.18
## 8 spo2.variation 0.22 ( -3 - 2 ) 0.54 ( -2 , 6 ) 0.94