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Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.IntroductionThe cost effectiveness acceptability curve (CEAC) is a tool used to describe the output of a probabilistic sensitivity analysis conducted on a model used in economic evaluations of health technologies. It communicates the probability of cost effectiveness conditional on our willingness to pay threshold for each unit of effectiveness or QALY gained. while this blog post will not provide the best treatment of theory behind the CEAC it will provide you some information on how to compute them in R.

Preliminaries: Simulating Cost Effectiveness Data.For the computing of the CEAC, I have chosen to simulate Data on 2000 treated and untreated patients (1000 in each group). If you are working with (real) patient level data using the net benefits regression framework would be more appropriate, but we can view this crude simulation as communicating the output from some probabilistic sensitivity analysis of a model. While setting the seed is not required, it does help with replicating the picture you see above.

I have also included code for the CE plane which is prodc

```

Load Packageslibrary('ggplot2')library('ggthemes')#Set seedset.seed(1234)#Simulating DataControl_QALYs<-rbeta(1000,8,2)Treatment_QALYs<-rbeta(1000,8,2)#Simulate CostsControl_Costs<-rnorm(1000,1000,200)Treatment_Costs<-rnorm(1000,1500,200)#Create Incremental CostsD_Costs<-Treatment_Costs-Control_CostsD_QALYs<-Treatment_QALYs-Control_QALYs#CE DataCEdata<-data.frame(D_Costs,D_QALYs)#Plot CE Planeggplot(CEdata,aes(y=D_Costs))+ geom_point(aes(x=D_QALYs),color="blue")+ geom_vline(xintercept=0,size=1)+ geom_hline(yintercept=0,size=1)+ xlim(-1,1)+ xlab("Incremental QALYs")+ ylab("Incremental Costs")+ ggtitle("The CE Plane")

``` Coding the CEACTo actually code your CEAC we need to convert the measures of incremental costs and effects into incremental net benefits. I do this by first creating a list of willingness to pay thresholds which we will be considering, creating an array for storing the data and then using a for loop for populating this array

```

Create Threshold VectorT_Names<-c("T0","T5000","T10000","T15000","T20000","T250000")Threshold<-c(0,5000,10000,15000,20000,25000)#Create Dataframeid<-seq(1,1000)df<-matrix(NA,nrow=1000,ncol=6,dimnames=list(NA,WTP=T_Names))#For loop for Incremental Net benefits Calculationfor(i in 1:6){ df[,i]<-Threshold[i]*D_QALYs-D_Costs }#ProbCE CalculationsProbCE_0<-sum(df[,1]>=0)/1000Prob_CE5k<-sum(df[,2]>=0)/1000Prob_CE10k<-sum(df[,3]>=0)/1000Prob_CE15k<-sum(df[,4]>=0)/1000Prob_CE20k<-sum(df[,5]>=0)/1000Prob_CE25k<-sum(df[,6]>=0)/1000#StoreValuesProb_CE<-c(ProbCE_0,Prob_CE5k,Prob_CE10k,Prob_CE15k,Prob_CE20k,Prob_CE25k)#CEAC DataframeCEAC_Data<-data.frame(Threshold,Prob_CE)CEAC_Data#Plot CEACggplot(CEAC_Data, aes(y=Prob_CE))+ geom_line(aes(x=Threshold),color="blue",size=1.0)+ geom_point(aes(x=Threshold),color="black", size=2.0)+ ylim(0,1)+ xlab("Willingness to Pay per QALY")+ ylab("Prob of CE")+ ggtitle("The Cost Effectiveness Acceptability Curve")+ theme_economist_white()

``` Concluding RemarksThis blog post has demonstrated a way to compute the cost effectiveness acceptability curve in R from scratch. There is likely a smarter way to compute each probability of cost effectiveness at each threshold in the form of a loop but the simplest solution is to calculate each probability directly.

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