[R] trouble with exiting loop if condition is met
Jim Lemon
drj|m|emon @end|ng |rom gm@||@com
Fri Jun 29 00:18:41 CEST 2018
Hi Kelly,
You seem to be testing the two "years" independently, so here is a
possible solution:
npg<-16
futility1<-futility2<-psignif1<-psignif2<-nrep<-0
while(!futility1 && !futility2 && !psignif1 && !psignif2 && nrep < 10000) {
control_respond1<-sum(runif(npg,0,1) < 0.05)
control_respond2<-sum(runif(npg,0,1) < 0.05)
treat_respond1<-sum(runif(npg,0,1) < 0.3)
treat_respond2<-sum(runif(npg,0,1) < 0.3)
futility1<-treat_respond1 == 0
futility2<-treat_respond2 == 0
psignif1<-fisher.test(matrix(c(control_respond1,npg-control_respond1,
treat_respond1,npg-treat_respond1),nrow=2),
alternative="greater")$p.value < 0.01
psignif2<-fisher.test(matrix(c(control_respond2,npg-control_respond2,
treat_respond2,npg-treat_respond2),nrow=2),
alternative="greater")$p.value < 0.01
nrep<-nrep+1
}
cat("futility1",futility1,"futility2",futility2,"psignif1",psignif1,
"psignif2",psignif2,"nrep",nrep,"\n")
The output tells you which condition was met and on which repetition
it occurred. The outcomes for all previous repetitions will be FALSE
and for the remaining repetitions, undefined (unless you have a cat
named Schrodinger)..
If you want to test the two "years" as sequential observations, it
will only require a minor modification.
Jim
On Fri, Jun 29, 2018 at 5:53 AM, Kelly Wu <kekwu using ucdavis.edu> wrote:
> I am working on a clinical trial simulation with two groups, treatment and
> placebo, and the outcome is dichotomous (recovery or no recovery) . I would
> like to stop my loop if either of my conditions are met:
>
> 1) futility - there are no responses to treatment in the treatment group.
> 2) the p-value is significant (<=0.01).
>
> The problem I am having is my loop continues to run 10,000 times even though
> I am sure that at least one of the conditions are met in some instances. It
> appears the main problem is that my if loop is not adequately filtering the
> conditions I specified in it.
>
> library(magrittr)
> library(dplyr)
>
> nSims <- 10000 #number of simulated experiments
> futility1 <-numeric(nSims) #set up empty container for all simulated
> futility
> futility2 <-numeric(nSims) #set up empty container for all simulated
> futility
>
> significant1 <-numeric(nSims) #set up empty container for all simulated
> significance
> significant2 <-numeric(nSims) #set up empty container for all simulated
> significance
>
> for(i in 1:nSims){ #for each simulated experiment
> # Year 1
>
> # p1<-response in controls
> # p2<-response in treated
> # Generating random deviates from a Uniform(0,1) distribution
> control.year1<-(runif(16, min = 0, max = 1))
> treat.year1<-(runif(16, min = 0, max = 1))
>
> #Generating dichotomous response variables for each group
> control.respond1<-ifelse(control.year1<=0.05,1,0)
> treat.respond1<-ifelse(treat.year1<=0.30,1,0)
>
> #Summing number of responses from each group
> control.no1<-sum(control.respond1==0)
> control.yes1<-sum(control.respond1==1)
> treat.no1<-sum(treat.respond1==0)
> treat.yes1<-sum(treat.respond1==1)
>
> #Perform the Fisher's exact test (one sided) with p<0.01
>
> fisher<-matrix(c(control.no1,control.yes1,treat.no1,treat.yes1),nrow=2,ncol=2)
> f<-fisher.test(fisher,alternative = "greater")
>
> #year 2
> if (f$p.value>0.01 && treat.yes1!=0){
> # Generating random deviates from a Uniform(0,1) distribution
> control.year2<-(runif(16, min = 0, max = 1))
> treat.year2<-(runif(16, min = 0, max = 1))
>
> #Generating dichotomous response variables for each group
> control.respond2<-ifelse(control.year2<=0.05,1,0)
> treat.respond2<-ifelse(treat.year2<=0.30,1,0)
>
> #Summing number of responses from each group
> control.no2<-sum(control.respond2==0)
> control.yes2<-sum(control.respond2==1)
> treat.no2<-sum(treat.respond2==0)
> treat.yes2<-sum(treat.respond2==1)
>
> #Perform the Fisher's exact test (one sided) with p<0.01
>
> fisher2<-matrix(c(control.no2,control.yes2,treat.no2,treat.yes2),nrow=2,ncol=2)
> f2<-fisher.test(fisher2,alternative = "greater")
> }
>
>
> significant2[i]<-ifelse(f2$p.value<0.01,1,0)
> futility2[i]<-ifelse(treat.yes2==0,1,0)
> }
>
> table(significant1)
> table(futility1)
>
> table(significant2)
> table(futility2)
>
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