Plot data with data simulated and sensitivity of Biomarker varying

[insb]

Hi everyone,

I am simulating a clinical trial to study the impact of a imperfect biomarker on the sample size and on the power. So I would like to have a graph with the sample size ( determined with calculation) and the power depending on the imperfection of the biomarker. I would like to know first how can I extract the power. Here is my code

#Definition 

# p_pos_bm is proportion of the population that will test positive for the biomarker
# NPV_bm is the negative predictive value of the biomarker
# PPV_bm is the positive predictive value of the biomarker
# hr is the hazard ratio of the treatment on the G+ (we suppose 1 for the G-)
# hr0 is the HR of the BK+ in the population (with or without tmt); 1 by default
# lambda is the constant risk of survival 
# npop is the size of the population 


#Input variables
p_pos_bm <- 0.4 
NPV_bm <-  seq (0.1, 1, by=0.1) 
PPV_bm <- seq(0.3, 1.0, by=0.1) 
npop <- 1e+06
lambda <- 0.067
n<- 200

##Construction of the Bk stratefy design 
simdat <- function(npop=1e+06, p_pos_bm=0.4, NPV_bm=0.9, PPV_bm=0.8, hr=0.7, hr0=1, lambda=0.067)
{
  dat <- data.frame(id=1:npop, bk=rbinom(npop, 1, p_pos_bm))
  dat$gen[dat$bk==1] <- rbinom(sum(dat$bk==1), 1, PPV_bm)
  dat$gen[dat$bk==0] <- rbinom(sum(dat$bk==0), 1, 1-NPV_bm)
  dat$T0 <- rexp(nrow(dat),0.067*hr0^dat$gen)
  dat$T1 <- rexp(nrow(dat),0.067*(hr0*hr)^dat$gen)
  return(dat)
}

set.seed(1234)
dat <- simdat()

Ddesign <- function(dat, x=1:n, a=12, b=36)
{
  temp <- dat[x,]
  n <- length(x)
  temp$arm <- rbinom(n,1,0.5)
  temp$treat <- ifelse(temp$arm==0,0,temp$bk)
  temp$temps <- temp$T1*temp$treat + temp$T0*(1-temp$treat) 
  # censure
  Cens <- runif(n, a, b)
  temp$survie <- pmin(Cens, temp$temps)
  temp$dc <- as.numeric(temp$temps <= Cens)
  return(temp[,c("bk","arm","treat","survie","dc")])
}

test <- Ddesign(dat=dat)
- coxph(Surv(survie,dc)~arm, data=dat)
  zfit <- cox.zph(fit)
  return(c(summary(fit)$coefficients[c(1,3,5)],zfit$table[3]))
}

##Simulation of essays 

n <- 500
res2 <- matrix(nrow=nsim, ncol=4)
for(i in 1:nsim)
{
  test <- Ddesign(dat=dat, x=seq(n*(i-1)+1,n*(i-1)+n,by=1))
  res2[i,] <- analysetrial(test)
}
summary(res2)

# Extract Power ?? 



## Need of the sample size ( ici mon HR est constant, psi aussi)

samplesize <- function(HR=0.7, p=0.5, psi=0.4, alpha=0.05, beta=0.1)
{
  n <- (qnorm(1-alpha/2)+qnorm(1-beta))^2/(log(HR)^2*p*(1-p)*psi)
  return(n)
}

[technocrat]

cox.zph does not return a value with a power statistic. See

• The CR package proposes power calculation for weighted Log-Rank tests in cure rate models.
• The NPHMC permits to calculate sample size based on proportional hazards mixture cure models.
• The powerSurvEpi package provides power and sample size calculation for survival analysis (with a focus towards epidemiological studies).
• Power analysis and sample size calculation for SNP association studies with time-to-event outcomes can be done using the survSNP package.

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