R/n4props.R

In CRTSize: Sample Size Estimation Functions for Cluster Randomized Trials

n4props <- function(pe,pc, m, ICC, alpha=0.05, power=0.8, AR=1, two.tailed=TRUE, digits=3)
{
#Error Checking
if ((pe >= 1) || (pe <= 0) || (pc <= 0) || (pc >= 1))
        stop("Sorry, the prior proportions must lie within (0,1)")

if ((alpha >= 1) || (alpha <= 0) || (power <= 0) || (power >= 1))
        stop("Sorry, the alpha and power must lie within (0,1)")

if (ICC <= 0)
        stop("Sorry, the ICC must lie within (0,1)")

if (AR <=0) 
        stop("Sorry, the specified value of the Allocation Ratio (AR) must be strictly positive...")

if (m <=1) 
        stop("Sorry, the (average) cluster size, m, should be greater than one...")

#If m is a decimal, round up to generate a more conservative sample size.
m <- ceiling(m);

#Initialize Parameters
r <- NULL;
r$pe <- pe; r$pc <- pc; r$digits <- digits;
r$m <- m; r$ICC <- ICC;
r$alpha <- alpha; r$power <- power;
r$AR <- AR; r$two.tailed <- two.tailed; 

#One or two-tailed tests
if (two.tailed)
{
r$n <- ((qnorm(1 - alpha/2) + qnorm(power))^2*(pe*(1-pe) + pc*(1-pc))*(1 + (m - 1)*ICC))/(m*(pe - pc)^2);


if (r$n < 30)
{

nTemp <- 0;
while (abs(r$n - nTemp) > 1)
{
nTemp <- r$n;
r$n <- ( (qt((1 - alpha/2), df=(2*(nTemp - 1))) + qt(power, df=(2*(nTemp - 1))))^2*(pe*(1-pe) + pc*(1-pc))*(1 + (m - 1)*ICC))/(m*(pe - pc)^2);
}

}

}

if (!two.tailed)
{
r$n <- ((qnorm(1 - alpha) + qnorm(power))^2*(pe*(1-pe) + pc*(1-pc))*(1 + (m - 1)*ICC))/(m*(pe - pc)^2);

if (r$n < 30)
{

nTemp <- 0;
while (abs(r$n - nTemp) > 1)
{
nTemp <- r$n;
r$n <- ((qt((1 - alpha), df=(2*(nTemp - 1))) + qt(power, df=(2*(nTemp - 1))))^2*(pe*(1-pe) + pc*(1-pc))*(1 + (m - 1)*ICC))/(m*(pe - pc)^2);
}

}

}


#Adjust for allocation ratio;
r$nE = (1/2)*r$n*(1 + (1/AR));
r$nC = (1/2)*r$n*(1 + AR);

class(r) <- "n4props";
return(r);

}

#Print Method
print.n4props <- function(x, ...)
{
cat("The required sample size is a minimum of ", ceiling(x$nE), " clusters of size ", x$m, " in the Experimental Group \n", sep="")
cat(" and a minimum of ", ceiling(x$nC), " clusters (size ", x$m, ") in the Control Group. \n", sep="")
}

#Summary Method
summary.n4props <- function(object, ...)
{
cat("Sample Size Calculation for Binary Outcomes", "\n \n")
cat("Assuming:", "\n")
cat("Proportion with Outcome in Experimental Group: ", object$pe, "\n")
cat("Proportion with Outcome in Control Group: ", object$pc, "\n")
cat("Cluster Size (average) = ", object$m, "\n");
cat("ICC = ", object$ICC, "\n");
cat("Type I Error Rate (alpha) = ", object$alpha, " and Power = ", object$power, "\n \n",sep="")

cat("The required sample size is a minimum of ", ceiling(object$nE), " clusters of size ", object$m, " in the Experimental Group \n", sep="")
cat(" and a minimum of ", ceiling(object$nC), " clusters (size ", object$m, ") in the Control Group. \n", sep="")

}