From noreply at r-forge.r-project.org  Sat May 24 11:53:14 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sat, 24 May 2014 11:53:14 +0200 (CEST)
Subject: [Gsdesign-commits] r367 - pkg/gsDesign/R
Message-ID: <20140524095314.8F163186CBC@r-forge.r-project.org>

Author: keaven
Date: 2014-05-24 11:53:13 +0200 (Sat, 24 May 2014)
New Revision: 367

Modified:
   pkg/gsDesign/R/nEvents.R
Log:
nEvents table output changed to data.frame class

Modified: pkg/gsDesign/R/nEvents.R
===================================================================
--- pkg/gsDesign/R/nEvents.R	2014-02-24 00:19:38 UTC (rev 366)
+++ pkg/gsDesign/R/nEvents.R	2014-05-24 09:53:13 UTC (rev 367)
@@ -4,16 +4,18 @@
     delta <- -c * (log(hr) - log(hr0))
     if (n[1] == 0)
     {   n <- (qnorm(1-alpha/sided)+qnorm(1-beta))^2/delta^2
-        if (tbl) n <- cbind(hr = hr, n = ceiling(n), alpha = alpha, sided=sided, beta = beta, 
+        if (tbl) n <- data.frame(cbind(hr = hr, n = ceiling(n), alpha = alpha,
+									 sided=sided, beta = beta, 
                             Power = 1-beta, delta = delta, ratio = ratio, 
-                            hr0 = hr0, se = 1/c/sqrt(ceiling(n)))
+                            hr0 = hr0, se = 1/c/sqrt(ceiling(n))))
         return(n)
     }
     else
     {   pwr <- pnorm(-(qnorm(1-alpha/sided)-sqrt(n) * delta))
-        if (tbl) pwr <- cbind(hr = hr, n = n, alpha = alpha, sided=sided, beta = 1-pwr,
+        if (tbl) pwr <- data.frame(cbind(hr = hr, n = n, alpha = alpha,
+									 sided=sided, beta = 1-pwr,
                             Power = pwr, delta = delta, ratio = ratio,
-                            hr0 = hr0, se = sqrt(1/n)/c)
+                            hr0 = hr0, se = sqrt(1/n)/c))
         return(pwr)
     }
 }


From noreply at r-forge.r-project.org  Sat May 24 14:44:13 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sat, 24 May 2014 14:44:13 +0200 (CEST)
Subject: [Gsdesign-commits] r368 - pkg/gsDesign/R
Message-ID: <20140524124413.C89361849A4@r-forge.r-project.org>

Author: keaven
Date: 2014-05-24 14:44:12 +0200 (Sat, 24 May 2014)
New Revision: 368

Modified:
   pkg/gsDesign/R/gsCP.R
   pkg/gsDesign/R/gsDesign.R
   pkg/gsDesign/R/nNormal.R
Log:


Modified: pkg/gsDesign/R/gsCP.R
===================================================================
--- pkg/gsDesign/R/gsCP.R	2014-05-24 09:53:13 UTC (rev 367)
+++ pkg/gsDesign/R/gsCP.R	2014-05-24 12:44:12 UTC (rev 368)
@@ -156,9 +156,8 @@
     checkVector(prior$gridwgts, "numeric", c(0, Inf), c(TRUE, FALSE))
     checkVector(prior$density, "numeric", c(0, Inf), c(TRUE, FALSE))
     checkVector(prior$z, "numeric", c(-Inf,Inf), c(FALSE, FALSE))
-    cl <- class(x)
-    if (cl != "gsDesign" && cl != "gsProbability")
-        stop("gsPosterior: x must have class gsDesign or gsProbability")
+    if (!(is(x, "gsProbability") || is(x, "gsDesign")))
+      stop("gsPosterior: x must have class gsDesign or gsProbability")
     test.type <- ifelse(is(x, "gsProbability"), 3, x$test.type)
     checkScalar(i, "integer", c(1, x$k-1))
     if (is.null(zi)) zi <- c(x$lower$bound[i],x$upper$bound[i])

Modified: pkg/gsDesign/R/gsDesign.R
===================================================================
--- pkg/gsDesign/R/gsDesign.R	2014-05-24 09:53:13 UTC (rev 367)
+++ pkg/gsDesign/R/gsDesign.R	2014-05-24 12:44:12 UTC (rev 368)
@@ -124,14 +124,14 @@
 "gsDesign"<-function(k=3, test.type=4, alpha=0.025, beta=0.1, astar=0,  
         delta=0, n.fix=1, timing=1, sfu=sfHSD, sfupar=-4,
         sfl=sfHSD, sflpar=-2, tol=0.000001, r=18, n.I=0, maxn.IPlan=0, 
-        nFixSurv=0, endpoint=NULL, delta1=1, delta0=0) 
+        nFixSurv=0, endpoint=NULL, delta1=1, delta0=0, overrun=0) 
 {
     # Derive a group sequential design and return in a gsDesign structure
     
     # set up class variable x for gsDesign being requested
     x <- list(k=k, test.type=test.type, alpha=alpha, beta=beta, astar=astar,
             delta=delta, n.fix=n.fix, timing=timing, tol=tol, r=r, n.I=n.I, maxn.IPlan=maxn.IPlan,
-            nFixSurv=nFixSurv, nSurv=0, endpoint=endpoint, delta1=delta1, delta0=delta0)
+            nFixSurv=nFixSurv, nSurv=0, endpoint=endpoint, delta1=delta1, delta0=delta0, overrun=overrun)
     
     class(x) <- "gsDesign"
     
@@ -210,14 +210,14 @@
     x
 }
 
-"gsProbability" <- function(k=0, theta, n.I, a, b, r=18, d=NULL)
+"gsProbability" <- function(k=0, theta, n.I, a, b, r=18, d=NULL, overrun=0)
 {
     # compute boundary crossing probabilities and return in a gsProbability structure
     
     # check input arguments
     checkScalar(k, "integer", c(0,30))
     checkVector(theta, "numeric")
-    
+
     if (k == 0)
     {   
         if (!is(d,"gsDesign"))
@@ -228,6 +228,7 @@
     }
     
     # check remaingin input arguments
+    checkVector(overrrun,length=k-1,interval=c(0,Inf),include=c(TRUE,FALSE))
     checkScalar(r, "integer", c(1,80))
     checkLengths(n.I, a, b)
     if (k != length(a))
@@ -248,9 +249,11 @@
     phi <- matrix(xx[[8]], k, ntheta)
     powr <- as.vector(array(1, k) %*% phi)
     futile <- array(1, k) %*% plo
-    en <- as.vector(n.I %*% (plo + phi) + n.I[k] * (t(array(1, ntheta)) - powr - futile))
+    nOver <- c(n.I[1:(k-1)]+overrun,n.I[k])
+    nOver[nOver>n.I[k]] <- n.I[k]
+    en <- as.vector(nOver %*% (plo + phi) + n.I[k] * (t(array(1, ntheta)) - powr - futile))
     x <- list(k=xx[[1]], theta=xx[[3]], n.I=xx[[4]], lower=list(bound=xx[[5]], prob=plo), 
-            upper=list(bound=xx[[6]], prob=phi), en=en, r=r)
+            upper=list(bound=xx[[6]], prob=phi), en=en, r=r, overrun=overrun)
     
     class(x) <- "gsProbability"
     
@@ -345,7 +348,7 @@
     
     # add boundary crossing probabilities for theta to x
     x$theta <- c(0,x$delta)
-    y <- gsprob(x$theta, x$n.I, a, x$upper$bound, r=x$r)
+    y <- gsprob(x$theta, x$n.I, a, x$upper$bound, r=x$r, overrun=x$overrun)
     x$upper$prob <- y$probhi
     x$en <- as.vector(y$en)
 
@@ -426,7 +429,7 @@
     }
     
     x$theta <- c(0, x$delta)
-    y <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r)
+    y <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r, overrun=overrun)
     x$upper$prob <- y$probhi
     x$lower$prob <- y$problo
     x$en <- as.vector(y$en)
@@ -522,7 +525,7 @@
 
     # add boundary crossing probabilities for theta to x
     x$theta <- c(0, x$delta)
-    x4 <- gsprob(x$theta, x2$I, x2$a, x2$b)
+    x4 <- gsprob(x$theta, x2$I, x2$a, x2$b, overrun=x$overrun)
     x$upper$prob <- x4$probhi
     x$lower$prob <- x4$problo
     x$en <- as.vector(x4$en)
@@ -633,7 +636,7 @@
     
     # add boundary crossing probabilities for theta to x
     x$theta <- c(0,x$delta)
-    x4 <- gsprob(x$theta,x$n.I,x$lower$bound,x$upper$bound)
+    x4 <- gsprob(x$theta,x$n.I,x$lower$bound,x$upper$bound,overrun=overrun)
     x$upper$prob <- x4$probhi
     x$lower$prob <- x4$problo
     x$en <- as.vector(x4$en)
@@ -680,7 +683,7 @@
 
     # add boundary crossing probabilities for theta to x
     x$theta <- c(0, x$delta)
-    x4 <- gsprob(x$theta, x$n.I, -x2$b, x1$b)
+    x4 <- gsprob(x$theta, x$n.I, -x2$b, x1$b, overrun=overrun)
     x$upper$prob <- x4$probhi
     x$lower$prob <- x4$problo
     x$en <- as.vector(x4$en)
@@ -720,7 +723,7 @@
     # compute additional error rates needed and add to x
     x$theta <- c(0, x$delta)
     x$falseposnb <- as.vector(gsprob(0, xx$I, array(-20, x$k), x0$b, r=x$r)$probhi)
-    x3 <- gsprob(x$theta, xx$I, xx$a, x0$b, r=x$r)
+    x3 <- gsprob(x$theta, xx$I, xx$a, x0$b, r=x$r, overrun=x$overrun)
     x$upper$prob <- x3$probhi
     x$lower$prob <- x3$problo
     x$en <- as.vector(x3$en)
@@ -797,7 +800,7 @@
     # compute error rates needed and add to x
     x$theta <- c(0,x$delta)
     x$falseposnb <- as.vector(gsprob(0, x$n.I, array(-20, x$k), x$upper$bound,r =x$r)$probhi)
-    x3 <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r)
+    x3 <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r, overrun=overrun)
     x$upper$prob <- x3$probhi
     x$lower$prob <- x3$problo
     x$en <- as.vector(x3$en)
@@ -877,7 +880,7 @@
     x
 }
 
-"gsprob" <- function(theta, I, a, b, r=18)
+"gsprob" <- function(theta, I, a, b, r=18, overrun=0)
 {     
     # gsprob: use call to C routine to compute upper and lower boundary crossing probabilities
     # given theta,  interim sample sizes (information: I),  lower bound (a) and upper bound (b)    
@@ -892,7 +895,9 @@
     phi <- matrix(xx[[8]], nanal, ntheta)
     powr <- array(1, nanal)%*%phi
     futile <- array(1, nanal)%*%plo
-    en <- I %*% (plo+phi) + I[nanal] * (t(array(1, ntheta)) - powr - futile)
+    IOver <- c(I[1:(nanal-1)]+overrun,I[nanal])
+    IOver[IOver>I[nanal]]<-nanal
+    en <- IOver %*% (plo+phi) + I[nanal] * (t(array(1, ntheta)) - powr - futile)
     list(k=xx[[1]], theta=xx[[3]], I=xx[[4]], a=xx[[5]], b=xx[[6]], problo=plo, 
             probhi=phi, powr=powr, en=en, r=r)
 }
@@ -1040,5 +1045,8 @@
     x$test.type <- as.integer(x$test.type)
     x$r <- as.integer(x$r)
     
+    # check overrun vector
+    checkVector(x$overrun,length(x$k-1),interval=c(0,Inf),inclusion=c(TRUE,FALSE))
+  
     x
 }

Modified: pkg/gsDesign/R/nNormal.R
===================================================================
--- pkg/gsDesign/R/nNormal.R	2014-05-24 09:53:13 UTC (rev 367)
+++ pkg/gsDesign/R/nNormal.R	2014-05-24 12:44:12 UTC (rev 368)
@@ -1,11 +1,42 @@
-"nNormal" <- function(delta1=1, sigma=1.7, sigalt=NULL, alpha=.025,
-               beta=.1, ratio=1, sided=1, n=NULL, delta0=0)
-{  xi <- ratio/(1+ratio)
-   if (is.null(sigalt)) sigalt <- sigma
-   v <- sigalt^2/xi + sigma^2/(1-xi)
-   theta1 <- (delta1-delta0)/sqrt(v)
-   if (is.null(n))
-      return(((qnorm(alpha/sided)+qnorm(beta))/theta1)^2)
-   else
-      return(pnorm(sqrt(n)*theta1-qnorm(1-alpha/sided)))
+"nNormal" <- function(delta1=1, sd=1.7, sd2=NULL, alpha=.025,
+               beta=.1, ratio=1, sided=1, n=NULL, delta0=0, outtype=1)
+{ # check input arguments
+  checkVector(delta1, "numeric", c(-Inf, Inf), c(FALSE, FALSE))
+  checkVector(sd, "numeric", c(0, Inf), c(FALSE, FALSE))    
+  checkScalar(sided, "integer", c(1, 2))    
+  checkScalar(alpha, "numeric", c(0, 1 / sided), c(FALSE, FALSE))
+  checkVector(beta, "numeric", c(0, 1 - alpha / sided), c(FALSE, FALSE))
+  checkVector(delta0, "numeric", c(-Inf, Inf), c(FALSE, FALSE))
+  checkVector(ratio, "numeric", c(0, Inf), c(FALSE, FALSE))
+  checkScalar(outtype, "integer", c(1, 3))
+  checkLengths(delta1, delta0, sd, sd2, alpha, beta, ratio, allowSingle=TRUE)
+  
+   xi <- ratio/(1+ratio)
+   if (is.null(sd2)) sd2 <- sd
+   se <- sqrt(sd2^2/xi + sd^2/(1-xi))
+   theta1 <- (delta1-delta0)/se
+   if (max(abs(theta1) == 0)) stop("delta1 may not equal delta0")
+   if (is.null(n)){
+      n <-((qnorm(alpha/sided)+qnorm(beta))/theta1)^2
+      if (outtype == 2)
+      {
+        return(data.frame(cbind(n1=n / (ratio + 1),  n2=ratio * n / (ratio + 1))))
+      }
+      else if (outtype == 3) 
+      {   
+        return(data.frame(cbind(n=n, n1=n / (ratio + 1), n2=ratio * n / (ratio + 1),
+                    alpha = alpha, sided=sided, beta = beta, Power = 1-beta,
+                    sd=sd, sd2=sd2, delta1=delta1, delta0=delta0, se=se/sqrt(n))))
+      }
+      else return(n=n)
+   }else{
+      powr <- pnorm(sqrt(n)*theta1-qnorm(1-alpha/sided))
+      if (outtype == 2) return(data.frame(cbind(n1=n / (ratio + 1),  n2=ratio * n / (ratio + 1), Power=powr)))
+      else if (outtype == 3){
+        return(data.frame(cbind(n=n, n1=n / (ratio + 1), n2=ratio * n / (ratio + 1),
+               alpha = alpha, sided=sided, beta = 1-powr, Power = powr,
+               sd=sd, sd2=sd2, delta1=delta1, delta0=delta0, se=se/sqrt(n))))
+      }
+      else(return(Power=powr))
+    }
 }


From noreply at r-forge.r-project.org  Mon May 26 03:52:08 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Mon, 26 May 2014 03:52:08 +0200 (CEST)
Subject: [Gsdesign-commits] r369 - in pkg/gsDesign: . R man
Message-ID: <20140526015209.01664186EC5@r-forge.r-project.org>

Author: keaven
Date: 2014-05-26 03:52:07 +0200 (Mon, 26 May 2014)
New Revision: 369

Modified:
   pkg/gsDesign/DESCRIPTION
   pkg/gsDesign/NAMESPACE
   pkg/gsDesign/R/gsCP.R
   pkg/gsDesign/R/gsDesign.R
   pkg/gsDesign/man/gsDesign.Rd
   pkg/gsDesign/man/gsProbability.Rd
Log:
Added overrun computations to expected sample size; updated help files for gsDesign, gsProbability

Modified: pkg/gsDesign/DESCRIPTION
===================================================================
--- pkg/gsDesign/DESCRIPTION	2014-05-24 12:44:12 UTC (rev 368)
+++ pkg/gsDesign/DESCRIPTION	2014-05-26 01:52:07 UTC (rev 369)
@@ -1,5 +1,5 @@
 Package: gsDesign
-Version: 2.8-9
+Version: 2.9
 Title: Group Sequential Design
 Author: Keaven Anderson 
 Maintainer: Keaven Anderson <keaven_anderson at merck.com>

Modified: pkg/gsDesign/NAMESPACE
===================================================================
--- pkg/gsDesign/NAMESPACE	2014-05-24 12:44:12 UTC (rev 368)
+++ pkg/gsDesign/NAMESPACE	2014-05-26 01:52:07 UTC (rev 369)
@@ -1,4 +1,5 @@
 useDynLib(gsDesign)
+import(RUnit,ggplot2,plyr,stringr,xtable)
 export(gsBoundCP, gsCP, gsPP, gsPI, gsPOS, gsCPOS, gsDensity, gsPosterior)
 export(gsCPz, gsHR, gsDelta, gsBValue, gsRR, hrn2z, hrz2n, zn2hr)
 export(gsBound, gsBound1, gsDesign, gsProbability)

Modified: pkg/gsDesign/R/gsCP.R
===================================================================
--- pkg/gsDesign/R/gsCP.R	2014-05-24 12:44:12 UTC (rev 368)
+++ pkg/gsDesign/R/gsCP.R	2014-05-26 01:52:07 UTC (rev 369)
@@ -71,7 +71,7 @@
         anew <- array(-20, knew)
     }
     
-    gsProbability(k=knew, theta=theta, n.I=Inew, a=anew, b=bnew, r=r)
+    gsProbability(k=knew, theta=theta, n.I=Inew, a=anew, b=bnew, r=r, overrun=0)
 }
 
 gsPP <- function(x, i=1, zi=0, theta=c(0,3), wgts=c(.5,.5), r=18, total=TRUE)

Modified: pkg/gsDesign/R/gsDesign.R
===================================================================
--- pkg/gsDesign/R/gsDesign.R	2014-05-24 12:44:12 UTC (rev 368)
+++ pkg/gsDesign/R/gsDesign.R	2014-05-26 01:52:07 UTC (rev 369)
@@ -227,8 +227,9 @@
         return(gsDProb(theta=theta, d=d))
     }
     
-    # check remaingin input arguments
-    checkVector(overrrun,length=k-1,interval=c(0,Inf),include=c(TRUE,FALSE))
+    # check remaining input arguments
+    checkVector(x=overrun,isType="numeric",interval=c(0,Inf),inclusion=c(TRUE,FALSE))
+    if(length(overrun)!= 1 && length(overrun)!= k-1) stop(paste("overrun length should be 1 or ",as.character(k-1)))
     checkScalar(r, "integer", c(1,80))
     checkLengths(n.I, a, b)
     if (k != length(a))
@@ -249,7 +250,9 @@
     phi <- matrix(xx[[8]], k, ntheta)
     powr <- as.vector(array(1, k) %*% phi)
     futile <- array(1, k) %*% plo
-    nOver <- c(n.I[1:(k-1)]+overrun,n.I[k])
+    if(k==1){nOver <- n.I[k]}else{
+      nOver <- c(n.I[1:(k-1)]+overrun,n.I[k])
+    }
     nOver[nOver>n.I[k]] <- n.I[k]
     en <- as.vector(nOver %*% (plo + phi) + n.I[k] * (t(array(1, ntheta)) - powr - futile))
     x <- list(k=xx[[1]], theta=xx[[3]], n.I=xx[[4]], lower=list(bound=xx[[5]], prob=plo), 
@@ -429,7 +432,7 @@
     }
     
     x$theta <- c(0, x$delta)
-    y <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r, overrun=overrun)
+    y <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r, overrun=x$overrun)
     x$upper$prob <- y$probhi
     x$lower$prob <- y$problo
     x$en <- as.vector(y$en)
@@ -636,7 +639,7 @@
     
     # add boundary crossing probabilities for theta to x
     x$theta <- c(0,x$delta)
-    x4 <- gsprob(x$theta,x$n.I,x$lower$bound,x$upper$bound,overrun=overrun)
+    x4 <- gsprob(x$theta,x$n.I,x$lower$bound,x$upper$bound,overrun=x$overrun)
     x$upper$prob <- x4$probhi
     x$lower$prob <- x4$problo
     x$en <- as.vector(x4$en)
@@ -683,7 +686,7 @@
 
     # add boundary crossing probabilities for theta to x
     x$theta <- c(0, x$delta)
-    x4 <- gsprob(x$theta, x$n.I, -x2$b, x1$b, overrun=overrun)
+    x4 <- gsprob(x$theta, x$n.I, -x2$b, x1$b, overrun=x$overrun)
     x$upper$prob <- x4$probhi
     x$lower$prob <- x4$problo
     x$en <- as.vector(x4$en)
@@ -800,7 +803,7 @@
     # compute error rates needed and add to x
     x$theta <- c(0,x$delta)
     x$falseposnb <- as.vector(gsprob(0, x$n.I, array(-20, x$k), x$upper$bound,r =x$r)$probhi)
-    x3 <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r, overrun=overrun)
+    x3 <- gsprob(x$theta, x$n.I, x$lower$bound, x$upper$bound, r=x$r, overrun=x$overrun)
     x$upper$prob <- x3$probhi
     x$lower$prob <- x3$problo
     x$en <- as.vector(x3$en)
@@ -896,8 +899,8 @@
     powr <- array(1, nanal)%*%phi
     futile <- array(1, nanal)%*%plo
     IOver <- c(I[1:(nanal-1)]+overrun,I[nanal])
-    IOver[IOver>I[nanal]]<-nanal
-    en <- IOver %*% (plo+phi) + I[nanal] * (t(array(1, ntheta)) - powr - futile)
+    IOver[IOver>I[nanal]]<-I[nanal]
+    en <- as.vector(IOver %*% (plo+phi) + I[nanal] * (t(array(1, ntheta)) - powr - futile))
     list(k=xx[[1]], theta=xx[[3]], I=xx[[4]], a=xx[[5]], b=xx[[6]], problo=plo, 
             probhi=phi, powr=powr, en=en, r=r)
 }
@@ -921,7 +924,12 @@
     phi <- matrix(xx[[8]], k, ntheta)
     powr <- as.vector(array(1, k) %*% phi)
     futile <- array(1, k) %*% plo
-    en <- as.vector(n.I %*% (plo + phi) + n.I[k] * (t(array(1, ntheta)) - powr - futile))
+    if (k==1){IOver <- n.I}else{
+      IOver <- c(n.I[1:(k-1)]+d$overrun,n.I[k])
+    }
+    IOver[IOver>n.I[k]]<-n.I[k]
+    en <- as.vector(IOver %*% (plo+phi) + n.I[k] * (t(array(1, ntheta)) - powr - futile))
+
     d$en <- en
     d$theta <- theta
     d$upper$prob <- phi
@@ -1046,7 +1054,8 @@
     x$r <- as.integer(x$r)
     
     # check overrun vector
-    checkVector(x$overrun,length(x$k-1),interval=c(0,Inf),inclusion=c(TRUE,FALSE))
+    checkVector(x$overrun,interval=c(0,Inf),inclusion=c(TRUE,FALSE))
+    if(length(x$overrun)!= 1 && length(x$overrun)!= x$k-1) stop(paste("overrun length should be 1 or ",as.character(x$k-1)))
   
     x
 }

Modified: pkg/gsDesign/man/gsDesign.Rd
===================================================================
--- pkg/gsDesign/man/gsDesign.Rd	2014-05-24 12:44:12 UTC (rev 368)
+++ pkg/gsDesign/man/gsDesign.Rd	2014-05-26 01:52:07 UTC (rev 369)
@@ -6,7 +6,7 @@
 gsDesign(k=3, test.type=4, alpha=0.025, beta=0.1, astar=0,  
          delta=0, n.fix=1, timing=1, sfu=sfHSD, sfupar=-4,
          sfl=sfHSD, sflpar=-2, tol=0.000001, r=18, n.I = 0,
-         maxn.IPlan = 0, nFixSurv=0, endpoint=NULL, delta1=1, delta0=0)
+         maxn.IPlan = 0, nFixSurv=0, endpoint=NULL, delta1=1, delta0=0,overrun=0)
 }
 \arguments{
 	\item{k}{Number of analyses planned, including interim and final.}
@@ -60,6 +60,7 @@
 
 	\item{delta1}{\code{delta1} and \code{delta0} may be used to store information about the natural parameter scale compared to \code{delta} that is a standardized effect size. \code{delta1} is the alternative hypothesis parameter value on the natural parameter scale (e.g., the difference in two binomial rates).}
   \item{delta0}{\code{delta0} is the null hypothesis parameter value on the natural parameter scale.}
+  \item{overrun}{Scalar or vector of length \code{k-1} with patients enrolled that are not included in each interim analysis.}
 }
 \value{
 An object of the class \code{gsDesign}. This class has the following elements and upon return from 
@@ -77,21 +78,27 @@
 \item{timing}{A vector of length \code{k} containing the portion of the total planned information or sample size at each analysis.}
 \item{tol}{As input.}
 \item{r}{As input.}
-\item{upper}{Upper bound spending function, boundary and boundary crossing probabilities under the NULL and
-alternate hypotheses. See \link{Spending function overview} and manual for further details.}
-\item{lower}{Lower bound spending function, boundary and boundary crossing probabilities at each analysis.
-Lower spending is under alternative hypothesis (beta spending) for \code{test.type=3} or \code{4}. 
-For \code{test.type=2}, \code{5} or \code{6}, lower spending is under the null hypothesis.
-For \code{test.type=1}, output value is \code{NULL}. See \link{Spending function overview} and manual.}
 \item{n.I}{Vector of length \code{k}. If values are input, same values are output.
 Otherwise, \code{n.I} will contain the sample size required at each analysis 
 to achieve desired \code{timing} and \code{beta} for the output value of \code{delta}. 
 If \code{delta=0} was input, then this is the sample size required for the specified group sequential design when a fixed design requires a sample size of \code{n.fix}.
 If \code{delta=0} and \code{n.fix=1} then this is the relative sample size compared to a fixed design; see details and examples.}
 \item{maxn.IPlan}{As input.}
+\item{nFixSurv}{As input.}
+\item{nSurv}{Sample size for Lachin and Foulkes method when \code{nSurvival} is used for fixed design input. If \code{nSurvival} is used to compute \code{n.fix}, then \code{nFixSurv} is inflated by the same amount as \code{n.fix} and stored in \code{nSurv}. Note that if you use \code{gsSurv} for time-to-event sample size, this is not needed and a more complete output summary is given.}
 \item{endpoint}{As input.}
 \item{delta1}{As input.}
 \item{delta0}{As input.}
+\item{overrun}{As input.}
+\item{upper}{Upper bound spending function, boundary and boundary crossing probabilities under the NULL and
+alternate hypotheses. See \link{Spending function overview} and manual for further details.}
+\item{lower}{Lower bound spending function, boundary and boundary crossing probabilities at each analysis.
+Lower spending is under alternative hypothesis (beta spending) for \code{test.type=3} or \code{4}. 
+For \code{test.type=2}, \code{5} or \code{6}, lower spending is under the null hypothesis.
+For \code{test.type=1}, output value is \code{NULL}. See \link{Spending function overview} and manual.}
+\item{theta}{Standarized effect size under null (0) and alternate hypothesis. If \code{delta} is input, \code{theta[1]=delta}. If \code{n.fix} is input, \code{theta[1]} is computed using a standard sample size formula (pseudocode): \code{((Zalpha+Zbeta)/theta[1])^2=n.fix}.}
+\item{falseprobnb}{For \code{test.type=4} or \code{6}, this contains false positive probabilities under the null hypothesis assuming that crossing a futility bound does not stop the trial.}
+\item{en}{Expected sample size accounting for early stopping. For time-to-event outcomes, this would be the expected number of events (although \code{gsSurv} will give expected sample size). For information-based-design, this would give the expected information when the trial stops. If \code{overrun} is specified, the expected sample size includes the overrun at each interim.}
 }
 \details{
 Many parameters normally take on default values and thus do not require explicit specification.

Modified: pkg/gsDesign/man/gsProbability.Rd
===================================================================
--- pkg/gsDesign/man/gsProbability.Rd	2014-05-24 12:44:12 UTC (rev 368)
+++ pkg/gsDesign/man/gsProbability.Rd	2014-05-26 01:52:07 UTC (rev 369)
@@ -8,7 +8,7 @@
 }
 
 \usage{
-gsProbability(k=0, theta, n.I, a, b, r=18, d=NULL)
+gsProbability(k=0, theta, n.I, a, b, r=18, d=NULL, overrun=0)
 \method{print}{gsProbability}(x,\dots)
 }
 \details{Depending on the calling sequence, an object of class \code{gsProbability} or class \code{gsDesign} is returned.
@@ -34,6 +34,7 @@
 	When this is specified, the values of \code{k}, \code{n.I}, \code{a}, \code{b}, and \code{r} will be obtained from \code{d} and
 	only \code{theta} needs to be specified by the user.}
   \item{x}{An item of class \code{gsProbability}.}
+  \item{overrun}{Scalar or vector of length \code{k-1} with patients enrolled that are not included in each interim analysis.}
   \item{\dots}{Not implemented (here for compatibility with generic print input).}
 }
 \value{


From noreply at r-forge.r-project.org  Thu May 29 21:50:08 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Thu, 29 May 2014 21:50:08 +0200 (CEST)
Subject: [Gsdesign-commits] r370 - pkg/gsDesign/man
Message-ID: <20140529195008.35790186DEF@r-forge.r-project.org>

Author: keaven
Date: 2014-05-29 21:50:07 +0200 (Thu, 29 May 2014)
New Revision: 370

Modified:
   pkg/gsDesign/man/gsbound.Rd
   pkg/gsDesign/man/nNormal.Rd
Log:
Documentation fix

Modified: pkg/gsDesign/man/gsbound.Rd
===================================================================
--- pkg/gsDesign/man/gsbound.Rd	2014-05-26 01:52:07 UTC (rev 369)
+++ pkg/gsDesign/man/gsbound.Rd	2014-05-29 19:50:07 UTC (rev 370)
@@ -2,7 +2,7 @@
 \alias{gsBound}
 \alias{gsBound1}
 
-\title{2.6: Boundary derivation - low level}
+\title{2.7: Boundary derivation - low level}
 \description{\code{gsBound()} and \code{gsBound1()} are lower-level functions used to find boundaries for a group sequential design.
 They are not recommended (especially \code{gsBound1()}) for casual users.
 These functions do not adjust sample size as \code{gsDesign()} does to ensure appropriate power for a design.

Modified: pkg/gsDesign/man/nNormal.Rd
===================================================================
--- pkg/gsDesign/man/nNormal.Rd	2014-05-26 01:52:07 UTC (rev 369)
+++ pkg/gsDesign/man/nNormal.Rd	2014-05-29 19:50:07 UTC (rev 370)
@@ -6,25 +6,43 @@
 Note that you may wish to investigate other R packages such as the \code{pwr} package which uses the t-distr
 }
 \usage{
-nNormal(delta1=1,sigma=1.7,sigalt=NULL,alpha=.025,beta=.1,ratio=1, sided=1,
-n=NULL,delta0=0)
+nNormal(delta1=1,sd=1.7,sd2=NULL,alpha=.025,beta=.1,ratio=1, sided=1,
+n=NULL,delta0=0,outtype=1)
 }
 \arguments{
 \item{delta1}{difference between sample means under the alternate hypothesis.}
 \item{delta0}{difference between sample means under the null hypothesis; normally this will be left as the default of 0.}
 \item{ratio}{randomization ratio of experimental group compared to control.}
 \item{sided}{1 for 1-sided test (default), 2 for 2-sided test.}
-\item{sigma}{Standard deviation for the control arm.}
-\item{sigalt}{Standard deviation of experimental arm; this will be set to be the same as the control arm with the default of \code{NULL}.}
+\item{sd}{Standard deviation for the control arm.}
+\item{sd2}{Standard deviation of experimental arm; this will be set to be the same as the control arm with the default of \code{NULL}.}
 \item{alpha}{type I error rate. Default is 0.025 since 1-sided testing is default.}
 \item{beta}{type II error rate. Default is 0.10 (90\% power). Not needed if \code{n} is provided.}
 \item{n}{Sample size; may be input to compute power rather than sample size. If \code{NULL} (default) then sample size is computed.}
+\code{delta0}{default value of 0 is set to test for superiority; negative values used for non-inferiority (assuming \code{delta1>0}).} 
+\item{outtype}{controls output; see value section below.}
 }
 \details{
 \code{nNormal()} computes sample size for comparing two normal means when the variance for observations in
 }
 \value{
-If \code{n} is \code{NULL} (default), total sample size (2 arms combined) is computed. Otherwise, power is c
+If \code{n} is \code{NULL} (default), total sample size (2 arms combined) is computed. Otherwise, power is computed.
+If \code{outtype=1} (default), the computed value (sample size or power) is returned in a scalar or vector.
+If \code{outtype=2}, a data frame with sample sizes for each arm (\code{n1}, \code{n2})is returned; if \code{n} is not input as \code{NULL}, a third variable, \code{Power}, is added to the output data frame.
+If \code{outtype=3}, a data frame with is returned with the following columns:
+  \item{n}{A vector with total samples size required for each event rate comparison specified}
+  \item{n1}{A vector of sample sizes for group 1 for each event rate comparison specified}
+  \item{n2}{A vector of sample sizes for group 2 for each event rate comparison specified}
+  \item{alpha}{As input}
+  \item{sided}{As input}
+  \item{beta}{As input; if \code{n} is input, this is computed}
+  \item{Power}{If \code{n=NULL} on input, this is \code{1-beta}; otherwise, the power is computed for each sample size input}
+  \item{sd}{As input}
+  \item{sd2}{As input}
+  \item{delta1}{As input}
+  \item{delta0}{As input}
+  \item{se}{standard error for estimate of difference in treatment group means}
+
 }
 \seealso{\link{gsDesign package overview}}
 \author{Keaven Anderson \email{keaven_anderson at merck.com}}
@@ -39,12 +57,12 @@
 \examples{
 # EXAMPLES
 # equal variances
-nNormal(delta1=.5,sigma=1.1,alpha=.025,beta=.2)
+nNormal(delta1=.5,sd=1.1,alpha=.025,beta=.2)
 # unequal variances
-nNormal(delta1=.5,sigma=1.1,sigalt=2,alpha=.025,beta=.2)
+nNormal(delta1=.5,sd=1.1,sd2=2,alpha=.025,beta=.2)
 # unequal sample sizes
-nNormal(delta1=.5,sigma=1.1,alpha=.025,beta=.2, ratio=2)
+nNormal(delta1=.5,sd=1.1,alpha=.025,beta=.2, ratio=2)
 # non-inferiority assuming a better effect than null
-nNormal(delta1=.5,delta0=-.1,sigma=1.2)
+nNormal(delta1=.5,delta0=-.1,sd=1.2)
 }
 \keyword{design}