extended 'SchmidliEtAl2017' example

data/SchmidliEtAl2017.R

@@ -9,7 +9,7 @@
 # specify data (Table 2):
 SchmidliEtAl2017 <- cbind.data.frame("study" = c("CATT", "CLEAR-IT 2", "HARBOR",
                                                  "IVAN", "VIEW 1", "VIEW 2"),
-                                     "N"     = c(599, 62, 550, 309, 909, 906),
+                                     "N"     = as.integer(c(599, 62, 550, 309, 909, 906)),
                                      "stdev" = c(12.11, 10.97, 10.94, 9.41, 10.97, 10.95),
-                                     "df"    = c(597, 60, 548, 307, 906, 903),
+                                     "df"    = as.integer(c(597, 60, 548, 307, 906, 903)),
                                      stringsAsFactors=FALSE)

man/SchmidliEtAl2017.Rd

@@ -7,9 +7,9 @@
 \format{The data frame contains the following columns:
   \tabular{lll}{
     \bold{study} \tab \code{character} \tab study label \cr
-    \bold{N}     \tab \code{numeric}   \tab total sample size \cr
+    \bold{N}     \tab \code{integer}   \tab total sample size \cr
     \bold{stdev} \tab \code{numeric}   \tab standard deviation estimate \cr
-    \bold{df}    \tab \code{numeric}   \tab associated degrees of freedom
+    \bold{df}    \tab \code{integer}   \tab associated degrees of freedom
   }
 }
 \details{Schmidli \emph{et al.} (2017) investigated the use of
@@ -28,8 +28,7 @@
   may then be modelled on the logarithmic scale, where the estimates and
   their associated standard errors are given by 
   \deqn{y_i=\log(\hat{s}_i) \quad \mbox{and} \quad 
-  \sigma_i=\sqrt{\frac{1}{2\,\nu_i}}}{y[i] = log(s[i])  and  sigma[i] =
-  sqrt(1/(2*nu[i]))}
+  \sigma_i=\sqrt{\frac{1}{2\,\nu_i}}}{y[i] = log(s[i])  and  sigma[i] = sqrt(1/(2*nu[i]))}
   
   The \emph{unit information standard deviation} for a logarithmic
   standard deviation then is at approximately
@@ -100,11 +99,9 @@ exp(bm$summary[c(2,5,6),"theta"])
 # prediction (variances):
 exp(2 * bm$summary[c(2,5,6),"theta"])
 
-# sample size formula (12) (per arm):
-alpha <- 0.025
-beta  <- 0.20
-delta <- 8
-10.9^2 * 2*(qnorm(alpha) + qnorm(beta))^2 / delta^2
+# compute required sample size (per arm):
+power.t.test(n=NULL, delta=8, sd=10.9, power=0.8)
+power.t.test(n=NULL, delta=8, sd=14.0, power=0.8)
 
 # check UISD:
 uisd(es, indiv=TRUE)