`SsSampleSizeKGivenJ.Rd`

Number of cases to achieve the desired power, for specified number of readers J, and specified DBM or ORH analysis method

```
SsSampleSizeKGivenJ(
dataset,
...,
J,
FOM,
effectSize = NULL,
method = "OR",
alpha = 0.05,
desiredPower = 0.8,
analysisOption = "RRRC",
UseDBMHB2004 = FALSE
)
```

- dataset
The

**pilot**dataset. If set to NULL then variance components must be supplied.- ...
Optional variance components, VarTR, VarTC and VarErr. These are needed if dataset is not supplied.

- J
The number of readers in the

**pivotal**study.- FOM
The figure of merit. Not needed if variance components are supplied.

- effectSize
The effect size to be used in the

**pivotal**study. Default is NULL. Must be supplied if dataset is set to NULL and variance components are supplied.- method
"OR" (default) or "DBM".

- alpha
The significance level of the study, default is 0.05.

- desiredPower
The desired statistical power, default is 0.8.

- analysisOption
Specifies the random factor(s): "RRRC" (the default), "FRRC", or "RRFC".

- UseDBMHB2004
Logical, default is

`FALSE`

, if`TRUE`

the 2004 DBM method is used. Otherwise the OR method is used.

A list of two elements:

- K
The minimum number of cases K in the pivotal study to just achieve the desired statistical power, calculated for each value of

`analysisOption`

.- power
The predicted statistical power.

`effectSize`

= NULL uses the **observed** effect size in
the pilot study. A numeric value over-rides the default value. This
argument must be supplied if dataset = NULL and variance components
(the optional ... arguments) are supplied.

The procedure is valid for ROC studies only; for FROC studies see online books.

```
## the following two should give identical results
SsSampleSizeKGivenJ(dataset02, FOM = "Wilcoxon", effectSize = 0.05, J = 6, method = "DBM")
#> KRRRC powerRRRC
#> 1 170 0.8016186
a <- UtilDBMVarComp(dataset02, FOM = "Wilcoxon")$VarCom
SsSampleSizeKGivenJ(dataset = NULL, J = 6, effectSize = 0.05, method = "DBM", UseDBMHB2004 = TRUE,
list(VarTR = a["VarTR",1],
VarTC = a["VarTC",1],
VarErr = a["VarErr",1]))
#> KRRRC powerRRRC
#> 1 170 0.8016186
## the following two should give identical results
SsSampleSizeKGivenJ(dataset02, FOM = "Wilcoxon", effectSize = 0.05, J = 6, method = "OR")
#> KRRRC powerRRRC
#> 1 170 0.8016186
a <- UtilORVarComp(dataset02, FOM = "Wilcoxon")$VarCom
KStar <- length(dataset02$ratings$NL[1,1,,1])
SsSampleSizeKGivenJ(dataset = NULL, J = 6, effectSize = 0.05, method = "OR",
list(KStar = KStar,
VarTR = a["VarTR",1],
Cov1 = a["Cov1",1],
Cov2 = a["Cov2",1],
Cov3 = a["Cov3",1],
Var = a["Var",1]))
#> KRRRC powerRRRC
#> 1 170 0.8016186
# \donttest{
for (J in 6:10) {
ret <- SsSampleSizeKGivenJ(dataset02, FOM = "Wilcoxon", J = J, analysisOption = "RRRC")
message("# of readers = ", J, " estimated # of cases = ", ret$K,
", predicted power = ", signif(ret$powerRRRC,3), "\n")
}
#> # of readers = 6 estimated # of cases = 251, predicted power = 0.801
#> # of readers = 7 estimated # of cases = 211, predicted power = 0.801
#> # of readers = 8 estimated # of cases = 188, predicted power = 0.801
#> # of readers = 9 estimated # of cases = 173, predicted power = 0.801
#> # of readers = 10 estimated # of cases = 163, predicted power = 0.802
# }
```