Statistical power for specified numbers of readers and cases

Calculate the statistical power for specified numbers of readers J, cases K, analysis method and DBM or OR variances components

SsPowerGivenJK(
  dataset,
  ...,
  FOM,
  J,
  K,
  effectSize = NULL,
  method = "OR",
  covEstMethod = "jackknife",
  analysisOption = "RRRC",
  UseDBMHB2004 = FALSE,
  alpha = 0.05
)

Arguments

dataset: The pilot dataset. If set to NULL then variance components must be supplied.
...: Optional variance components: needed if dataset is not supplied.
FOM: The figure of merit.
J: The number of readers in the pivotal study.
K: The number of cases in the pivotal study.
effectSize: The effect size to be used in the pivotal study. Default is NULL, which uses the observed effect size in the pilot dataset. Must be supplied if dataset is set to NULL and variance components are supplied.
method: "OR" (the default) or "DBM" (but see UseDBMHB2004 option below).
covEstMethod: Specify the variance covariance estimation method(s): "jackknife" (the default), "bootstrap" or "DeLong" (for ROC datasets).
analysisOption: Specify the random factor(s): "RRRC" (the default), "RRFC or FRRC".
UseDBMHB2004: Logical, defaults to FALSE, which results in OR sample size method being used, even if DBM method is specified, as in Hillis 2011 & 2018 papers. If TRUE the method based on Hillis-Berbaum 2004 sample size paper is used.
alpha: The significance level, default is 0.05.

Value

The expected statistical power in pivotal study for the given conditions and J and K.

Details

The default effectSize 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 compenents (the ... arguments) are supplied.

Note

The procedure is valid for ROC studies only; for FROC studies see Vignettes 19.

References

Hillis SL, Berbaum KS (2004). Power Estimation for the Dorfman-Berbaum-Metz Method. Acad Radiol, 11, 1260–1273.

Hillis SL, Obuchowski NA, Berbaum KS (2011). Power Estimation for Multireader ROC Methods: An Updated and Unified Approach. Acad Radiol, 18, 129–142.

Hillis SL, Schartz KM (2018). Multireader sample size program for diagnostic studies: demonstration and methodology. Journal of Medical Imaging, 5(04).

Examples

## EXAMPLE 1: RRRC power 
## specify 2-modality ROC dataset and force DBM alg.
res <- SsPowerGivenJK(dataset = dataset02, FOM = "Wilcoxon", effectSize = 0.05, 
J = 6, K = 251, method = "DBM", UseDBMHB2004 = TRUE) # RRRC is default  

## EXAMPLE 1A: FRRC power 
res <- SsPowerGivenJK(dataset = dataset02, FOM = "Wilcoxon", effectSize = 0.05, 
J = 6, K = 251, method = "DBM", UseDBMHB2004 = TRUE, analysisOption = "FRRC") 

## EXAMPLE 1B: RRFC power 
res <- SsPowerGivenJK(dataset = dataset02, FOM = "Wilcoxon", effectSize = 0.05, 
J = 6, K = 251, method = "DBM", UseDBMHB2004 = TRUE, analysisOption = "RRFC") 

## EXAMPLE 2: specify NULL dataset & DBM var. comp. & force DBM-based alg.
vcDBM <- UtilDBMVarComp(dataset02, FOM = "Wilcoxon")$VarCom
res <- SsPowerGivenJK(dataset = NULL, FOM = "Wilcoxon", J = 6, K = 251, 
effectSize = 0.05, method = "DBM", UseDBMHB2004 = TRUE, 
list( 
VarTR = vcDBM["VarTR","Estimates"], # replace rhs with actual values as in 4A
VarTC = vcDBM["VarTC","Estimates"], # do:
VarErr = vcDBM["VarErr","Estimates"])) # do:
                     
## EXAMPLE 3: specify 2-modality ROC dataset and use OR-based alg.
res <- SsPowerGivenJK(dataset = dataset02, FOM = "Wilcoxon", effectSize = 0.05, 
J = 6, K = 251)

## EXAMPLE 4: specify NULL dataset & OR var. comp. & use OR-based alg.
JStar <- length(dataset02$ratings$NL[1,,1,1])
KStar <- length(dataset02$ratings$NL[1,1,,1])
vcOR <- UtilORVarComp(dataset02, FOM = "Wilcoxon")$VarCom
res <- SsPowerGivenJK(dataset = NULL, FOM = "Wilcoxon", effectSize = 0.05, J = 6, 
K = 251, list(JStar = JStar, KStar = KStar, 
   VarTR = vcOR["VarTR","Estimates"], # replace rhs with actual values as in 4A
   Cov1 = vcOR["Cov1","Estimates"],   # do:
   Cov2 = vcOR["Cov2","Estimates"],   # do:
   Cov3 = vcOR["Cov3","Estimates"],   # do:
   Var = vcOR["Var","Estimates"]))
   
## EXAMPLE 4A: specify NULL dataset & OR var. comp. & use OR-based alg.
res <- SsPowerGivenJK(dataset = NULL, FOM = "Wilcoxon", effectSize = 0.05, J = 6, 
K = 251, list(JStar = 5, KStar = 114, 
   VarTR = 0.00020040252,
   Cov1 = 0.00034661371,
   Cov2 = 0.00034407483,
   Cov3 = 0.00023902837,
   Var = 0.00080228827))
   
## EXAMPLE 5: specify NULL dataset & DBM var. comp. & use OR-based alg.
## The DBM var. comp. are converted internally to OR var. comp.
vcDBM <- UtilDBMVarComp(dataset02, FOM = "Wilcoxon")$VarCom
KStar <- length(dataset02$ratings$NL[1,1,,1])
res <- SsPowerGivenJK(dataset = NULL, J = 6, K = 251, effectSize = 0.05, 
method = "DBM", FOM = "Wilcoxon",
list(KStar = KStar,                # replace rhs with actual values as in 5A 
VarR = vcDBM["VarR","Estimates"], # do:
VarC = vcDBM["VarC","Estimates"], # do:
VarTR = vcDBM["VarTR","Estimates"], # do:
VarTC = vcDBM["VarTC","Estimates"], # do:
VarRC = vcDBM["VarRC","Estimates"], # do:
VarErr = vcDBM["VarErr","Estimates"]))

## EXAMPLE 5A: specify NULL dataset & DBM var. comp. & use OR-based alg.
res <- SsPowerGivenJK(dataset = NULL, J = 6, K = 251, effectSize = 0.05, 
method = "DBM", FOM = "Wilcoxon",
list(KStar = 114,
VarR = 0.00153499935,
VarC = 0.02724923428,
VarTR = 0.00020040252,
VarTC = 0.01197529621,
VarRC = 0.01226472859,
VarErr = 0.03997160319))