View source: R/SsPowerGivenJK.R
SsPowerGivenJK | R Documentation |
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
)
dataset |
The pilot dataset. If set to NULL then variance components must be supplied. |
... |
Optional variance components: needed if |
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 |
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 |
alpha |
The significance level, default is 0.05. |
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.
The expected statistical power in pivotal study for the given conditions and J and K.
The procedure is valid for ROC studies only; for FROC studies see Vignettes 19.
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).
## 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))
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