SsPowerTable

The pilot ROC dataset to be used to extrapolate 
to the pivotal study.

dataset

The effect size to be used in the pivotal study, 
default value is <code>NULL</code>. See Details.

effectSize

The The size of the test, default is 0.05.

alpha

The desired statistical power, default is 0.8.

desiredPower

Analysis method, "DBMH" or "ORH", the default is "DBMH".

method

Desired generalization, "RRRC", "FRRC", "RRFC" or "ALL" (the default).

option

Generate combinations of numbers of readers J and numbers of cases K
 for desired power and specified generalization(s)

Implements software for assessing medical imaging systems, radiologists or computer aided detection algorithms.
Models of observer performance are implemented, including the binormal model (BM), the contaminated binormal model (CBM), the
correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). The software and applications are
described in a book - Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and
Applications with R-Based Examples. Taylor-Francis LLC; 2017 - and its vignettes <https://dpc10ster.github.io/RJafroc/>.
Observer performance data collection paradigms are the receiver operating characteristic (ROC) and its location specific
extensions, primarily free-response ROC (FROC) and the location ROC (LROC). ROC data consists of single ratings per images.
A rating is the perceived confidence level that the image is that of a diseased patient. FROC data consists of a variable
number (including zero) of mark-rating pairs per image, where a mark is the location of a clinically reportable suspicious region
and the rating is the corresponding confidence level that it is a real lesion. LROC data consists of a rating and a forced
localization of the most suspicious region on every image. RJafroc supersedes the Windows version of JAFROC software
V4.2.1, <http://www.devchakraborty.com>:. Package functions are organized as follows. Data file related function names are
preceded by Df, curve fitting functions by Fit, included data sets by dataset, plotting functions by
Plot, significance testing functions by St, sample size related functions by Ss, data simulation functions
by Simulate and utility functions by Util. Implemented are figures of merit (FOMs) for quantifying performance,
functions for visualizing empirical operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC
(wAFROC) curves. Four maximum likelihood curve-fitting algorithms are implemented: the binormal model (BM), the contaminated
binormal model (CBM), the correlated contaminated binormal model (CORCBM) and the radiological search model (RSM). Unlike the
binormal model, CBM, CORCBM and RSM predict "proper" ROC curves that do not cross the chance diagonal. RSM fitting additionally
yields measures of search and lesion-classification performances. Search performance is the ability to find lesions while
avoiding finding non-lesions. Lesion-classification performance is the ability to correctly classify found lesions from found
non-lesions. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is
implemented via both Dorfman-Berbaum-Metz and the Obuchowski-Rockette methods, including Hillis' extensions. Also implemented are
single treatment analyses, which allow comparison of performance of a group of radiologists to a specified value, or comparison
to CAD to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed
treatment factors and the desire is to determined performance in each treatment factor averaged over all levels of the other factor.
Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study
to predict required numbers of readers and cases in a pivotal study to achieve a desired power. Utility and data file manipulation
functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be
viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's international
collaborations. This version corrects a few bugs noticed by users and extends the Excel file input format for
greater flexibility in handling non-crossed datasets and the sample size routines have been rewritten for ease of use.

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SsPowerTable: Generate a power table

Description

Usage

Arguments

Value

Details

Examples