R/MPM_RBM_sim.R

In Morphonodepredictivemodel/morphonode: The Morphonode Predictive Model for ultrasound signatures detection and malignancy prediction in vulvar cancer

#  Morphonode Predictive Model (MPM) - The morphonode R package

#  Copyright (C) 2022 Fernando Palluzzi
#  e-mail: <fernando.palluzzi@gmail.com>
#  Bioinformatics facility 
#  Gemelli Science and Technological Park (GSTeP)
#  Fondazione Policlinico Universitario Agostino Gemelli IRCCS,
#  Largo Agostino Gemelli 8, 00168 Roma, Italy

#  morphonode is free software: you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation, either version 3 of the License, or
#  (at your option) any later version.

#  morphonode is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.

#  You should have received a copy of the GNU General Public License
#  along with this program.  If not, see <https://www.gnu.org/licenses/>.

# -------------------------------------------------------------------- #

#' @title Morphonode default Robust Binomial Model (RBM) endemble
#'
#' @description Logistic model generated by fitting a simulated dataset 
#'    of 948 ultrasound profiles (440 malignant and 508 non-malignant), 
#'    with robust bootstrap standard errors estimation (5000 bootstrap 
#'    iterations), through the internal morphonode function 
#'    \code{\link[morphonode]{boot.se}}. The input ultrasound feature 
#'    dataset was dichotomized before model fitting (see 
#'    \code{\link[morphonode]{dichotomize}}) to avoid parameter estimation 
#'    biases due to very low frequency of the levels of some categorical 
#'    ultrasound features. The RBM is used to estimate the malignancy risk, 
#'    (R) providing a continuous measure of malignancy (in contrast to the 
#'    dichotomous prediction of the RFC ensemble). Considering the 
#'    expected simulated phenotype (y) as the ground truth, two optimal 
#'    malignancy risk cutoffs were estimated, defining three risk levels: 
#'    low (R < 0.23), moderate (0.23 <= R <= 0.29), and high (R > 0.29).
#' @name mpm.rbm
#' @usage mpm.rbm
#' @docType data
#' @format
#' "mpm.rbm" is a list of 3 objects:
#' \enumerate{
#' \item "coef", a data.frame reporting bootstrap-based estimations:
#'    ultrasound feature (Variable), log(odds ratio) (Estimate), 
#'    bootstrap standard errors (se.boot), confidence interval lower 
#'    bound (lower), confidence interval upper bound (upper), 
#'    confidence level (conf.level), bootstrap estimation method (method), 
#'    z-score (z), 2-sided p-value (P);
#' \item "model", \code{R} formula representing the fitted model;
#' \item "fit", MLE-based fitted model object of class \code{glm}.
#' }
#'
#' @references
#' 
#' Fragomeni SM, Moro F, Palluzzi F, Mascilini F, Rufini V, Collarino A, 
#' Inzani F, Giacò L, Scambia G, Testa AC, Garganese G (2022). 
#' Evaluating the risk of inguinal lymph node metastases before surgery 
#' using the Morphonode Predictive Model: a prospective diagnostic study. 
#' Ultrasound xx Xxxxxxxxxx xxx Xxxxxxxxxx. 00(0):000-000.
#' <https://doi.org/00.0000/00000000000000000000>
#' 
#' @examples
#' 
#' # Create a simulated malignant ultrasound profile
#' x <- new.profile(us.simulate(y = 1))
#' 
#' # Lauch the Morhonode Predictive Model
#' u <- us.predict(x)
#'

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