Package: predictNMB 0.2.1.9000

Rex Parsons

predictNMB: Evaluate Clinical Prediction Models by Net Monetary Benefit

Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) <doi:10.21105/joss.05328>.

Authors:Rex Parsons [aut, cre], Robin Blythe [aut], Adrian Barnett [aut], Emi Tanaka [rev], Tinula Kariyawasam [rev], Susanna Cramb [ctb], Steven McPhail [ctb]

predictNMB_0.2.1.9000.tar.gz
predictNMB_0.2.1.9000.zip(r-4.5)predictNMB_0.2.1.9000.zip(r-4.4)predictNMB_0.2.1.9000.zip(r-4.3)
predictNMB_0.2.1.9000.tgz(r-4.4-any)predictNMB_0.2.1.9000.tgz(r-4.3-any)
predictNMB_0.2.1.9000.tar.gz(r-4.5-noble)predictNMB_0.2.1.9000.tar.gz(r-4.4-noble)
predictNMB_0.2.1.9000.tgz(r-4.4-emscripten)predictNMB_0.2.1.9000.tgz(r-4.3-emscripten)
predictNMB.pdf |predictNMB.html✨
predictNMB/json (API)
NEWS

# Install 'predictNMB' in R:
install.packages('predictNMB', repos = c('https://ropensci.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ropensci/predictnmb/issues

Pkgdown:https://docs.ropensci.org

On CRAN:

6.23 score 10 stars 17 scripts 220 downloads 14 exports 44 dependencies

Last updated 5 months agofrom:8fd168237e (on master). Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-winOKDec 04 2024
R-4.5-linuxOKDec 04 2024
R-4.4-winOKDec 04 2024
R-4.4-macOKDec 04 2024
R-4.3-winOKDec 04 2024
R-4.3-macOKDec 04 2024

Exports:%>%autoplotce_plotdo_nmb_simevaluate_cutpoint_costevaluate_cutpoint_nmbevaluate_cutpoint_qalysget_inbuilt_cutpointget_inbuilt_cutpoint_methodsget_nmb_samplerget_sampleget_thresholdsscreen_simulation_inputstheme_sim

Dependencies:assertthatclicodetoolscolorspacecpp11cutpointrdplyrfansifarverforeachgenericsggplot2gluegridExtragtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpmsampsizepurrrR6RColorBrewerRcpprlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Creating NMB functions

Rendered fromcreating-nmb-functions.Rmdusingknitr::rmarkdownon Dec 04 2024.

Last update: 2023-05-30
Started: 2022-10-26

Detailed example: pressure injury model

Rendered fromdetailed-example.Rmdusingknitr::rmarkdownon Dec 04 2024.

Last update: 2023-05-30
Started: 2022-11-08

predictNMB

Rendered frompredictNMB.Rmdusingknitr::rmarkdownon Dec 04 2024.

Last update: 2023-06-02
Started: 2023-02-06

Summarising results from predictNMB

Rendered fromsummarising-results-with-predictNMB.Rmdusingknitr::rmarkdownon Dec 04 2024.

Last update: 2023-06-02
Started: 2022-09-26

Readme and manuals

Help Manual

Help pageTopics
Create plots of from screened predictNMB simulations.autoplot.predictNMBscreen
Create plots of from predictNMB simulations.autoplot.predictNMBsim
Create a cost-effectiveness plot.ce_plot
Create a cost-effectiveness plot.ce_plot.predictNMBsim
Do the predictNMB simulation, evaluating the net monetary benefit (NMB) of the simulated model.do_nmb_sim
Evaluates a cutpoint by returning the mean treatment cost per sample.evaluate_cutpoint_cost
Evaluates a cutpoint by returning the mean NMB per sample.evaluate_cutpoint_nmb
Evaluates a cutpoint by returning the mean QALYs lost per sample.evaluate_cutpoint_qalys
Get a cutpoint using the methods inbuilt to predictNMBget_inbuilt_cutpoint
Get a vector of all the inbuilt cutpoint methodsget_inbuilt_cutpoint_methods
Make a NMB sampler for use in 'do_nmb_sim()' or 'screen_simulation_inputs()'get_nmb_sampler
Samples data for a prediction model with a specified AUC and prevalence.get_sample
Gets probability thresholds given predicted probabilities, outcomes and NMB.get_thresholds
Print a summary of a predictNMBscreen objectprint.predictNMBscreen
Print a summary of a predictNMBsim objectprint.predictNMBsim
Screen many simulation inputs: a parent function to 'do_nmb_sim()'screen_simulation_inputs
Create table summaries of 'predictNMBscreen' objects.summary.predictNMBscreen
Create table summaries of 'predictNMBsim' objects.summary.predictNMBsim
Returns a 'ggplot2' theme that reduces clutter in an 'autoplot()' of a 'predictNMBsim' object.theme_sim