Package: pseval 1.3.1

pseval: Methods for Evaluating Principal Surrogates of Treatment Response

Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.

Authors:Michael C Sachs [aut, cre], Erin E Gabriel [aut]

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pseval.pdf |pseval.html
pseval/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/sachsmc/pseval/issues

On CRAN:

3.88 score 1 stars 15 scripts 175 downloads 25 exports 3 dependencies

Last updated 6 years agofrom:458035b9ec. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 23 2024
R-4.5-winNOTEOct 23 2024
R-4.5-linuxNOTEOct 23 2024
R-4.4-winOKOct 23 2024
R-4.4-macOKOct 23 2024
R-4.3-winOKOct 23 2024
R-4.3-macOKOct 23 2024

Exports:add_integrationadd_riskmodelcalc_riskcalc_STGempirical_TEempirical_VEgenerate_example_dataintegrate_bivnormintegrate_nonparametricintegrate_parametricintegrate_semiparametricps_bootstrapps_estimatepsdesignpseudo_scorerisk_binaryrisk_continuousrisk_exponentialrisk_poissonrisk_weibullrisk.logitrisk.probitsp_locscalesummarize_bswem_test

Dependencies:latticeMatrixsurvival

Introduction to Principal Surrogate Evaluation in R

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Oct 23 2024.

Last update: 2019-01-25
Started: 2015-10-30

Readme and manuals

Help Manual

Help pageTopics
Modify a psdesign object by adding on new components.+.ps
Bootstrap resampling parametersadd_bootstrap
Estimate parametersadd_estimate
Integration modelsadd_integration
Add risk model to a psdesign objectadd_riskmodel
Calculate the risk and functions of the riskcalc_risk
Calculate the Standardized total gaincalc_STG
Compute the empirical Treatment Efficacyempirical_TE
Compute the empirical Treatment Efficacyempirical_VE
Expand augmented data using the integration functionexpand_augdata
Generate sample data used for testinggenerate_example_data
Bivariate normal integration models for the missing S(1)integrate_bivnorm
Nonparametric integration model for the missing S(1)integrate_nonparametric
Parametric integration model for the missing S(1)integrate_parametric
Semiparametric integration model using the location-scale modelintegrate_semiparametric
Plot summary statistics for a psdesign objectplot.psdesign
Concisely print information about a psdesign objectprint.psdesign
Estimate parameters from a specified model using bootstrap resampling and estimated maximum likelihoodps_bootstrap
Estimate parameters from a specified model using estimated maximum likelihoodps_estimate
Specify a design for a principal surrogate evaluationpsdesign
Estimate parameters from a specified model using pseudo-scorepseudo_score
Risk model for binary outcomerisk_binary
Risk model for continuous outcomerisk_continuous
Exponential risk model for time to event outcomerisk_exponential
Poisson risk model for count outcomesrisk_poisson
Weibull risk model for time to event outcomerisk_weibull
Logit link functionrisk.logit
Probit link functionrisk.probit
Calculate risks with handlers for survival datariskcalc
Fit the semi-parametric location-scale modelsp_locscale
Compute the standardized total gainstg
Summarize bootstrap samplessummarize_bs
Summary method for psdesign objectssummary.psdesign
Treatment efficacy contrast functionsTE
Check that a variable is suitable for using as binary treatment indicatorverify_trt
Test for wide effect modificationwem_test