pseval v1.3.1

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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.

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pseval: Methods for Evaluating Principal Surrogates of Treatment Response

Installation

pseval is an R package aimed at implementing existing methods for surrogate evaluation using a flexible and common interface. Development will take place on the Github page, and the current version of the package can be installed as shown below. First you must install the devtools package, if you haven't already install.packages("devtools").

devtools::install_github("sachsmc/pseval")

Check out the vignette for methodological details and information on how to use the package.

Check out the cheat sheet for a quick reference.

References

Functions in pseval

Name Description
calc_risk Calculate the risk and functions of the risk
empirical_VE Compute the empirical Treatment Efficacy
add_estimate Estimate parameters
add_integration Integration models
empirical_TE Compute the empirical Treatment Efficacy
risk_binary Risk model for binary outcome
TE Treatment efficacy contrast functions
+.ps Modify a psdesign object by adding on new components.
risk_continuous Risk model for continuous outcome
expand_augdata Expand augmented data using the integration function
print.psdesign Concisely print information about a psdesign object
integrate_nonparametric Nonparametric integration model for the missing S(1)
add_bootstrap Bootstrap resampling parameters
integrate_parametric Parametric integration model for the missing S(1)
add_riskmodel Add risk model to a psdesign object
risk_exponential Exponential risk model for time to event outcome
risk_weibull Weibull risk model for time to event outcome
risk_poisson Poisson risk model for count outcomes
integrate_semiparametric Semiparametric integration model using the location-scale model
riskcalc Calculate risks with handlers for survival data
plot.psdesign Plot summary statistics for a psdesign object
verify_trt Check that a variable is suitable for using as binary treatment indicator
sp_locscale Fit the semi-parametric location-scale model
stg Compute the standardized total gain
calc_STG Calculate the Standardized total gain
generate_example_data Generate sample data used for testing
integrate_bivnorm Bivariate normal integration models for the missing S(1)
summarize_bs Summarize bootstrap samples
ps_bootstrap Estimate parameters from a specified model using bootstrap resampling and estimated maximum likelihood
ps_estimate Estimate parameters from a specified model using estimated maximum likelihood
psdesign Specify a design for a principal surrogate evaluation
summary.psdesign Summary method for psdesign objects
wem_test Test for wide effect modification
pseudo_score Estimate parameters from a specified model using pseudo-score
risk.logit Logit link function
risk.probit Probit link function
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Vignettes of pseval

Name
introduction.Rmd
psreferences.bib
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Details

Type Package
Date 2019-01-10
License MIT + file LICENSE
LazyData TRUE
VignetteBuilder knitr
RoxygenNote 6.1.1
NeedsCompilation no
Packaged 2019-01-28 07:18:52 UTC; micsac
Repository CRAN
Date/Publication 2019-01-28 07:40:03 UTC

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