Package index

Package overview

PDRobust PDRobust-package

PDRobust: Principal-stratification treatment-effect estimation

print(<pd_mapping>) print(<pd_data_check>) `[`(<pd_data>) print(<pd_hte_timevarying>) print(<pd_hte_pooled>) print(<PSDiag>) print(<PrinSDiag>) print(<odds_ratios>) print(<QR>) print(<SA>) plot(<pd_hte_timevarying>) plot(<pd_hte_pooled>) plot(<PSDiag>) plot(<PrinSDiag>) plot(<odds_ratios>)

Print, plot, and subset PDRobust results

Mapping and prepared data

Define the data-layout contract, validate raw data, and attach the standardized mapping.

Mapping()

Identify variables and analysis times for PDRobust

DataCheck()

Check whether longitudinal data are ready for analysis

DataStandard()

Prepare longitudinal data for PDRobust analyses

BiSample

Binary longitudinal example data

ImperfectConSample

Imperfect Continuous Longitudinal Example Data

Independent prediction functions

Refit and predict independently on every call without cached fitted models.

PSPred()

Estimate propensity scores

PrinPred()

Estimate cumulative principal scores

OutPred()

Estimate outcome predictions

Heterogeneous treatment effects

HTESepT()

Separate estimation of heterogeneous treatment effects by time point

HTEAllT()

Joint estimation of heterogeneous treatment effects across time

Diagnostics and supporting analyses

PSDiag()

Evaluate how well a propensity score model performs

PrinSDiag()

Evaluate covariate balance for the principal score model

QR()

Summary statistics of covariates within the always-survivor principal stratum

ORCI()

Estimate covariate associations with survival at the cutoff time

SA()

Sensitivity analysis of outcome mean model misspecification