Package 'mrPStrata'

Title: Multiply robust estimation in causal survival analysis with treatment noncompliance
Description: This package provides functions to calculate the multiply robust estimator of the principal survival causal effect among the always takers, compliers, and never takers.
Authors: Chao Cheng <[email protected]>, Bo Liu <[email protected]>, Fan Li <[email protected]>, Fan Li <[email protected]>
Maintainer: Chao Cheng <[email protected]>
License: GPL (>= 2)
Version: 0.1.0
Built: 2026-05-13 10:42:01 UTC
Source: https://github.com/chaochengstat/mrpstrata

Help Index

Bootstrap confidence intervals for the bias-corrected multiply robust estimator under violations of monotonicity

Description

Bootstrap confidence intervals for the bias-corrected multiply robust estimator under violations of monotonicity

Usage

BootEst.MO.SA(
  times,
  propensity.model,
  principal.model,
  censor.model,
  failure.model,
  data,
  zeta,
  estimand = c("NACE", "CACE", "AACE", "DACE"),
  bootstrap = 50
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
propensity.model

propensity score model
principal.model

principal score model
censor.model

censoring model
failure.model

outcome model
data

dataset
zeta

the sensitivity parameter zeta
estimand

the estimands of interest
bootstrap

number of bootstrap replications

Value

The bootstrap confidence intervals

Bootstrap confidence intervals for the multiply robust estimator

Description

Bootstrap confidence intervals for the multiply robust estimator

Usage

BootEst.PI.SA(
  times,
  propensity.model,
  principal.model,
  censor.model,
  failure.model,
  data,
  xi0,
  xi1,
  eta0,
  eta1,
  estimand = c("NACE", "CACE", "AACE"),
  bootstrap
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
propensity.model

propensity score model
principal.model

principal score model
censor.model

censoring model
failure.model

outcome model
data

dataset
xi0

sensitivity parameter xi_0
xi1

sensitivity parameter xi_1
eta0

sensitivity parameter eta_0
eta1

sensitivity parameter eta_1
estimand

the estimands of interest
bootstrap

number of bootstrap replications

Value

The bootstrap confidence intervals

Multiply robust estimator for calculating the principal survival causal effects among always takers, compliers, and never takers

Description

Multiply robust estimator for calculating the principal survival causal effects among always takers, compliers, and never takers

Usage

mrPStrata(
  times,
  data,
  Xpi_names,
  Xe_names,
  Xc_names,
  Xt_names,
  Z_name,
  S_name,
  U_name,
  delta_name,
  B = 100
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
data

the dataset
Xpi_names

names of the covariates for the propensity score model
Xe_names

names of the covariates for the principal score model
Xc_names

names of the covariates for the censoring model
Xt_names

names of the covariates for the failure outcome model
Z_name

name of the treatment assignment status
S_name

name of the true treatment receipt status
U_name

name of the observed failuture time
delta_name

name of the censoring indicator
B

number of the bootstrap replications (default 100)

Value

The PSCE estimates and their 95% confidence intervals

Examples

# example code
attach(sim_data)
res = mrPStrata(times=c(1,2,3,4,5,6,7,8),
                data = sim_data,
                Xpi_names = c("X1","X2","X3","X4","X5"),
                Xe_names = c("X1","X2","X3","X4","X5"),
                Xc_names = c("X1","X2","X3","X4","X5"),
                Xt_names = c("X1","X2","X3","X4","X5"),
                Z_name = "z",
                S_name = "s",
                U_name ="U",
                delta_name = "delta",
                B=50)
print(res)

Bias-corrected multiply robust estimator of the PSCE under violation of monotonicity

Description

Bias-corrected multiply robust estimator of the PSCE under violation of monotonicity

Usage

mrPStrata_MO_SA(
  times,
  data,
  Xpi_names,
  Xe_names,
  Xc_names,
  Xt_names,
  Z_name,
  S_name,
  U_name,
  delta_name,
  zeta = 0.01,
  B = 100
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
data

the dataset
Xpi_names

names of the covariates for the propensity score model
Xe_names

names of the covariates for the principal score model
Xc_names

names of the covariates for the censoring model
Xt_names

names of the covariates for the outcome model
Z_name

name of the treatment assignment status
S_name

name of the true treatment receipt status
U_name

name of the observed failuture time
delta_name

name of the censoring indicator
zeta

sensitivity parameter zeta
B

number of the bootstrap replications (default 100)

Value

The PSCE estimates and their 95% confidence intervals

Bias-corrected multiply robust estimator of the PSCE under violation of the principal ignorability assumption

Description

Bias-corrected multiply robust estimator of the PSCE under violation of the principal ignorability assumption

Usage

mrPStrata_PI_SA(
  times,
  data,
  Xpi_names,
  Xe_names,
  Xc_names,
  Xt_names,
  Z_name,
  S_name,
  U_name,
  delta_name,
  xi0 = 0,
  xi1 = 0,
  eta0 = 1,
  eta1 = 1,
  B = 100
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
data

the dataset
Xpi_names

names of the covariates for the propensity score model
Xe_names

names of the covariates for the principal score model
Xc_names

names of the covariates for the censoring model
Xt_names

names of the covariates for the outcome model
Z_name

name of the treatment assignment status
S_name

name of the true treatment receipt status
U_name

name of the observed failuture time
delta_name

name of the censoring indicator
xi0

sensitivity parameter xi_0
xi1

sensitivity parameter xi_1
eta0

sensitivity parameter eta_0
eta1

sensitivity parameter eta_1
B

number of the bootstrap replications (default 100)

Value

The PSCE estimates and their 95% confidence intervals

Plot of the PSCEs and their associated 95% pointwise confidence intervals

Description

Plot of the PSCEs and their associated 95% pointwise confidence intervals

Usage

## S3 method for class 'psce'
plot(res)

Arguments

res

an output from mrPStrata

Value

The PSCE point estimates and 95% pointwise confidence intervals

Point estimation for the multiply robust estimator under violation of monotonicity assumption

Description

Point estimation for the multiply robust estimator under violation of monotonicity assumption

Usage

PointEst.MO.SA(
  times,
  propensity.model,
  principal.model,
  censor.model,
  failure.model,
  data,
  zeta,
  estimand = c("NACE", "CACE", "AACE", "DACE")
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
propensity.model

propensity score model
principal.model

principal score model
censor.model

censoring model
failure.model

outcome model
data

dataset
zeta

the sensitivity parameter zeta
estimand

the estimands of interest

Value

The PSCE point estimates

Point estimation for the multiply robust estimator

Description

Point estimation for the multiply robust estimator

Usage

PointEst.PI.SA(
  times,
  propensity.model,
  principal.model,
  censor.model,
  failure.model,
  data,
  xi0,
  xi1,
  eta0,
  eta1,
  estimand = c("NACE", "CACE", "AACE")
)

Arguments

times

a vector of time when the principal survival causal effects (PSCEs) are of interest
propensity.model

propensity score model
principal.model

principal score model
censor.model

censoring model
failure.model

outcome model
data

dataset
xi0

sensitivity parameter xi_0
xi1

sensitivity parameter xi_1
eta0

sensitivity parameter eta_0
eta1

sensitivity parameter eta_1
estimand

the estimands of interest

Value

The PSCE point estimates