Package 'RCTrep' reference manual

Title:	Validation of Estimates of Treatment Effects in Observational Data
Description:	Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v1>.
Authors:	Lingjie Shen [aut, cre, cph], Gijs Geleijnse [aut], Maurits Kaptein [aut]
Maintainer:	Lingjie Shen <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.0
Built:	2024-11-20 04:23:05 UTC
Source:	https://github.com/duolajiang/rctrep

Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validation

Description

Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validation

Usage

call_dashboard(source.obj = NULL, target.obj = NULL, source.obj.rep = NULL)
call_dashboard(source.obj = NULL, target.obj = NULL, source.obj.rep = NULL)

Arguments

`source.obj`	an instantiated object of class `TEstimator`. The estimates of conditional average treatment effects are compared to those from `target.obj`.
`target.obj`	an instantiated object of class `TEstimator`. The estimates of conditional average treatment effects are regarded as unbiased of truth.
`source.obj.rep`	an instantiated object of class `SEstimator`. The estimates of conditional average treatment effects are compared to those from `target.obj`.

Value

an interactive interface visualizing results of four steps

Examples


source.data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
target.data <- RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),]

vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y')
)
confounders_sampling_name <- c("x2","x6")

source.obj <- TEstimator_wrapper(
 Estimator = "G_computation",
 data = source.data,
 vars_name = vars_name,
 outcome_method = "glm",
 outcome_form=y ~ x1 + x2 + x3 + z + z:x1 + z:x2 +z:x3+ z:x6,
 name = "RWD",
 data.public = FALSE
)

target.obj <- TEstimator_wrapper(
 Estimator = "Crude",
 data = target.data,
 vars_name = vars_name,
 name = "RCT",
 data.public = FALSE,
 isTrial = TRUE
)

strata <- c("x1","x4")
source.rep.obj <- SEstimator_wrapper(Estimator = "Exact",
                                     target.obj = target.obj,
                                     source.obj = source.obj,
                                     confounders_sampling_name =
                                     confounders_sampling_name)
source.rep.obj$EstimateRep(stratification = strata, stratification_joint = TRUE)

call_dashboard(source.obj = source.obj,
               target.obj = target.obj,
               source.obj.rep = source.obj.rep)


source.data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
target.data <- RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),]

vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y')
)
confounders_sampling_name <- c("x2","x6")

source.obj <- TEstimator_wrapper(
 Estimator = "G_computation",
 data = source.data,
 vars_name = vars_name,
 outcome_method = "glm",
 outcome_form=y ~ x1 + x2 + x3 + z + z:x1 + z:x2 +z:x3+ z:x6,
 name = "RWD",
 data.public = FALSE
)

target.obj <- TEstimator_wrapper(
 Estimator = "Crude",
 data = target.data,
 vars_name = vars_name,
 name = "RCT",
 data.public = FALSE,
 isTrial = TRUE
)

strata <- c("x1","x4")
source.rep.obj <- SEstimator_wrapper(Estimator = "Exact",
                                     target.obj = target.obj,
                                     source.obj = source.obj,
                                     confounders_sampling_name =
                                     confounders_sampling_name)
source.rep.obj$EstimateRep(stratification = strata, stratification_joint = TRUE)

call_dashboard(source.obj = source.obj,
               target.obj = target.obj,
               source.obj.rep = source.obj.rep)

Generating RCT data or observational data for the examples used in the package

Description

Generating RCT data or observational data for the examples used in the package

Usage

DGM(
  trial,
  n,
  var_name,
  p_success,
  tau,
  y0,
  log.ps = NULL,
  binary = FALSE,
  noise = 1,
  ...
)
DGM(
  trial,
  n,
  var_name,
  p_success,
  tau,
  y0,
  log.ps = NULL,
  binary = FALSE,
  noise = 1,
  ...
)

Arguments

`trial`	Logical indicating whether the treatment is randomly assigned in the generated data. If TRUE, RCT data is generated. Otherwise, observational data is generated.
`n`	A numeric value indicating the number of observations in the generated data
`var_name`	A character vector indicating the names of variables
`p_success`	the success probability of binary variables
`tau`	a character indicating the generation of the true treatment effect of each individual
`y0`	a character indicating the generation of the potential outcome under control
`log.ps`	a numeric value indicating the logit of propensity score
`binary`	logical indicating whether the outcome is binary or continuous variable
`noise`	a numeric value indicating the standard error of noise term of continuous outcome
`...`	an optional argument indicating pairwise correlations between variables

Value

a data frame; column names are variables names, z, y

Examples

n_rct <- 500; n_rwd <- 500
var_name <- c("x1","x2","x3","x4","x5","x6")
p_success_rct <- c(0.7,0.9,0.2,0.3,0.2,0.3)
p_success_rwd <- c(0.2,0.2,0.8,0.8,0.7,0.8)
tau <- "6*x2+x6+2"
y0 <- "x1"
log.ps <- "x1*x2+x3*x4+5*x5+x6"
rho1 <- c("x1","x2",0)
rho2 <- c("x2","x3",0)

target.data <- RCTrep::DGM(trial=TRUE, n_rct, var_name,
                           p_success_rct, tau, y0, log.ps=0,
                           binary = FALSE, noise=1, rho1, rho2)
source.data <- RCTrep::DGM(trial=FALSE, n_rwd, var_name,
                           p_success_rwd, tau, y0, log.ps,
                           binary = FALSE, noise=1, rho1, rho2)


n_rct <- 500; n_rwd <- 500
var_name <- c("x1","x2","x3","x4","x5","x6")
p_success_rct <- c(0.7,0.9,0.2,0.3,0.2,0.3)
p_success_rwd <- c(0.2,0.2,0.8,0.8,0.7,0.8)
tau <- "6*x2+x6+2"
y0 <- "x1"
log.ps <- "x1*x2+x3*x4+5*x5+x6"
rho1 <- c("x1","x2",0)
rho2 <- c("x2","x3",0)

target.data <- RCTrep::DGM(trial=TRUE, n_rct, var_name,
                           p_success_rct, tau, y0, log.ps=0,
                           binary = FALSE, noise=1, rho1, rho2)
source.data <- RCTrep::DGM(trial=FALSE, n_rwd, var_name,
                           p_success_rwd, tau, y0, log.ps,
                           binary = FALSE, noise=1, rho1, rho2)

Validation of estimates of conditional average treatment effects in objects of class `TEstimator` and `SEstimator`.

Description

Validation of estimates of conditional average treatment effects in objects of class TEstimator and SEstimator.

Value

an R6 object

Methods

Method `new()`

Usage

Fusion$new(..., stratification = NULL, stratification_joint = NULL)

Arguments

...: objects of class TEstimator and SEstimator.
stratification: a character vector specifying variables. The variables are used to select subgroups individually or in combination depending on stratification_joint. Default value is NULL.
stratification_joint: a logical indicating if subgroups are selected based on levels of individual variable in stratification or levels of combined variables in stratifiation. Default value is NULL.

Method `plot()`

Usage

Fusion$plot()

Method `print()`

Usage

Fusion$print()

Method `evaluate()`

Usage

Fusion$evaluate()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Fusion$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


source.data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
target.data <- RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),]

vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y')
)
confounders_sampling_name <- c("x2","x6")

source.obj <- TEstimator_wrapper(
 Estimator = "G_computation",
 data = source.data,
 vars_name = vars_name,
 outcome_method = "glm",
 outcome_form=y ~ x1 + x2 + x3 + z + z:x1 + z:x2 +z:x3+ z:x6,
 name = "RWD",
 data.public = FALSE
)

target.obj <- TEstimator_wrapper(
 Estimator = "Crude",
 data = target.data,
 vars_name = vars_name,
 name = "RCT",
 data.public = FALSE,
 isTrial = TRUE
)

strata <- c("x1","x4")
source.rep.obj <- SEstimator_wrapper(Estimator = "Exact",
                                     target.obj = target.obj,
                                     source.obj = source.obj,
                                     confounders_sampling_name =
                                     confounders_sampling_name)
source.rep.obj$EstimateRep(stratification = strata, stratification_joint = TRUE)

fusion <- Fusion$new(target.obj,
                     source.obj,
                     source.rep.obj)
fusion$plot()
fusion$evaluate()


source.data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
target.data <- RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),]

vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y')
)
confounders_sampling_name <- c("x2","x6")

source.obj <- TEstimator_wrapper(
 Estimator = "G_computation",
 data = source.data,
 vars_name = vars_name,
 outcome_method = "glm",
 outcome_form=y ~ x1 + x2 + x3 + z + z:x1 + z:x2 +z:x3+ z:x6,
 name = "RWD",
 data.public = FALSE
)

target.obj <- TEstimator_wrapper(
 Estimator = "Crude",
 data = target.data,
 vars_name = vars_name,
 name = "RCT",
 data.public = FALSE,
 isTrial = TRUE
)

strata <- c("x1","x4")
source.rep.obj <- SEstimator_wrapper(Estimator = "Exact",
                                     target.obj = target.obj,
                                     source.obj = source.obj,
                                     confounders_sampling_name =
                                     confounders_sampling_name)
source.rep.obj$EstimateRep(stratification = strata, stratification_joint = TRUE)

fusion <- Fusion$new(target.obj,
                     source.obj,
                     source.rep.obj)
fusion$plot()
fusion$evaluate()

Generating the synthetic RCT data given marginal distribution of each covariate

Description

Generating the synthetic RCT data given marginal distribution of each covariate

Usage

GenerateSyntheticData(margin_dis, N, margin, var_name, pw.cor = 0)
GenerateSyntheticData(margin_dis, N, margin, var_name, pw.cor = 0)

Arguments

`margin_dis`	a character indicating the distribution of each variable, allowable options are `"bernoulli_categorical"` and `"bernoulli"`. If some variables have two categories and some have more than two categories, `"bernoulli_categorical"` should be specified; if all variables have two categories, `"bernoulli"` should be specified
`N`	a numeric value specifying the sample size for the simulated data
`margin`	a list containing the marginal distribution of variables; if margin_dis="bernoulli_categorical", then margin should be list(x1=c("x1",nlevels(x1),level1, level2,...,leveln, plevel1, plevel2,...,plevel3), x2=c("x2",...)); if margin_dis="bernoulli", margin=list(p(x1=1),p(x2=1),...,p(xn=1))
`var_name`	a vector indicating the name of variables, the order of variables should be aligned with `margin`
`pw.cor`	a vector specifying the pairwise correlations of the variables, default is 0; when margin_dis="bernoulli", then pw.cor must be specified.

Value

a data frame with columns names x1, x2,....

Aggregated data derived from paper of QUASAR trial

Description

Aggregated data derived from paper of QUASAR trial

Usage

quasar.agg
quasar.agg

Format

An object of class list of length 5.

An object of class TEstimator_Synthetic using quasar.synthetic

Description

An object of class TEstimator_Synthetic using quasar.synthetic

Usage

quasar.obj
quasar.obj

Format

An object of class TEstimator_Synthetic (inherits from TEstimator, R6) of length 15.

A synthetic QUASAR trial dataset, where outcome is a binary variable, treatment is a binary variable.

Description

A synthetic QUASAR trial dataset, where outcome is a binary variable, treatment is a binary variable.

Usage

quasar.synthetic
quasar.synthetic

Format

## 'quasar.synthetic' A data frame with 5934 rows and 3 variables:

Stage2: binary variable, 1 indicating stage 2 and 0 indicating stage 3
male: binary variable, 1 indicating male and 0 indicating female
age: categorical variable, 1 indicating [23,50], 2 indicating [50,59], 3 indicating [60,69], 4 indicating [70,86]

Replicate treatment effect estimates obtained from a randomized control trial using observational data

Description

The function RCTREP is used to validate the estimates of treatment effects obtained from observational data by comparing to estimates from a target randomized control trial. The function currently implements the following types of estimators of treatment effects: G_computation, inverse propensity score weighting (IPW), and augmented propensity score weighting. The function implements the following three types of weighting estimators to compare the resulting estimates of treatment effects from RWD to the target RCT: exact matching weights, inverse selection probability weighting, and sub-classification. Since we regard the sample in the RCT as the target population, weights for each individual in observational data is $p/(1-p)$ so that the weighted population of observational data is representative to the target population.

Usage

RCTREP(
  TEstimator = "G_computation",
  SEstimator = "Exact",
  source.data = source.data,
  target.data = target.data,
  source.name = "RWD",
  target.name = "RCT",
  vars_name,
  confounders_sampling_name,
  outcome_method = "glm",
  treatment_method = "glm",
  weighting_method = "glm",
  outcome_formula = NULL,
  treatment_formula = NULL,
  selection_formula = NULL,
  stratification = NULL,
  stratification_joint = FALSE,
  strata_cut_source = NULL,
  strata_cut_target = NULL,
  two_models = FALSE,
  data.public = TRUE,
  ...
)
RCTREP(
  TEstimator = "G_computation",
  SEstimator = "Exact",
  source.data = source.data,
  target.data = target.data,
  source.name = "RWD",
  target.name = "RCT",
  vars_name,
  confounders_sampling_name,
  outcome_method = "glm",
  treatment_method = "glm",
  weighting_method = "glm",
  outcome_formula = NULL,
  treatment_formula = NULL,
  selection_formula = NULL,
  stratification = NULL,
  stratification_joint = FALSE,
  strata_cut_source = NULL,
  strata_cut_target = NULL,
  two_models = FALSE,
  data.public = TRUE,
  ...
)

Arguments

`TEstimator`	A character specifying an estimator for conditional average treatment effects. The allowed estimators for `TEstimator` are: `"G_computation"`, `"IPW"`, and `"DR"`. The corresponding object will be created by the wrapper function `TEstimator_wrapper()`. The default is `"G_computation"`, which, along with `outcome_method="glm"` models the potential outcomes.
`SEstimator`	A character specifying an estimator for weight. The allowed estimators are: `"Exact"`, `"Subclass"`, `"ISW"`. The default is `"Exact"`, which, implements the exact matching on variables in `confounders_sampling_name` to balance the population covariates between `source.data` and `target.data`.
`source.data`	A data frame containing variables named in `vars_name` and possible other variables. `source.obj` is instantiated using `source.data`.
`target.data`	A data frame containing variables named in `vars_name` and possible other variables. `target.obj` is instantiated using `target.data`.
`source.name`	A character indicating the name of `source.obj`.
`target.name`	A character indicating the name of `target.obj`.
`vars_name`	A list containing four vectors `confounders_treatment_name`, `treatment_name`, and `outcome_name`. `confounders_treatment_name` is a character vector containing the adjustment variables, which, along with `TEstimator` and the corresponding `outcome_method` or `treatment_method` to correct for confounding; `outcome_name` is a character vector of length one containing the variable name of outcome; `treatment_name` is a character vector of length one containing the variable name of treatment.
`confounders_sampling_name`	a character vector specifying variable names. The weights are estimated based on the variables.
`outcome_method`, `treatment_method`, `weighting_method`	A character specifying model for outcome, treatment, and weight to use. Possible values are found using `names(getModelInfo())`. See http://topepo.github.io/caret/train-models-by-tag.html.
`outcome_formula`, `treatment_formula`, `selection_formula`	An optional object of class `formula` describing the outcome model specification, treatment model specification, and selection model specification.
`stratification`	An optional character vector containing variables to select subgroups. `source.obj` will compute both weighted and unweighted average treatment effects of the subgroups, `targe.obj` will calculate the average treatment effects of the subgroups.
`stratification_joint`	An optional logical indicating if the subgroups are selected based on levels of combined variables in `stratification` or levels of individual variable in `stratification`.
`strata_cut_source`	An optional list containing lists. Each component is a list with tag named by a variable in `source.data` to discretize, containing `break` which is a vector specifying the interval of range of the variable to divide, `lable` which is a character vector specifying how to code value in the variable according to which interval they fall. The leftmost interval corresponds to level one, the next leftmost to level two and so on. This parameter is useful in the case we concern the integrated treatment effect conditioning on variables with multiple levels (for instance, continuous variable or ordinal variable with multiple levels). Note that we first model based on these continuous variables, then we discretize these variables according to `strata_cut`. The variables in `data` of `TEstimator` object are discretized, and the weight is calculated based on the discretized variables.
`strata_cut_target`	An optional list containing lists. Each component is a list with tag named by a variable in `target.data` to discretize.
`two_models`	An optional logical indicating whether potential outcomes should be modeled separately when `TEstimator="DR"`. Default is `FALSE`.
`data.public`	An optional logical indicating whether the `data` in the output objects are public. Default is `TRUE`.
`...`	An optional argument passed to `fit()` of each estimator object for model training and tuning. See https://topepo.github.io/caret/model-training-and-tuning.html for details.

Details

An R6 object is constructed by a wrapper function TEstimator_wrapper and SEstimator_wrapper with user's input of data and estimators for treatment effect and weight. TEstimator_wrapper() returns initialized objects source.obj and target.obj. SEstimator_wrapper() weights the estimates of source.obj via the class method RCTrep(). The weights are computed using data in the source object source.obj, target object target.obj, and estimator of weights SEstimator.

Value

A list of length three with three R6 class objects, source.obj, target.obj and source.rep.obj

Examples


output <- RCTREP(TEstimator = "G_computation", SEstimator = "Exact",
                 outcome_method = "BART",
                 source.data = RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),],
                 target.data = RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),],
                 vars_name = list(confounders_treatment_name =
                                    c("x1","x2","x3","x4","x5","x6"),
                                 treatment_name = c('z'),
                                 outcome_name = c('y')),
                 confounders_sampling_name = c("x2","x6"),
                 stratification = c("x1","x3","x4","x5"),
                 stratification_joint = TRUE)
output$target.obj
output$source.obj
output$source.rep.obj

output <- RCTREP(TEstimator = "G_computation", SEstimator = "Exact",
                 outcome_method = "BART",
                 source.data = RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),],
                 target.data = RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),],
                 vars_name = list(confounders_treatment_name =
                                    c("x1","x2","x3","x4","x5","x6"),
                                 treatment_name = c('z'),
                                 outcome_name = c('y')),
                 confounders_sampling_name = c("x2","x6"),
                 stratification = c("x1","x3","x4","x5"),
                 stratification_joint = TRUE)
output$target.obj
output$source.obj
output$source.rep.obj

Estimating the weighted conditional average treatment effects in `source.obj` based on input objects `source.obj` and `target.obj` of class `TEstimator`.

Description

Estimating the weighted conditional average treatment effects in source.obj based on input objects source.obj and target.obj of class TEstimator.

Usage

SEstimator_wrapper(
  Estimator,
  target.obj,
  source.obj,
  confounders_sampling_name,
  method = "glm",
  sampling_formula = NULL,
  ...
)
SEstimator_wrapper(
  Estimator,
  target.obj,
  source.obj,
  confounders_sampling_name,
  method = "glm",
  sampling_formula = NULL,
  ...
)

Arguments

`Estimator`	a character specifying an estimator for weight. The allowed estimators are `"Exact"`, `"ISW"`, and `"Subclass"`.
`target.obj`, `source.obj`	an instantiated object of class `TEstimator`.
`confounders_sampling_name`	a character vector specifying the names of variables in `data` of `source.obj` and `target.obj`. Weights are estimated based on the variables.
`method`	an optional character specifying a model for estimating sampling probability when `Estimator='ISW'` or `Estimator='Subclass'`.
`sampling_formula`	an object of class `formula` specifying a model specification for sampling probability. Default value is `NULL`.
`...`	an optional argument specifying training and tuning for a model of sampling probability. See https://topepo.github.io/caret/model-training-and-tuning.html for details.

Value

An object of class SEstimator

Examples


source.data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
target.data <- RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),]

vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y'))

target.obj <- TEstimator_wrapper(
  Estimator = "Crude",
  data = target.data,
  vars_name = vars_name,
  name = "RCT",
  data.public = FALSE,
  isTrial = TRUE)

source.obj <- TEstimator_wrapper(
  Estimator = "G_computation",
  data = source.data,
  vars_name = vars_name,
  outcome_method = "glm",
  outcome_form=y ~ x1 + x2 + x3 + z + z:x1 + z:x2 +z:x3+ z:x6,
  name = "RWD",
  data.public = TRUE)

source.rep.obj <- SEstimator_wrapper(Estimator="Exact",
                                     target.obj=target.obj,
                                     source.obj=source.obj,
                                     confounders_sampling_name=c("x2","x6"))
source.rep.obj$EstimateRep(stratification = c("x1","x3","x4","x5"),
                           stratification_joint = TRUE)


source.data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
target.data <- RCTrep::target.data[sample(dim(RCTrep::target.data)[1],500),]

vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y'))

target.obj <- TEstimator_wrapper(
  Estimator = "Crude",
  data = target.data,
  vars_name = vars_name,
  name = "RCT",
  data.public = FALSE,
  isTrial = TRUE)

source.obj <- TEstimator_wrapper(
  Estimator = "G_computation",
  data = source.data,
  vars_name = vars_name,
  outcome_method = "glm",
  outcome_form=y ~ x1 + x2 + x3 + z + z:x1 + z:x2 +z:x3+ z:x6,
  name = "RWD",
  data.public = TRUE)

source.rep.obj <- SEstimator_wrapper(Estimator="Exact",
                                     target.obj=target.obj,
                                     source.obj=source.obj,
                                     confounders_sampling_name=c("x2","x6"))
source.rep.obj$EstimateRep(stratification = c("x1","x3","x4","x5"),
                           stratification_joint = TRUE)

A dataset of simulated observational data, where outcome is binary variable. The data is filtered after compared to target.binary.data

Description

A dataset of simulated observational data, where outcome is binary variable. The data is filtered after compared to target.binary.data

Usage

source.binary.data
source.binary.data

Format

A data frame with 2624 rows and 9 variables.

x1: binary variable, x1 ~ rbinom(5000,1,0.2)
x2: binary variable, x2 ~ rbinom(5000,1,0.2)
x3: binary variable, x3 ~ rbinom(5000,1,0.8)
x4: binary variable, x4 ~ rbinom(5000,1,0.8)
x5: binary variable, x5 ~ rbinom(5000,1,0.7)
x6: binary variable, x6 ~ rbinom(5000,1,0.8)
z: binary variable. pp = x1*x2+x3*x4+5*x5+x6, p(z=1) = p = 1/1+e^-(pp-mean(pp))/sd(pp)*sqrt(3)/pi, z ~ rbinom(5000,1,p)
y: binary variable. pp = x1 + (6*x2+x6+2)*z, p(y=1) = p = 1/1+e^-(pp-mean(pp))/sd(pp)*sqrt(3)/pi, y ~ rbinom(5000,1,p)
pt: a continuous variable within 0 and 1, specifying the probability of p(z=1) given x1,x2,x3,x4,x5,x6

A data set of simulated observational data, where outcome is continuous variable, treatment is a binary variable.

Description

A data set of simulated observational data, where outcome is continuous variable, treatment is a binary variable.

Usage

source.data
source.data

Format

## 'source.data' A data frame with 5000 rows and 8 variables:

x1: binary variable, x1 ~ rbinom(5000,1,0.2)
x2: binary variable, x2 ~ rbinom(5000,1,0.2)
x3: binary variable, x3 ~ rbinom(5000,1,0.8)
x4: binary variable, x4 ~ rbinom(5000,1,0.8)
x5: binary variable, x5 ~ rbinom(5000,1,0.7)
x6: binary variable, x6 ~ rbinom(5000,1,0.8)
z: binary variable indicating treatment and control. pp = x1*x2+x3*x4+5*x5+x6, p(z=1) = p = 1/1+e^-(pp-mean(pp))/sd(pp)*sqrt(3)/pi, z ~ rbinom(5000,1,p)
y: continuous variable indicating outcome, y ~ x1 + 6*x2+x6+2*z + rnorm(5000,0,1)

A dataset of simulated RCT data, where outcome is binary variable. The data is filtered after compared to source.binary.data

Description

A dataset of simulated RCT data, where outcome is binary variable. The data is filtered after compared to source.binary.data

Usage

target.binary.data
target.binary.data

Format

A data frame with 3194 rows and 9 variables.

x1: binary variable, x1 ~ rbinom(5000,1,0.7)
x2: binary variable, x2 ~ rbinom(5000,1,0.9)
x3: binary variable, x3 ~ rbinom(5000,1,0.2)
x4: binary variable, x4 ~ rbinom(5000,1,0.3)
x5: binary variable, x5 ~ rbinom(5000,1,0.2)
x6: binary variable, x6 ~ rbinom(5000,1,0.3)
z: binary variable. pp = x1*x2+x3*x4+5*x5+x6, p(z=1) = p = 1/1+exp^-(pp-mean(pp))/sd(pp)*sqrt(3)/pi, z ~ rbinom(5000,1,p)
y: binary variable. pp = x1 + (6*x2+x6+2)*z, p(y=1) = p = 1/1+exp^-(pp-mean(pp))/sd(pp)*sqrt(3)/pi, y ~ rbinom(5000,1,p)
pt: a continuous variable within 0 and 1, specifying the probability of p(z=1) given x1,x2,x3,x4,x5,x6

A data set of simulated RCT data, where outcome is continuous variable, treatment is a binary variable.

Description

A data set of simulated RCT data, where outcome is continuous variable, treatment is a binary variable.

Usage

target.data
target.data

Format

## 'target.data' A data frame with 5000 rows and 8 variables:

x1: binary variable, x1 ~ rbinom(5000,1,0.7)
x2: binary variable, x2 ~ rbinom(5000,1,0.9)
x3: binary variable, x3 ~ rbinom(5000,1,0.2)
x4: binary variable, x4 ~ rbinom(5000,1,0.3)
x5: binary variable, x5 ~ rbinom(5000,1,0.2)
x6: binary variable, x6 ~ rbinom(5000,1,0.3)
z: binary variable indicating treatment and control, z ~ rbinom(5000,1,0.5)
y: continuous variable indicating outcome, y ~ x1 + 6*x2+x6+2*z + rnorm(5000,0,1)

Estimating conditional average treatment effects

Description

Estimating conditional average treatment effects

Usage

TEstimator_wrapper(
  Estimator,
  data,
  vars_name,
  name = "",
  outcome_method = "glm",
  treatment_method = "glm",
  two_models = FALSE,
  outcome_formula = NULL,
  treatment_formula = NULL,
  data.public = TRUE,
  isTrial = FALSE,
  strata_cut = NULL,
  ...
)
TEstimator_wrapper(
  Estimator,
  data,
  vars_name,
  name = "",
  outcome_method = "glm",
  treatment_method = "glm",
  two_models = FALSE,
  outcome_formula = NULL,
  treatment_formula = NULL,
  data.public = TRUE,
  isTrial = FALSE,
  strata_cut = NULL,
  ...
)

Arguments

`Estimator`	A character specifying an estimator for conditional average treatment effects. The allowed estimators are: `"G_computation"`, `"IPW"`, and `"DR"`.i The corresponding object wll be created by the function `TEstimator_wrapper()`. The default is `"G_computation"`, which, along with `outcome_method="glm"` models the potential outcomes.
`data`	A data frame containing variables named in `vars_name` and possible other variables.
`vars_name`	A list containing four character vectors `confounders_treatment_name`, `treatment_name`, and `outcome_name`. `confounders_treatment_name` is a character vector containing the adjustment variables, which, along with `TEstimator` and the corresponding `outcome_method` or `treatment_method` to correct for confounding; `outcome_name` is a character vector of length one containing the name of outcome; `treatment_name` is a character vector of length one containing the name of treatment.
`name`	A character indicating the name of the output object
`outcome_method`	A character specifying a model for outcome. Possible values are found using `names(getModelInfo())`. See http://topepo.github.io/caret/train-models-by-tag.html. Default is "glm".
`treatment_method`	A character specifying a model for treatment. Possible values are found using `names(getModelInfo())`. See http://topepo.github.io/caret/train-models-by-tag.html. Default is "glm".
`two_models`	An optional logical indicating whether potential outcomes should be modeled separately when `TEstimator="DR"`. Default is `FALSE`.
`outcome_formula`	An optional object of class `formula` describing the outcome model specification when `Estimator="G_computation"` or `Estimator="DR"`.
`treatment_formula`	An optional object of class `formula` describing the treatment model specification when `Estimator="IPW"` or `Estimator="DR"`
`data.public`	An optional logical indicating whether individual-level `data` is public in the output object. Default is `TRUE`.
`isTrial`	An optional logical indicating whether the treatment assignment of `data` is random or unknown.
`strata_cut`	An optional list containing lists. Each component is a list with tag named by a variable in `data` to discretize, containing `break` which is a vector specifying the interval of range of the variable to divide, `lable` which is a character vector specifying how to code value in the variable according to which interval they fall. The leftmost interval corresponds to level one, the next leftmost to level two and so on. This parameter is useful in the case we concern the integrated treatment effect conditioning on variables with multiple levels (for instance, continuous variable or ordinal variable with multiple levels). Note that we first model based on these continuous variables, then we discretize these variables according to `strata_cut`. The variables in `data` of the output object are discretized.
`...`	An optional argument passed to the private function `fit()` of each class for model training and tuning. See https://topepo.github.io/caret/model-training-and-tuning.html for details.

Value

An object of class TEstimator.

Examples


data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y'))

obj <- TEstimator_wrapper(
 Estimator = "G_computation",
 data = data,
 vars_name = vars_name,
 name = "RCT",
 data.public = TRUE,
 isTrial = FALSE)


data <- RCTrep::source.data[sample(dim(RCTrep::source.data)[1],500),]
vars_name <- list(confounders_treatment_name = c("x1","x2","x3","x4","x5","x6"),
                  treatment_name = c('z'),
                  outcome_name = c('y'))

obj <- TEstimator_wrapper(
 Estimator = "G_computation",
 data = data,
 vars_name = vars_name,
 name = "RCT",
 data.public = TRUE,
 isTrial = FALSE)

Package 'RCTrep'

Help Index

Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validation

Description

Usage

Arguments

Value

Examples

Generating RCT data or observational data for the examples used in the package

Description

Usage

Arguments

Value

Examples

Validation of estimates of conditional average treatment effects in objects of class TEstimator and SEstimator.

Description

Value

Methods

Public methods

Method new()

Usage

Arguments

Method plot()

Usage

Method print()

Usage

Method evaluate()

Usage

Method clone()

Usage

Arguments

Examples

Generating the synthetic RCT data given marginal distribution of each covariate

Description

Usage

Arguments

Value

Aggregated data derived from paper of QUASAR trial

Description

Usage

Format

An object of class TEstimator_Synthetic using quasar.synthetic

Description

Usage

Format

A synthetic QUASAR trial dataset, where outcome is a binary variable, treatment is a binary variable.

Description

Usage

Format

Replicate treatment effect estimates obtained from a randomized control trial using observational data

Description

Usage

Arguments

Details

Value

Examples

Estimating the weighted conditional average treatment effects in source.obj based on input objects source.obj and target.obj of class TEstimator.

Description

Usage

Arguments

Value

Examples

A dataset of simulated observational data, where outcome is binary variable. The data is filtered after compared to target.binary.data

Description

Usage

Format

A data set of simulated observational data, where outcome is continuous variable, treatment is a binary variable.

Description

Usage

Format

A dataset of simulated RCT data, where outcome is binary variable. The data is filtered after compared to source.binary.data

Description

Usage

Format

A data set of simulated RCT data, where outcome is continuous variable, treatment is a binary variable.

Description

Usage

Format

Estimating conditional average treatment effects

Description

Validation of estimates of conditional average treatment effects in objects of class `TEstimator` and `SEstimator`.

Method `new()`

Method `plot()`

Method `print()`

Method `evaluate()`

Method `clone()`

Estimating the weighted conditional average treatment effects in `source.obj` based on input objects `source.obj` and `target.obj` of class `TEstimator`.