Learn R Programming

TrialEmulation

This package implements algorithms to conduct a sequence of target trials analysis.

Installation

The package is available on CRAN:

install.packages('TrialEmulation')

To install the latest development version from github:

remotes::install_github("Causal-LDA/TrialEmulation")

Getting Started

Please see our Getting Started vignette for information on how to work with this package.

A manuscript is in preparation to fully describe the methodology.

Copy Link

Version

Install

install.packages('TrialEmulation')

Monthly Downloads

540

Version

0.0.5

License

Apache License (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/Causal-LDA/TrialEmulation

Homepage

https://causal-lda.github.io/TrialEmulation/

Maintainer

Isaac Gravestock

Last Published

August 25th, 2026

Functions in TrialEmulation (0.0.5)

Fit outcome models using parsnip models

predict_marginal

Predict marginal cumulative incidences with confidence intervals for a target trial population

fit_outcome_model

Method for fitting outcome models

Outcome Data Accessor and Setter

IPW Data Accessor and Setter

read_expanded_data

Method to read expanded data

load_expanded_data

Method to read, subset and sample expanded data

sample_expanded_data

Internal method to sample expanded data

Save expanded data to DuckDB

save_expanded_data

Method to save expanded data

Save expanded data as CSV

robust_calculation

Robust Variance Calculation

set_expansion_options

Set expansion options

TrialEmulation Data Class

save_to_datatable

Save expanded data as a data.table

print.TE_weight_summary

Print a weight summary object

show_weight_models

Show Weight Model Summaries

set_outcome_model

Specify the outcome model

set_censor_weight_model

Set censoring weight model

summary.TE_data_prep

Summary methods

select_data_cols

Select Data Columns

stats_glm_logit

Fit outcome models using stats::glm

te_model_fitter-class

Outcome Model Fitter Class

te_outcome_fitted-class

Fitted Outcome Model Object

te_outcome_data-class

TrialEmulation Outcome Data Class

Example of a fitted marginal structural model object

set_switch_weight_model

Set switching weight model

Set the trial data

Example of a prepared data object

te_outcome_model-class

Fitted Outcome Model Object

te_datastore-class

te_datastore_csv-class

te_datastore_csv, functions and methods

te_datastore_duckdb-class

te_datastore_duckdb, functions and methods

Fit the marginal structural model for the sequence of emulated trials

weight_model_data_indices

Data used in weight model fitting

te_parsnip_model-class

Fit Models using parsnip

Create a sequence of emulated target trials object

trial_sequence-class

Trial Sequence class

vignette_switch_data

Example of expanded longitudinal data for sequential trial emulation

te_stats_glm_logit-class

Fit Models using logistic stats::glm

Example of longitudinal data for sequential trial emulation

Expand Function

calculate_weights

Calculate Inverse Probability of Censoring Weights

case_control_sampling_trials

Case-control sampling of expanded data for the sequence of emulated trials

data_preparation

Prepare data for the sequence of emulated target trials

fit_weights_model

Method for fitting weight models

Fit the marginal structural model for the sequence of emulated trials

A wrapper function to perform data preparation and model fitting in a sequence of emulated target trials

data_manipulation

Data Manipulation Function

expand_until_switch

Check Expand Flag After Treatment Switch

Check Data used for Prediction

TrialEmulation-package

TrialEmulation Package

Censoring Function in C++

Example of longitudinal data for sequential trial emulation containing censoring

calculate_predictions

Calculate and transform predictions

internal-methods

Internal Methods