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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.

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Version

Install

install.packages('TrialEmulation')

Monthly Downloads

339

Version

0.0.4.2

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

February 21st, 2025

Functions in TrialEmulation (0.0.4.2)

internal-methods

Internal Methods

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

save_expanded_data

Method to save expanded data

save_to_datatable

Save expanded data as a data.table

Save expanded data to DuckDB

Fit outcome models using parsnip models

predict_marginal

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

TrialEmulation Data Class

select_data_cols

Select Data Columns

set_outcome_model

Specify the outcome model

te_outcome_fitted-class

Fitted Outcome Model Object

sample_expanded_data

Internal method to sample expanded data

robust_calculation

Robust Variance Calculation

summary.TE_data_prep

Summary methods

set_censor_weight_model

Set censoring weight model

te_outcome_model-class

Fitted Outcome Model Object

set_switch_weight_model

Set switching weight model

Save expanded data as CSV

te_outcome_data-class

TrialEmulation Outcome Data Class

Set the trial data

Example of a prepared data object

te_model_fitter-class

Outcome Model Fitter Class

Create a sequence of emulated target trials object

te_datastore-class

set_expansion_options

Set expansion options

vignette_switch_data

Example of expanded longitudinal data for sequential trial emulation

Example of a fitted marginal structural model object

te_parsnip_model-class

Fit Models using parsnip

te_stats_glm_logit-class

Fit Models using logistic stats::glm

load_expanded_data

Method to read, subset and sample expanded data

weight_model_data_indices

Data used in weight model fitting

print.TE_weight_summary

Print a weight summary object

Outcome Data Accessor and Setter

trial_sequence-class

Trial Sequence class

stats_glm_logit

Fit outcome models using stats::glm

show_weight_models

Show Weight Model Summaries

te_datastore_csv-class

te_datastore_csv, functions and methods

read_expanded_data

Method to read expanded data

Fit the marginal structural model for the sequence of emulated trials

te_datastore_duckdb-class

te_datastore_duckdb, functions and methods

Example of longitudinal data for sequential trial emulation

Censoring Function in C++

data_preparation

Prepare data for the sequence of emulated target trials

calculate_weights

Calculate Inverse Probability of Censoring Weights

case_control_sampling_trials

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

Example of longitudinal data for sequential trial emulation containing censoring

TrialEmulation-package

TrialEmulation Package

Check Data used for Prediction

data_manipulation

Data Manipulation Function

Expand Function

calculate_predictions

Calculate and transform predictions

fit_outcome_model

Method for fitting outcome models

IPW Data Accessor and Setter

expand_until_switch

Check Expand Flag After Treatment Switch

fit_weights_model

Method for fitting weight models