Causal Analysis of Observational Time-to-Event Data


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Documentation for package ‘TrialEmulation’ version 0.0.4.0

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calculate_weights Calculate Inverse Probability of Censoring Weights
calculate_weights-method Calculate Inverse Probability of Censoring Weights
case_control_sampling_trials Case-control sampling of expanded data for the sequence of emulated trials
data_censored Example of longitudinal data for sequential trial emulation containing censoring
data_preparation Prepare data for the sequence of emulated target trials
expand_trials Expand trials
fit_msm Fit the marginal structural model for the sequence of emulated trials
fit_msm-method Fit the marginal structural model for the sequence of emulated trials
fit_weights_model Method for fitting weight models
initiators A wrapper function to perform data preparation and model fitting in a sequence of emulated target trials
ipw_data IPW Data Accessor and Setter
ipw_data-method IPW Data Accessor and Setter
ipw_data<- IPW Data Accessor and Setter
ipw_data<--method IPW Data Accessor and Setter
load_expanded_data Method to read, subset and sample expanded data
load_expanded_data-method Method to read, subset and sample expanded data
outcome_data Outcome Data Accessor and Setter
outcome_data-method Outcome Data Accessor and Setter
outcome_data<- Outcome Data Accessor and Setter
outcome_data<--method Outcome Data Accessor and Setter
parsnip_model Fit outcome models using 'parsnip' models
predict Predict marginal cumulative incidences with confidence intervals for a target trial population
predict-method Predict marginal cumulative incidences with confidence intervals for a target trial population
predict.TE_msm Predict marginal cumulative incidences with confidence intervals for a target trial population
predict_marginal Predict marginal cumulative incidences with confidence intervals for a target trial population
print.TE_weight_summary Print a weight summary object
read_expanded_data Method to read expanded data
read_expanded_data-method Method to read expanded data
sample_expanded_data Internal method to sample expanded data
sample_expanded_data-method Internal method to sample expanded data
save_expanded_data Method to save expanded data
save_expanded_data-method Method to save expanded data
save_to_csv Save expanded data as CSV
save_to_datatable Save expanded data as a 'data.table'
save_to_duckdb Save expanded data to 'DuckDB'
set_censor_weight_model Set censoring weight model
set_censor_weight_model-method Set censoring weight model
set_data Set the trial data
set_data-method Set the trial data
set_expansion_options Set expansion options
set_expansion_options-method Set expansion options
set_outcome_model Specify the outcome model
set_outcome_model-method Specify the outcome model
set_switch_weight_model Set switching weight model
set_switch_weight_model-method Set switching weight model
show_weight_models Show Weight Model Summaries
stats_glm_logit Fit outcome models using 'stats::glm'
summary.TE_data_prep Summary methods
summary.TE_data_prep_dt Summary methods
summary.TE_data_prep_sep Summary methods
summary.TE_msm Summary methods
summary.TE_robust Summary methods
te_data-class TrialEmulation Data Class
te_datastore-class te_datastore
te_data_ex Example of a prepared data object
te_model_ex Example of a fitted marginal structural model object
te_model_fitter-class Outcome Model Fitter Class
te_outcome_data-class TrialEmulation Outcome Data Class
te_outcome_fitted-class Fitted Outcome Model Object
te_outcome_model-class Fitted Outcome Model Object
trial_example Example of longitudinal data for sequential trial emulation
trial_msm Fit the marginal structural model for the sequence of emulated trials
trial_sequence Create a sequence of emulated target trials object
trial_sequence-class Trial Sequence class
trial_sequence_AT-class Trial Sequence class
trial_sequence_ITT-class Trial Sequence class
trial_sequence_PP-class Trial Sequence class
vignette_switch_data Example of expanded longitudinal data for sequential trial emulation
weight_model_data_indices Data used in weight model fitting