Parametric G-Formula
Get Plotting Information
Estimation of Survival Outcome, Continuous End-of-Follow-Up Outcome, o...
Estimation of Binary End-of-Follow-Up Outcome Under the Parametric G-F...
Estimation of Continuous End-of-Follow-Up Outcome Under the Parametric...
Estimation of Survival Outcome Under the Parametric G-Formula
Format Simulated Dataset for Hazard Ratio Calculation
Execute Intervention
History functions
Generates Functions of History of Existing Covariates
Natural Course Intervention
Calculate Observed Covariate Means and Risk
Plot method for objects of class "gformula_binary_eof"
Plot method for objects of class "gformula_continuous_eof"
Plot method for objects of class "gformula_survival"
Fit Covariate Models
Fit Competing Event Model
Print and summary methods for "gformula" objects
Calculate RMSE for Covariate, Outcome, and Competing Risk Models
Simple Restriction
Simulate Counterfactual Outcomes Under Intervention
Static Intervention
Threshold Intervention
Variance-covariance method for objects of class "gformula"
Create Visit Sum Covariate
Bootstrap Observed Data and Simulate Under All Interventions
Carry Forward
Coefficient method for objects of class "gformula"
General Error Catching
Fit Bounded Normal Model on Covariate
Fit GLM on Covariate
Fit Multinomial Model on Covariate
Fit Truncated Normal Model on Covariate
Fit Zero-Inflated Normal Model on Covariate
Get Covariate Plots
Get Risk and Survival Plots
Fit Outcome Model
Simulate Binary Values
Simulate Normal Values
Simulate Truncated Normal Values
Implements the parametric g-formula algorithm of Robins (1986) <doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate the causal effects of hypothetical time-varying treatment interventions on the mean or risk of an outcome from longitudinal data with time-varying confounding. This package allows: 1) binary or continuous/multi-level time-varying treatments; 2) different types of outcomes (survival or continuous/binary end of follow-up); 3) data with competing events or truncation by death and loss to follow-up and other types of censoring events; 4) different options for handling competing events in the case of survival outcomes; 5) a random measurement/visit process; 6) joint interventions on multiple treatments; and 7) general incorporation of a priori knowledge of the data structure.
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