G-Computation to Estimate Interpretable Epidemiological Effects
Estimate difference and ratio effects with 95% confidence intervals.
Take predicted dataframe and calculate the outcome (risk difference/ra...
Using glm results, predict outcomes for each individual at each leve...
Plot estimates of difference and ratio effects obtained in the bootstr...
Perform g-computation to estimate difference and ratio effects of an e...
Print estimates of difference and ratio effects obtained in the bootst...
riskCommunicator: Obtaining interpretable epidemiological effect estim...
Print a summary of a gComp class object.
Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.