Use Inverse Probability Weighting to Estimate Treatment Effect for Semi Competing Risks Data

Documentation for package ‘semicmprskcoxmsm’ version 0.1.0

cif_est_usual	Estimating Three Cumulative Incidence Functions Using the Usual Markov Model
doPS	Generate the Inverse Probability Treatment Weights
em_illness_death_phmm_weight	Using EM Type Algorithm for MSM Illness-death General Markov Model
get_hazard	Compute the (Cumulative) Baseline Hazard from Cox Model
get_hazard_offset_weights	Compute the (Cumulative) Baseline Hazard from Cox Model with Offsets
initial_fit_em_weights	Fit the MSM Cox Model with IP Weights
initial_lambda_em	Compute the Initial (Cumulative) Baseline Hazard From the MSM Illness-death Model
OUT_em_weights	Initial Value For Fitting the General Markov Model
plot.PS	Plotting Histogram of Propensity Score and Balancing Plot for Covariates in the Propensity Score Model
sim_cox_msm_semicmrsk	Simulating Semi-competing Risks with Right-censored Survival Data under Marginal Structural Illness-death Cox Model
usual_illness_death_weight	Fit MSM Illness-death Usual Markov Model For Semi-competing Risks Data
var_em_illness_death_phmm	Variance of parameters in MSM Illness-death General Markov Model