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

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

## Documentation for package ‘semicmprskcoxmsm’ version 0.2.0

- DESCRIPTION file

## Help Pages

bayesian_boot_irrd | Obtaining Bayesian Bootstrap Sample for Individual Risk Difference and Risk Ratio.
cif_est_usual | Estimating Three Cumulative Incidence Functions Using the Usual Markov Model
conditional_cif_b | Estimating Three Conditional Cumulative Incidence Functions Using the General Markov Model Conditional on Random Effect
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
individual_RR_RD | Estimating Three Individual Risk Difference and Risk Ratio Using the General Markov Model Conditional on Predicted Random Effect
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