Package: semicmprskcoxmsm 0.2.0

Yiran Zhang

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

Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) <arxiv:2204.10426>.

Authors:Yiran Zhang

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DESCRIPTION
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semicmprskcoxmsm/json (API)

# Install 'semicmprskcoxmsm' in R:

install.packages('semicmprskcoxmsm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 251 downloads 12 exports 42 dependencies

Last updated from:b2f2ea38dd. Checks:6 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	152
source / vignettes	OK	194
linux-release	OK	141
windows-devel	OK	113
windows-release	OK	113
wasm-release	OK	117

Exports:cif_est_usual doPS em_illness_death_phmm_weight get_hazard get_hazard_offset_weights initial_fit_em_weights initial_lambda_em OUT_em_weights plot.PS sim_cox_msm_semicmrsk usual_illness_death_weight var_em_illness_death_phmm

Dependencies:cli cpp11 data.table DBI deldir farver fastGHQuad gbm ggplot2 glue gtable interp isoband jpeg jsonlite labeling lattice latticeExtra lifecycle MASS Matrix MatrixModels minqa mitools numDeriv png R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang S7 scales survey survival twang vctrs viridisLite withr xgboost xtable

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

Package: semicmprskcoxmsm 0.2.0

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

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