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semicmprskcoxmsm
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install.packages("semicmprskcoxmsm")
Install from Github:
library("remotes")
install_github("cran/semicmprskcoxmsm") Install by package version:
library("remotes")
install_version("semicmprskcoxmsm", "0.2.0") Attach the package and use:
library("semicmprskcoxmsm")
Maintained by
Yiran Zhang
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[Scholar Profile | Author Map]
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First Published: 2022-03-30
Latest Update: 2022-04-29
Description:
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) .
How to cite:
Yiran Zhang (2022). semicmprskcoxmsm: Use Inverse Probability Weighting to Estimate Treatment Effect for Semi Competing Risks Data. R package version 0.2.0, https://cran.r-project.org/web/packages/semicmprskcoxmsm. Accessed 23 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:02), 0.1.0 (2022-03-30 09:40)
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Complete documentation for semicmprskcoxmsm
Functions, R codes and Examples using
the semicmprskcoxmsm R package
Some associated functions: OUT_em_weights . bayesian_boot_irrd . cif_est_usual . conditional_cif_b . doPS . em_illness_death_phmm_weight . get_hazard . get_hazard_offset_weights . individual_RR_RD . initial_fit_em_weights . initial_lambda_em . plot.PS . sim_cox_msm_semicmrsk . usual_illness_death_weight . var_em_illness_death_phmm .
Some associated R codes: IRR_IRD.R . general_markov_model.R . sim_cox_msm_semicmprsk.R . usual_markov_model.R . weight.R . Full semicmprskcoxmsm package functions and examples
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