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cure.bib
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@article{cai_applying_2022,
year = {2022},
title = {Applying mixture cure survival modeling to medication persistence analysis},
doi = {10.1002/pds.5441},
abstract = {Purpose Standard survival models are often used in a medication persistence analysis. These methods implicitly assume that all patients will experience the event (medication discontinuation), which may bias the estimation of persistence if long-term medication persistent patients rate is expected in the population. We aimed to introduce a mixture cure model in the medication persistence analysis to describe the characteristics of long-term and short-term persistent patients, and demonstrate its application using a real-world data analysis. Methods A cohort of new users of statins was used to demonstrate the differences between the standard survival model and the mixture cure model in the medication persistence analysis. The mixture cure model estimated effects of variables, reported as odds ratios (OR) associated with likelihood of being long-term persistent and effects of variables, reported as hazard ratios (HR) associated with time to medication discontinuation among short-term persistent patients. Results Long-term persistent rate was estimated as 17\% for statin users aged between 45 and 55 versus 10\% for age less than 45 versus 4\% for age greater than 55 via the mixture cure model. The HR of covariates estimated by the standard survival model (HR = 1.41, 95\% CI = [1.35, 1.48]) were higher than those estimated by the mixture cure model (HR = 1.32, 95\% CI = [1.25, 1.39]) when comparing patients with age greater than 55 to those between 45 and 55. Conclusions Compared with standard survival modeling, a mixture cure model can improve the estimation of medication persistence when long-term persistent patients are expected in the population.},
language = {en},
urldate = {2022-05-02},
journal = {Pharmacoepidemiology and Drug Safety},
author = {Cai, Chao and Love, Bryan L. and Yunusa, Ismaeel and Reeder, Claiborne E.},
keywords = {pharmacoepidemiology, medication persistence, survival analysis, long-term persistent fraction, mixture cure model},
file = {Full Text PDF:C\:\\Users\\z9292540\\Zotero\\storage\\2CN43XH4\\Cai et al. - Applying mixture cure survival modeling to medicat.pdf:application/pdf;Snapshot:C\:\\Users\\z9292540\\Zotero\\storage\\J8QS539D\\pds.html:text/html},
}
@article{cai_smcure_2012,
title = {smcure: {An} {R}-package for estimating semiparametric mixture cure models},
volume = {108},
shorttitle = {smcure},
doi = {10.1016/j.cmpb.2012.08.013},
abstract = {The mixture cure model is a special type of survival models and it assumes that the studied population is a mixture of susceptible individuals who may experience the event of interest, and cure/non-susceptible individuals who will never experience the event. For such data, standard survival models are usually not appropriate because they do not account for the possibility of cure. This paper presents an R package smcure to fit the semiparametric proportional hazards mixture cure model and the accelerated failure time mixture cure model.},
language = {en},
number = {3},
urldate = {2022-05-18},
journal = {Computer Methods and Programs in Biomedicine},
author = {Cai, Chao and Zou, Yubo and Peng, Yingwei and Zhang, Jiajia},
month = dec,
year = {2012},
keywords = {Accelerated failure time model, EM algorithm, Proportional hazards model, R package, Semiparametric mixture cure model},
pages = {1255--1260},
}
@article{othus_cure_2012,
title = {Cure {Models} as a {Useful} {Statistical} {Tool} for {Analyzing} {Survival}},
volume = {18},
doi = {10.1158/1078-0432.CCR-11-2859},
abstract = {Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors. Clin Cancer Res; 18(14); 3731–6. ©2012 AACR.},
number = {14},
urldate = {2022-05-18},
journal = {Clinical Cancer Research},
author = {Othus, Megan and Barlogie, Bart and LeBlanc, Michael L. and Crowley, John J.},
month = jul,
year = {2012},
pages = {3731--3736},
}
@Manual{flexsurvcure_2020,
title = {flexsurvcure: Flexible Parametric Cure Models},
author = {Jordan Amdahl},
year = {2020},
note = {R package version 1.2.0},
url = {https://CRAN.R-project.org/package=flexsurvcure},
}