geecure: An R-package for marginal proportional hazards mixture cure models

Niu, Yi; Wang, Xiaoguang; Peng, Yingwei

doi:10.1016/j.cmpb.2018.04.017

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Combining the equality in (10) and the definition P (Y = 1 | z ) = π ( z ) , the cure proportion can also be extracted by using the type = ''probcure'' argument and specifying FUyear = 0 .…”

Section: Discussionmentioning

confidence: 99%

“…The miCoPTCM package enables fitting of semi-parametric non-mixture (or promotion time) cure models incorporating measurement errors in covariates. The GORCure package [9] enables fitting of mixture cure models based on interval-censored data, while the geecure package fits parametric and semi-parametric mixture cure models using a generalized estimation equation scheme that allows for modelling of clustered time-to-event data [10] . The penPHcure package implements a variable selection procedure for mixture cure models using a penalization scheme and allows for time-varying covariates [11] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fitting parametric cure models in R using the packages cuRe and rstpm2

Jensen¹,

Clements²,

Gjærde³

et al. 2022

Computer Methods and Programs in Biomedicine

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fitting parametric cure models in R using the packages cuRe and rstpm2

Jensen¹,

Clements²,

Gjærde³

et al. 2022

Computer Methods and Programs in Biomedicine

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“…According to the data from GEO and TCGA databases, univariate and multivariate Cox regression analysis was conducted to explore which was the most relevant clinical factor in the survival of LUAD patients through the “survival” package in R ( 23 , 24 ). Age, gender, stage, T, N, M classification, and ADCY9 expression were also contained in this model.…”

Section: Methodsmentioning

confidence: 99%

ADCY9 functions as a novel cancer suppressor gene in lung adenocarcinoma

Tang¹,

Wang²,

Zhang³

et al. 2023

J Thorac Dis

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Background: The process of lucubrating into lung adenocarcinoma (LUAD) is of profound clinical and practical significance in improving the prognosis of LUAD patients. Multiple biomarkers are reportedly involved in the proliferation or metastasis of adenocarcinoma. However, whether the ADCY9 gene influences the development of LUAD remains unknown. Therefore, we aimed to elucidate the relationship between the expression of ADCY9 and the proliferation and migration of LUAD.Methods: The ADCY9 gene was filtered via a survival analysis of LUAD acquired from the Gene Expression Omnibus (GEO). Then, we conducted a validation analysis and ADCY9-microRNA, microRNA-lncRNA, and ADCY9-lncRNA targeting relationship analysis through the data obtained from The Cancer Genome Atlas (TCGA) dataset. The survival curve, correlation, and prognostic analysis were implemented through bioinformatics methods. Both protein and mRNA expression levels of LUAD cell lines and LUAD patient samples (80 pairs) were detected using western blot assays and quantitative real-time polymerase chain reaction (qRT-PCR). An immunohistochemistry assay was performed to display the correlation between the expression level of the ADCY9 gene and prognosis in LUAD patients (2012-2013; n=115).Overexpression of cell lines SPCA1 and A549 were used for a series of cell function assays. Results:Compared with the expression level in adjacent normal tissues, ADCY9 expression was downregulated in LUAD tissues. Based on the result of the survival curve analysis, high expression of ADCY9 may lead to a better prognosis and may be seen as an independent predictor for LUAD patients. High expression of ADCY9-related microRNA hsa-miR-7-5p may lead to a worse prognosis, and high expression of hsa-miR-7-5p-related lncRNAs may lead to the opposite effects. Overexpression of ADCY9 restrained the proliferation, invasion, and migration abilities of SPCA1, A549 cells. Conclusions:Results indicate that the ADCY9 gene acts as a tumor suppressor to restrain the proliferation, migration, and invasion in LUAD and can lead to a better survival or prognosis in LUAD patients.

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“…where ( ) The analysis was implemented using R version 4.1.3 [22]. The semi-parametric Cox marginal and frailty models were fitted using geecure() function in geecure package [23].…”

Section: Model Evaluationmentioning

confidence: 99%

Modelling the Survival of Western Honey Bee <i>Apis mellifera</i> and the African Stingless Bee <i>Meliponula ferruginea</i> Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Isiaho,

Salifu,

Mwalili

et al. 2024

JDAIP

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Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses ( 1 0.

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geecure: An R-package for marginal proportional hazards mixture cure models

Cited by 6 publications

References 24 publications

Fitting parametric cure models in R using the packages cuRe and rstpm2

Fitting parametric cure models in R using the packages cuRe and rstpm2

ADCY9 functions as a novel cancer suppressor gene in lung adenocarcinoma

Modelling the Survival of Western Honey Bee <i>Apis mellifera</i> and the African Stingless Bee <i>Meliponula ferruginea</i> Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

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geecure: An R-package for marginal proportional hazards mixture cure models

Cited by 6 publications

References 24 publications

Fitting parametric cure models in R using the packages cuRe and rstpm2

Fitting parametric cure models in R using the packages cuRe and rstpm2

ADCY9 functions as a novel cancer suppressor gene in lung adenocarcinoma

Modelling the Survival of Western Honey Bee &lt;i&gt;Apis mellifera&lt;/i&gt; and the African Stingless Bee &lt;i&gt;Meliponula ferruginea&lt;/i&gt; Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

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Modelling the Survival of Western Honey Bee <i>Apis mellifera</i> and the African Stingless Bee <i>Meliponula ferruginea</i> Using Semiparametric Marginal Proportional Hazards Mixture Cure Model