Comparison of exact, efron and breslow parameter approach method on hazard ratio and stratified cox regression model

Fatekurohman, Mohamat; Nurmala, Nita; Anggraeni, Dian

doi:10.1088/1742-6596/1008/1/012007

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Untuk menangani kasus demikian maka digunakan metode estimasi parameter yang sesuai. Tiga metode estimasi yang sering digunakan dalam masalah estimasi kasus demikian, yaitu metode Breslow, Efron dan exact (Amir et al, 2020;Borucka, 2014;Fatekurohman et al, 2018;Gisondi et al, 2021). Beberapa penelitian yang terkait dengan analisis survival telah dilakukan dan efesien digunakan (Atlam et al, 2021;Jung et al, 2018;Lánczky & Győrffy, 2021;Schober & Vetter, 2018;Xie et al, 2020).…”

Section: Pendahuluanunclassified

Model Survival Semiparametrik Dan Parametrik Untuk Data Demam Berdarah Dengue (Dbd) Di Rsud Kabupaten Ciamis Tahun 2020

Jajang

Ashfahani

Sb.

et al. 2022

j. sains. teknologi.

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The research is about the best survival analysis model used for patients with Dengue Hemorrhagic Fever (DHF) at Ciamis Hospital in 2020. The purpose of this research is to identify the factors that influence the rate of cure for DHF at Ciamis Hospital in 2020. The method used is survival analysis. Furthermore, the parametric model used Weibull PH, whereas the semiparametric model used Cox PH regression. The result of the analysis showed that the best model obtained was the semiparametric Cox PH regression model, with significant factors including age, Pack Cell Volume (PCV), hemoglobin, White Blood Cell (WBC), and body temperature.

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Section: Pendahuluanunclassified

Model Survival Semiparametrik Dan Parametrik Untuk Data Demam Berdarah Dengue (Dbd) Di Rsud Kabupaten Ciamis Tahun 2020

Jajang

Ashfahani

Sb.

et al. 2022

j. sains. teknologi.

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“…These models can be used to identify high-risk groups of MetS, persuade them to change their unhealthy lifestyles, reduce and slow down the occurrence and development of the disease. Among the m[ 19 – 21 ], Logistic regression and Cox regression, as traditional statistical modeling methods, are widely used and have strong explanatory power. However, Cox regression is often used for survival analysis data, which requires two dependent variables at the same time and has relatively strict requirements on data.…”

Section: Introductionmentioning

confidence: 99%

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Wang

et al. 2020

BMC Public Health

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Background The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people’s health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome. Methods Design existing circumstances research. A total of 1468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models. Results The results showed that the accuracy of the three models was 82.49,95.98 and 92.03%, the sensitivity was 87.94,95.52 and 90.59%, the specificity was 74.54, 96.65 and 94.14%, the F1 score was 0.86,0.97 and 0.93, and the area under ROC curve was 0.88,0.96 and 0.92, respectively. The Brier score of the three models was 0.15, 0.08 and 0.12, Observed-expected ratio was 0.83, 0.97 and 1.13, and the Integrated Calibration Index was 0.075,0.073 and 0.074, respectively, and explained how the random forest model was used for individual disease risk score. Conclusions The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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“…Among them [19][20][21], Logistic regression and Cox regression, as traditional statistical modeling methods, are widely used and have strong explanatory power. However, Cox regression is often used for survival analysis data, which requires two dependent variables at the same time and has relatively strict requirements on data.…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

Wang¹,

Li²,

Li³

et al. 2020

Preprint

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Background. The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health.In this paper, three kinds of risk prediction models applicable to the metabolic syndrome of oil workers were established, and the optimal models were found through comparison. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Methods. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results. The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, and the area under the ROC curve was 0.894, 0.987 and 0.935, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, and the area under the ROC curve was 0.797, 0.861 and 0.855, respectively.Conclusions. The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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Comparison of exact, efron and breslow parameter approach method on hazard ratio and stratified cox regression model

Cited by 4 publications

References 3 publications

Model Survival Semiparametrik Dan Parametrik Untuk Data Demam Berdarah Dengue (Dbd) Di Rsud Kabupaten Ciamis Tahun 2020

Model Survival Semiparametrik Dan Parametrik Untuk Data Demam Berdarah Dengue (Dbd) Di Rsud Kabupaten Ciamis Tahun 2020

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

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