Survival Analysis for a Breast Cancer Data Set

Li, Hong

doi:10.4236/abcr.2017.61001

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Cox proportional hazards models with machine learning approaches for time-to-event prediction in breast cancer, 7 heart disease, 8 and tuberculosis. 9 However, few to no similar research using survival analysis has been conducted on AD clinical data.…”

Section: Introductionmentioning

confidence: 99%

“…From preventive medicine clinical practice, the ability to accurately predict the next stage of progression of AD for a patient over a given period could help physicians make more informed clinical decisions on treatment strategies 6 . Several studies have extended traditional Cox proportional hazards models with machine learning approaches for time‐to‐event prediction in breast cancer, 7 heart disease, 8 and tuberculosis 9 . However, few to no similar research using survival analysis has been conducted on AD clinical data.…”

Section: Introductionmentioning

confidence: 99%

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Time‐to‐event prediction using survival analysis methods for Alzheimer's disease progression

Sharma

Anand

Badr

et al. 2021

A&D Transl Res & Clin Interv

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Introduction: Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of ADis not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time. Methodologies:We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017. Results:The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval. Conclusions:The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time‐to‐event prediction using survival analysis methods for Alzheimer's disease progression

Sharma

Anand

Badr

et al. 2021

A&D Transl Res & Clin Interv

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Evaluation of Different Survival Analysis Models for Nki Breast Cancer Data

Alejandro Ganas,

Berces Aranas

2023

JHTD

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The goal of this study is to evaluate different Survival Analysis Models in terms of their predictive capabilities, accuracy in determining significant covariates within the data, as well as their respective results compared across standard indices. Highest Concordance Index and Lowest Akaike Information Criterion (AIC) are used as the basis of selecting the ideal Survival Analysis model as a template for the construction of the Survival Prediction model for NKI Breast Cancer Data. 6 Survival Analysis Models were used in this study. For the semi-parametric survival models, Classical Cox, Cox-Lasso, and Cox-Ridge Regressions. For the parametric models, 3 Accelerated Failure Time (AFT) models were implemented. These are: Weibull AFT, Log-logistic AFT, and Log-Normal AFT models. Right-censoring was performed in the data since it has been assumed that there are subjects which were not called back anymore for the entire, 18-year clinical trial where the data was taken from. A proportional hazards test was then performed to find out if the covariates in the data are fit to be modeled using Cox Regression and its derivatives. A test for the distribution on the time of event was also done to find whether it follows a specific distribution or not. This was done to verify the usability of the parametric survival analysis models on the data. It has been found out that in terms of Concordance Index and AIC, the Cox-Ridge Regression model outperforms its 2 other semi-parametric counterparts, having the least AIC of 752.6703 and Highest Concordance Index of 0.7709. As for the other 3 parametric models, Log-Normal AFT outperformed the Weibull AFT and Log-Logistic AFT models by a Concordance Index of 0.780 with a corresponding AIC of 608.822. This result also suggests that the time of event of the subjects is best fitted by Log-Normal Distribution. By comparing the 2, best-performing models, it has been reported that Log-Normal AFT outperforms Cox-Ridge Regressions, therefore suggesting to use this Parametric Survival Analysis Model as the basis for a Survival Prediction model suited for NKI Breast Cancer data.

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Survival Analysis for a Breast Cancer Data Set

Cited by 2 publications

References 11 publications

Time‐to‐event prediction using survival analysis methods for Alzheimer's disease progression

Time‐to‐event prediction using survival analysis methods for Alzheimer's disease progression

Evaluation of Different Survival Analysis Models for Nki Breast Cancer Data

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