CA : A Cancer Journal for Clinicians

Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) (Book)

Background: The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran Methods: The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr Artificial Neural Networks (ANN) and Auto-Regressive Integrated Mov-ing Average (ARIMA) models were applied for prediction The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins To compare models, dataset was sepa-rated into train and test sets Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria Results: Both algorithms forecasted an exponential increase in number of newly infected patients If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA While Model comparison confirmed that ARIMA prediction was more accurate than ANN Conclusion: COVID-19 is contagious disease, and has infected many people in Iran Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed © 2020, Iranian Journal of Public Health All rights reserved

Exponentially increasing trend of infected patients with covid-19 in iran: A comparison of neural network and arima forecasting models

Background: Breast cancer is the most common cancer after lung cancer and the second cause of death. In this study we compared Weibull and Lognormal Cure Models with Cox regression on the survival of breast cancer. Study design: A cohort study. Methods: The current study retrospective cohort study was conducted on 140 patients referred to Ali Ibn Abitaleb Hospital, Rafsanjan southeastern Iran from 2001 to 2015 suffering from breast cancer. We determined and analyzed the effective survival causes by different models using STATA14. Results: According to AIC, log-normal model was more consistent than Weibull. In the multivariable Lognormal model, the effective factors like smoking, second -hand smoking, drinking herbal tea and the last breast-feeding period were included. In addition, using Cox regression factors of significant were the disease grade, size of tumor and its metastasis (p-value<0.05). As Rafsanjan is surrounded by pistachio orchards and pesticides applied by farmers, people of this city are exposed to agricultural pesticides and its harmful consequences. The effect of the pesticide on breast cancer was studied and the results showed that the effect of pesticides on breast cancer was not in agreement with the models used in this study . Conclusions: Based on different methods for survival analysis, researchers can decide how they can reach a better conclusion. This comparison indicates the result of semi-parametric Cox method is closer to clinical experiences evidences.

Comparison of Weibull and Lognormal Cure Models with Cox in the Survival Analysis Of Breast Cancer Patients in Rafsanjan.

Introduction: Survival modeling is a very important tool to detect risk factors and provide a basis for health care planning. However, cancer data may have properties leading to distorted results with routine methods. Therefore, this study aimed to cover specific factors (competing risk, cure fraction and heterogeneity) with a real dataset of Iranian breast cancer patients using a competing risk-cure-frailty model. Materials and methods: For this historical cohort study, information for 550 Iranian breast cancer patients who underwent surgery for tumor removal from 2001 to 2007 and were followed up to March 2017, was analyzed using R 3.2 software. Results: In contrast to T-stage and N-stage, hormone receptor status did not have any significant effect on the cure fraction (long-term disease-free survival). However, T-stage, N-stage and hormone receptor status all had a significant effect on short-term disease-free survival so that the hazard of loco-regional relapse or distant metastasis in cases positive for a hormone receptor was only 0.3 times that for their negative hormone receptor counterparts. The likelihood of locoregional relapse in the first quartile of follow up was nearly twice that of other quartiles. The least cumulative incidence of time to locoregional relapse was for cases with a positive hormone receptor, low N stage and low T stage. The effect of frailty term was significant in this study and a model with frailty appeared more appropriate than a model without, based on the Akaike information criterion (AIC); values for the frailty model and one without the frailty parameter were 1370.39 and 1381.46, respectively. Conclusions: The data from this study indicate ae necessity to consider competing risk, cure fraction and heterogeneity in survival modeling. The competing risk-cure-frailty model can cover complex situations with survival data.

http://journal.waocp.org/article_51129_404f3eae141a6b1cb51eac6bdcc411fa.pdf

Long-Term Disease-Free Survival of Non-Metastatic Breast Cancer Patients in Iran: A Survival Model with Competing Risks Taking Cure Fraction and Frailty into Account

Background. Breast cancer which is the most common cause of women cancer death has an increasing incidence and mortality rates in Iran. A proper modeling would correctly detect the factors' effect on breast cancer, which may be the basis of health care planning. Therefore, this study aimed to practically develop two recently introduced statistical models in order to compare them as the survival prediction tools for breast cancer patients. Materials and Methods. For this retrospective cohort study, the 18-year follow-up information of 539 breast cancer patients was analyzed by "Parametric Mixture Cure Model" and "Model-Based Recursive Partitioning." Furthermore, a simulation study was carried out to compare the performance of mentioned models for different situations. Results. "Model-Based Recursive Partitioning" was able to present a better description of dataset and provided a fine separation of individuals with different risk levels. Additionally the results of simulation study confirmed the superiority of this recursive partitioning for nonlinear model structures. Conclusion. "Model-Based Recursive Partitioning" seems to be a potential instrument for processing complex mixture cure models. Therefore, applying this model is recommended for long-term survival patients.

/pdf/a-comparison-between-cure-model-and-recursive-partitioning-a-31l4d4xjc2.pdf

A Comparison between Cure Model and Recursive Partitioning: A Retrospective Cohort Study of Iranian Female with Breast Cancer

BACKGROUND : Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients’ risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare ‘Model-Based Recursive Partitioning’ and ‘Random Survival Forest’; whether the partitioning, as the more interpretable learning technique, could be a suitable successor for forests. PATIENTS & METHODS : The applied dataset for this retrospective cohort study includes the information of 539 Iranian females with breast cancer. To model the patients’ survival, various learning algorithms were fitted and their accuracy measures were statistically compared by means of several precision criteria. RESULTS : This study verified the stability of ‘Model-based Recursive Partitioning’, further to ‘Random Survival Forest’ deficiency to present a unique pervasive model. Moreover, except ‘Log-Logistic-Based Recursive Partitioning’, none of the methods significantly outperformed ‘Exponential- Based Recursive Partitioning’. CONCLUSIONS: Briefly, it was concluded that the loss of interpretability due to the use of over complex models, may not always counterbalanced by the amount of prediction improvements.

/pdf/a-comparative-study-for-modelling-the-survival-of-breast-4nz4fupf91.pdf

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Background: Precise diagnosis of disease risk factors via efficient statistical models is the primary step for reducing the heavy costs of breast cancer, as one of the most highly prevalent cancer throughout the world. Therefore, the aim of this study was to present a recently introduced statistical model in order to assess its proficiency for model fitting. Methods: The information of 1465 eligible Iranian women with breast cancer was used for this retrospective cohort study. The statistical performances of exponential, Weibull, Log-logistic and Lognormal, as the most proper parametric survival models, were evaluated and compared with 'Model-based Recursive Partitioning' in order to survey their capability of more relevant risk factor detection. Results: 'Model-based Recursive Partitioning' recognized the largest number of significant affective risk factors, whereas, all four parametric models agreed and unable to detect the effectiveness of 'Progesterone Receptor' as an indicator; 'Log-Normal-based Recursive Partitioning' could provide the paramount fit. Conclusion: The superiority of 'Model-based Recursive Partitioning' was ascertained; not only by its excellent fitness but also by its susceptibility for classification of individuals to homogeneous severity levels and its impressive visual intuition potentiality.

/pdf/model-based-recursive-partitioning-for-survival-of-iranian-1xvoqc8ato.pdf

Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models.

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A Comparison between Cure Model and Recursive Partitioning: A Retrospective Cohort Study of Iranian Female with Breast Cancer

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models.