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- W4365138431 abstract "Abstract Objective: The aim of this study is to investigate the factors that influencing the number of axillary lymph nodes in women diagnosed with early breast cancer by choosing a strong model to evaluate the excess of zeros and outliers usually present in these data. Methods: The study based on a retrospective analysis of hospital records of 669 breast cancer patients in Iran. Zero-inflated, robust zero-inflated and Bayesian modelling techniques were used to assess the association between factors studied and the number of involved lymph nodes in breast cancer patients. Count data models, including zero-inflated models (zero-inflated Poisson and zero-inflated negative binomial), robust zero-inflated models (robust zero-inflated Poisson and robust zero-inflated negative binomial) and Bayesian models (Bayesian zero-inflated Poisson and Bayesian zero-inflated negative binomial) were applied. Performance evaluation of models was compared using AIC and BIC. Results : According to the AIC and BIC, the robust zero-inflated negative binomial model is the best model. Findings indicate that women who had a larger tumor had a greater number of axillary lymph nodes, hormone receptor status was associated with the number of lymph nodes, tumor grades II and III also contributed to a higher number of lymph nodes. Women who were older had a higher risk of having lymph nodes. Conclusions : Our analysis showed that the robust zero-inflated negative binomial is the best model for predicting and describing the number of nodes involved in primary breast cancer when overdispersion and outliers occurs." @default.
- W4365138431 created "2023-04-13" @default.
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- W4365138431 date "2023-04-12" @default.
- W4365138431 modified "2023-10-17" @default.
- W4365138431 title "Robust Inference for Zero-Inflated Models with Outliers Applied to the Number of Involved Lymph Nodes in Patients with Breast Cancer" @default.
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- W4365138431 doi "https://doi.org/10.21203/rs.3.rs-2692063/v1" @default.
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