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

Mozhgan Safe, Javad Faradmal, Jalal Poorolajal, Hossein Mahjub

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Article in Iranian journal of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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1 citing paper in PubMed.

Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models.Iranian journal of public health · 2020
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5 · Who and what money

Authors and funding

4 authors.

Mozhgan SafeDept. of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Javad FaradmalDept. of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Jalal PoorolajalDept. of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Hossein MahjubDept. of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecise 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.

methodsThe 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.

conclusionThe 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.

Indexed as

Breast cancerParametric survival modelRecursive partitioning

Identifiers

PMID28451527

PMCPMC5401933

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