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Journals / Alphanumeric Journal / 2015 / Cilt 3, Sayı 2, 2015

Outliers in Survival Analysis

Yaşam Çözümlemesinde Aykırı Değerler

Durdu KARASOY
Nuray TUNCER
Pages
139–152
Publication Date
2015-12-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

After omitting these outliers, model is set up again and results were found better.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Survival analysis is a collection of statistical methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest.
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Key Findings & Data:Outliers in survival anaysis calculated differently from classical regression analysis.
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Conclusion & Impact:After omitting these outliers, model is set up again and results were found better.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Survival analysis is a collection of statistical methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest. Outliers in survival anaysis calculated differently from classical regression analysis. Outlier detection methods in survival analysis are commonly carried out based on residuals and residual analysis. In survival analysis, there are different types of residuals that are Cox-Snell, Martingale, Schoenfeld, Deviance, Log-odds and Normal deviance residuals. There are methods which are DFBETA, LMAX and Likelihood Displacement values for detecting influential observations. The residuals are analyzed during the study which is applied on a stomach cancer data set and the outliers are detected. After omitting these outliers, model is set up again and results were found better.

Özet

Yaşam çözümlemesi, tanımlanan herhangi bir olayın ortaya çıkmasına kadar geçen sürenin incelenmesinde kullanılan istatistiksel yöntemler bütünüdür. Yaşam çözümlemesinde aykırı değerler klasik regresyonda kullanılan yöntemlerden farklı yöntemler kullanılarak hesaplanmaktadır. Yaşam çözümlemesinde aykırı değer belirleme yöntemleri artıklara ve artıkların analizine dayanmaktadır. Yaşam çözümlemesinde kullanılan başlıca artık türleri Cox-Snell, Martingale, Schoenfeld, Sapma, Log-odds ve Normal sapma artıklarıdır. Etkili gözlemleri belirlemek için kullanılan yöntemler ise DFBETA, LMAX ve Olabilirlik Değişim değerleridir. İncelenen artık türleri mide kanseri ile ilgili verilere uygulanmış ve aykırı değerler belirlenmiştir. Belirlenen aykırı değerler çıkarılarak model yeniden kurulmuş ve aykırı değerler çıkarıldığında sonuçların daha iyi olduğu görülmüştür.

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Cite This Article

KARASOY, D., TUNCER, N. (2015). Outliers in Survival Analysis. Alphanumeric Journal, Cilt 3, Sayı 2, 2015, 139–152. https://ajindex.com/articles/63621

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