ROC Curves in Survival Analysis and an Application

Duru Karasoy

Makale detayları

ROC Curves in Survival Analysis and an Application

Yazarlar:

Duru Karasoy

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Biyoistatistik | Uygulamalı İstatistik

Dergi:

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.28 Issue 3, pp.490-503

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001332874

pISSN:

1308-6529

Anahtar kelimeler:

Özet:

 The survival data includes survival times and status indicating whether the event occurred. Receiver Operating Characteristic ROC curves used in the analysis of survival data determine how well they discriminate between those who experience the event and those who do not, allowing to choose the correct cut-off value. Since time is involved in survival analysis and the status may change, classical ROC curves do not give accurate results. For this reason, new methods have been developed by researching time-dependent ROC curves and ROC curve estimates denoted by ROCt have been proposed. Among the ROC curve estimator methods used for survival analysis in this paper, cumulative sensitivity and dynamic specificity CD, incident sensitivity and dynamic specificity ID, incident sensitivity and static selectivity IS and finally the naive estimator is introduced. To demonstrate the applicability of these estimators, an application was made on real data, cervical cancer. For the data, CD1 and CD2 gave similar results. Additionally, CD5 and CD6 also gave similar results. While ID1 had the lowest classification performance, CD4 showed good classification performance from the 10th month.

 The survival data includes survival times and status indicating whether the event occurred. Receiver Operating Characteristic ROC curves used in the analysis of survival data determine how well they discriminate between those who experience the event and those who do not, allowing to choose the correct cut-off value. Since time is involved in survival analysis and the status may change, classical ROC curves do not give accurate results. For this reason, new methods have been developed by researching time-dependent ROC curves and ROC curve estimates denoted by ROCt have been proposed. Among the ROC curve estimator methods used for survival analysis in this paper, cumulative sensitivity and dynamic specificity CD, incident sensitivity and dynamic specificity ID, incident sensitivity and static selectivity IS and finally the naive estimator is introduced. To demonstrate the applicability of these estimators, an application was made on real data, cervical cancer. For the data, CD1 and CD2 gave similar results. Additionally, CD5 and CD6 also gave similar results. While ID1 had the lowest classification performance, CD4 showed good classification performance from the 10th month.

 The survival data includes survival times and status indicating whether the event occurred. Receiver Operating Characteristic ROC curves used in the analysis of survival data determine how well they discriminate between those who experience the event and those who do not, allowing to choose the correct cut-off value. Since time is involved in survival analysis and the status may change, classical ROC curves do not give accurate results. For this reason, new methods have been developed by researching time-dependent ROC curves and ROC curve estimates denoted by ROCt have been proposed. Among the ROC curve estimator methods used for survival analysis in this paper, cumulative sensitivity and dynamic specificity CD, incident sensitivity and dynamic specificity ID, incident sensitivity and static selectivity IS and finally the naive estimator is introduced. To demonstrate the applicability of these estimators, an application was made on real data, cervical cancer. For the data, CD1 and CD2 gave similar results. Additionally, CD5 and CD6 also gave similar results. While ID1 had the lowest classification performance, CD4 showed good classification performance from the 10th month.

 Yaşam verisi, yaşam sürelerini ve olayın gerçekleşip gerçekleşmediğini gösteren durumu içerir. Yaşam verilerinin analizinde kullanılan alıcı işlem karakteristiği ROC eğrileri olayı yaşayanlar ile yaşamayanlar arasında ne kadar iyi ayrım yaptığını belirleyip doğru eşik değerini seçmeyi sağlar. Yaşam çözümlemesinde süre söz konusu olduğu için durum değişebileceğinden klasik ROC eğrileri doğru sonuçlar vermez. Bu nedenle zamana bağlı ROC eğrileri üzerinde araştırmalar yapılarak yeni yöntemler geliştirilmiş ve ROCt ile gösterilen ROC eğrisi tahminleri önerilmiştir. Bu makalede yaşam çözümlemesi için kullanılan ROC eğrisi tahmin edicileri yöntemlerinden kümülatif duyarlılık ve dinamik seçicilik CD, olay duyarlılığı ve dinamik seçicilik ID, olay duyarlılığı ve statik seçicilik IS ve son olarak naive tahmin edicisi tanıtılmıştır. Bu tahmin edicilerin uygulanabilirliğini göstermek için gerçek veri olan serviks kanseri verisi üzerinde uygulama yapılmıştır. Bu veri için CD1 ile CD2 benzer sonuçlar vermiştir. Ayrıca CD5 ile CD6 da benzer sonuçlar vermiştir. ID1 en düşük sınıflandırma performansına sahipken CD4, 10. aydan itibaren iyi bir sınıflandırma performansı göstermiştir.

Makale detayları

ROC Curves in Survival Analysis and an Application

Yazarlar:

Duru Karasoy

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Biyoistatistik | Uygulamalı İstatistik

Dergi:

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.28 Issue 3, pp.490-503

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001332874

pISSN:

1308-6529

ROC nearest neighbor estimator

ROC Kaplan-Meier

ROC survival

Survival analysis

Time dependent ROC curvesROC en yakın komşu tahmin edicisi

ROC Kaplan-Meier

ROC yaşam

Yaşam çözümlemesi

Zamana bağlı ROC eğrileri

Sayı

Makale detayları

Yazarlar: Duru Karasoy

Yayıncı: Süleyman Demirel University

Ana Başlıklar: Biyoistatistik | Uygulamalı İstatistik

Dergi: Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.28 Issue 3, pp.490-503

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001332874

pISSN: 1308-6529

Sayı

Yazarlar:

Duru Karasoy

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Biyoistatistik | Uygulamalı İstatistik

Dergi:

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.28 Issue 3, pp.490-503

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001332874

pISSN:

1308-6529