Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Evaluating the Performance of the Time Dependent Net Reclassification Improvement Under Different Settings
Farklı Düzenlerde Zamana Bağlı Net Yeniden Sınıflandırma İyileştirme Performansının Değerlendirilmesi
Eda KARAİSMAİLOĞLUa
aKastamonu University Faculty of Medicine, Department of Biostatistics, Kastamonu, TURKEY
Turkiye Klinikleri J Biostat. 2020;12(1):27-37
doi: 10.5336/biostatic.2019-71294
Article Language: EN
Full Text
ABSTRACT
 Objective: In recent years, new measures have been proposed to evaluate the improvement in classification performance by the addition of a new risk factor to a baseline risk model that includes a set of baseline risk factors. Therefore, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) methods have been utilized in medicine and these metrics have been adapted to time-to-event data in recent years. The aim of this study is to evaluate the performance of the time dependent NRI (NRI(t)) under different scenarios. Material and Methods: Various datasets were composed according to the purpose of each different scenario which were censoring rates (20%, 40%, and 60%), sample sizes (30, 50, 250, 500, and 1000) and number of risk categories (2, 3, and 4). Also, follow-up time was generated from Weibull distribution. All analyses and data generation process were performed using R version 3.4.3. Results: When number of risk categories was specified as three or four, the performance of improved model was better than two-category version. As censoring rate increased, the performance of improved model was decreased. Also, as expected, the performance of the model improved as sample size increased. In general, NRI(t) values were stable for two-category version independently of sample size and censoring rate through follow-up times. But especially for large sample sizes, the performance was higher in early time for three or four risk categories. Conclusion: In this study, it was found that as censoring rate decreased and number of risk categories and sample size increased, the NRI(t) improved.

Keywords: Risk prediction models; net reclassification improvement; simulation; risk factor; reclassification
ÖZET
 Amaç: Son yıllarda, çeşitli risk faktörlerini içeren temel risk modeline yeni bir risk faktörü eklendiğinde, sınıflandırma performansındaki iyileşmeyi değerlendirmek için yeni ölçüler önerilmiştir. Bu kapsamda, net yeniden sınıflandırma iyileştirmesi (NYSİ) ve birleştirilmiş ayrımsama iyileştirmesi (BAİ) yöntemleri tıpta kullanılmakta olup son yıllarda ilgili ölçüler sağkalım verisine de uyarlanmıştır. Bu çalışmanın amacı, zamana bağlı NRI'nın (NRI(t)) performansını farklı senaryolar altında değerlendirmektir. Gereç ve Yöntemler: Her bir senaryonun amacına yönelik olarak, farklı sansürleme oranlarında (% 20, % 40 ve % 60), farklı örneklem büyüklüklerinde (30, 50, 250, 500 ve 1000) ve farklı risk kategorilerinde (2, 3 ve 4) çeşitli veri setleri oluşturulmuştur. Ayrıca, takip süresi Weibull dağılımından üretilmiştir. Tüm analizler ve veri üretme süreci R programı 3.4.3 versiyonu kullanılarak yapılmıştır. Bulgular: Risk kategori sayısı üç veya dört olarak belirlendiğinde, iyileşmiş modelin performansı iki kategorili versiyondan daha iyidir. Sansür oranı arttıkça, iyileşmiş modelin performansı düşmüştür. Ayrıca, beklendiği gibi, örneklem büyüklüğü arttıkça iyileşmiş modelin performansı da artmıştır. Genel olarak, iki kategorili NRI(t) değerleri, örneklem büyüklüğünden ve sansürleme oranından bağımsız olarak izlem süresi boyunca değişmeyen bir performans göstermiştir. Fakat özellikle büyük örneklem büyüklüklerinde, risk kategori sayısı üç veya dört olarak alındığında, performans erken dönemde daha yüksektir. Sonuç: Bu çalışmada, sansürleme oranı azaldıkça ve risk kategori sayısı ve örneklem büyüklüğü arttıkça, NRI(t)'nin arttığı tespit edilmiştir.

Anahtar Kelimeler: Risk tahmin modelleri; net yeniden sınıflandırma iyileştirmesi; benzetim; risk faktörü; tekrar sınıflama
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