Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Örneklem Büyüklüğünün Tahmin-Doğrulama Metrikleri Üzerindeki Etkisinin İncelenmesi: Bir Simülasyon Çalışması
Examining the Effects of Sample Size on Forecast-Verification Metrics: A Simulation Study
İsmet DOĞANa , Nurhan DOĞANa
aAfyonkarahisar Sağlık Bilimleri Üniversitesi Tıp Fakültesi, Biyoistatistik ve Tıbbi Bilişim AD, Afyonkarahisar, Türkiye
Turkiye Klinikleri J Biostat. 2024;16(2):107-17
doi: 10.5336/biostatic.2024-103854
Article Language: TR
Full Text
ÖZET
 Amaç: Bu çalışmanın amacı, yaygın kullanılan tahmin doğruluk metriklerinin tanıtılması ve farklı örneklem büyüklükleri için performanslarının karşılaştırılmasıdır. Gereç ve Yöntemler: Tahmin doğrulama metrikleri, bilimin çeşitli disiplinlerinde karar verme araçları olarak yaygın şekilde kullanılmaktadır. İki sonuçlu tahminler söz konusu olduğunda tahmin doğrulamaya yönelik ayırt etme kapasitesi metrikler kullanılarak değerlendirilebilir. Bu metriklere eşiğe bağlı metrikler denir. Farklı örneklem büyüklüklerinin tahmin-doğrulama metriklerinin performansı üzerindeki etkisini ortaya koymak amacıyla yaygın olarak kullanılan 9 farklı eşik bazlı metrik dikkate alınarak bir simülasyon çalışması yapılmıştır. Python-random kütüphanesi kullanılarak 10 ≤ n ≤ 1000 aralığında 35 farklı n değeri için veri elde edilmiştir. Performans değerlendirmesinde Kappa katsayısı için literatürde önerilen değerler ve yorumlama düzeyleri dikkate alınmıştır. Bulgular: Farklı örneklem büyüklüklerinin dikkate alındığı bu çalışmadan elde edilen sonuçlardan, örneklem büyüklüğünün artırılması veya azaltılmasının tahmin doğrulama üzerindeki etkisinin neredeyse sabit olduğu tespit edilmiştir. Dikkate alınan tüm örneklem büyüklükleri için tahminlerin yaklaşık %50'sinin neredeyse tüm metrikler için ''doğrulama yok veya önemsiz'' yorumlama seviyesine sahip olduğu görülmüştür. Metrikler adil, orta, önemli ve mükemmel doğrulama düzeyleri bir arada dikkate alınarak liberal olma bakımından sıralandığında sıralama F, Odds Oranı Beceri Puanı, Kritik Başarı İndeksi, Peirce Beceri Puanı, Clayton Beceri Puanı, Tahmin Beceri İndeksi, Heidke Beceri Puanı, Kappa ve Gilbert Beceri Puanı şeklinde elde edilmiştir. Sonuç: Tahmin doğrulama metrikleri, örneklem büyüklüğünden ziyade gözlem değerlerinin 2x2 çapraz tablolardaki gözelere dağılımdan daha çok etkilenmektedir.

Anahtar Kelimeler: Tahmin doğrulaması; iki sonuçlu olaylar; tahmin beceri puanı; doğrulama çalışmaları
ABSTRACT
 Objective: The aim of this study is to introduce commonly used forecast-verification metrics and compare their performance for different sample sizes. Material and Methods: Forecast verification metrics are widely used as decision support tools in various scientific disciplines. If the prediction results are binary, metrics can be used to evaluate the discriminative power for prediction verification. These metrics are called thresholddependent metrics. In order to show the effect of different sample sizes on the performance of forecast-verification metrics, a simulation study was conducted considering nine different commonly used threshold-based metrics. Using the Python-random library, data were obtained for 35 different n values in the range of 10 ≤ n ≤ 1000. For the performance evaluation, the values and interpretation levels recommended in the literature for the Kappa coefficient were taken into account. Results: From the results of this study, where different sample sizes were considered, it was found that the effect of increasing or decreasing the sample size on forecast verification was almost constant. It was observed that for all sample sizes considered, around 50 percent of the estimates had ''none or none to low'' levels of interpretation for almost all metrics. When the metrics were ranked in terms of liberality by considering fair, moderate, substantial and perfect levels of verification together, the order was obtained as F, Odds Ratio Skill Score, Critical Success Index, Peirce Skill Score, Clayton Skill Score, Prediction Skill Index, Heidke Skill Score, Kappa and Gilbert Skill Score. Conclusion: Forecast-verification metrics are more affected by the distribution of observations across cells in 2x2 crosstabs than by sample size.

Keywords: Forecast-verification; binary events; prediction skill score; validation studies
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