Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Örneklem Büyüklüğünün Net Etki İçin Tedavi Edilmesi Gereken Sayı Üzerindeki Etkileri: Bir Simülasyon Çalışması
The Effects of Sample Size on the Number Needed to Treat for Net Effect: A Simulation Study
İsmet DOĞANa , Nurhan DOĞANa
aAfyonkarahisar Sağlık Bilimleri Üniversitesi Tıp Fakültesi, Biyoistatistik ve Tıbbi Bilişim ABD, Afyonkarahisar, Türkiye
Turkiye Klinikleri J Biostat. 2023;15(3):182-90
doi: 10.5336/biostatic.2023-98865
Article Language: TR
Full Text
ÖZET
Amaç: Literatürde, tedavi edilmesi gereken sayı ve net etki için tedavi edilmesi gereken sayı ile ilgili çalışmalar bulunmaktadır. Ancak örneklem büyüklüğünün net etki için tedavi edilmesi gereken sayı üzerindeki etkilerinin incelendiği bir çalışma bulunmamaktadır. Dolayısıyla net etki için örneklem büyüklüğünün etkisinin belirlenmesi gerekmektedir. Bu çalışmanın amacı, örneklem büyüklüğünün net etki için tedavi edilmesi gereken sayı üzerindeki etkisini incelemektir. Gereç ve Yöntemler: Çalışmada Phytonrandom kütüphanesi kullanılarak 10 ≤ n ≤ 1000 aralığında yer alan 35 farklı n değeri için veri türetilmiştir. Verilerin türetilmesinde önce a, b, c ve d ile gösterilen gözelerden hangisine değer atanacağı sonra da ilgili gözeye atanacak değer belirlenmiştir. n=10 için 286, n=15 için 815 ve n ≥ 20 için biner farklı veri seti çalışmada kullanılmıştır. Bulgular: için önerilen hesaplama yöntemleri tatmin edici sonuçlar vermemektedir. Tedavilerin çoğu yalnızca hastaların sonuçlarını iyileştirmekle kalmaz, aynı zamanda olumsuz yan etki riskini de artırır. Faydaları en üst düzeye çıkarmak ve zararı en aza indirmek için tüm paydaşlar için randomize kontrollü deneme sonuçlarından elde edilen birleşik fayda ve zarar profilleri dikkate alınmalıdır. , tedavilerin hem faydalarını hem de zararlarını tek bir özet istatistiğe dâhil etmenin kolay ve kabul edilebilir bir yoludur. Sonuç: Çalışmadan elde edilen bulgulara göre değerlerinin tamamına yakını için olumlu etki için gerekli sayıdan çok olumsuz etki için gereken sayı ile karşılaşma olasılığının daha yüksek olacağı söylenebilir. Genel olarak, sonuçların yaklaşık yarısının olumsuz bir sonuç için gereken sayıyı göstereceği ve 1/3'ünün olumlu bir etki için gereken sayıyı göstereceği neredeyse kesin olarak söylenebilir.

Anahtar Kelimeler: Net etki için tedavi için gerekli sayı; fayda; zarar; randomize kontrollü çalışma; iki sonuçlu veri
ABSTRACT
Objective: There are studies in the literature regarding the number needed to treat and the number to treat for net effect. However, there are no studies that have examined the effects of sample size on the number needed to treat for net effect. It is therefore necessary to determine the effect of sample size on the number needed to treat for the net effect. The aim of this study is to examine the effects of sample size on the number needed to treat for net effect. Material and Methods: In the study, data were derived for 35 different n values in the range of 10 ≤ n ≤ 1000 using the Phyton-random library. In the derivation of the data, firstly, which cell shown with a, b, c and d will be assigned value, then the value to be assigned to the relevant cell was determined. 286 for n=10, 815 for n=15 and 1,000 different data sets for n ≥ 20 were used in the study. Results: The calculation methods proposed for do not provide satisfactory results. Most treatments not only improve patients' outcomes but also increase their risk of adverse side effects. Combined benefit and harm profiles from randomized controlled trial results should be considered for all stakeholders to maximize benefits and minimize harm. is an easy and acceptable way to include both the benefits and harms of treatments in a single summary statistic. Conclusion: According to the findings obtained from the study, it can be said that for almost all values, it will be more likely to encounter the number required to cause harm rather than the number required for benefit. Overall, it can almost certainly be said that about half of the results will show the number needed for a harmful outcome and one-third will show the number needed for a positive impact.

Keywords: Number needed to treat for net effect; benefit; harm; randomized controlled trial; binary data
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