İstatistiksel Eşleme Metodolojisi ve Rubin Eşleme Yöntemi’ nin Sağlıkta Kullanımı ile İlgili Ampirik Bir Değerlendirme

Amaç: Bu çalışmanın amacı, istatistiksel eşleme yöntemlerini değerlendirmek ve Rubin'in istatistiksel eşleme yöntemini sağlık alanında örnek bir uygulama ile tanıtmaktır. Gereç ve Yöntemler: İstatistiksel eşleme yapay mikro veri setleri oluşturmak için bir yöntem olarak son yıllarda giderek artan bir popülariteye sahiptir. İstatistiksel eşleme, bir araştırmada aynı anda gözlenmemiş (Y,Z) rasgele değişken çiftinden elde edilen taslak bilgi problemini ele almaktadır. Gerçekte Y ve Z birbirinden bağımsız iki farklı araştırmada birbirleri ile örtüşmeyen gözlem birimlerinin oluşturduğu kümelerden elde edilmektedir. Ancak iki araştırmada aynı X değişkeni ortaklaşa gözlenmektedir. İstatistiksel eşleme yöntemleri iki farklı veri kümesinden elde edilen bilginin birleştirilmesini hedeflemektedir. Bulgular: Eşleme işleminde hangi veri setinin alıcı hangisinin donör veri seti olacağı ve kohort değişken kullanmanın söz konusu olup olmayacağı önem arz etmektedir. Çünkü bunlar hem eşlemede hem de eşleme sonucunda hesaplanan uzaklık ölçüsünün değerinin belirlenmesinde belirleyici olmaktadır. Özellikle kohort değişken kullanılması uzaklık ölçüsünün değerini minimum olmaktan uzaklaştırmaktadır. Sonuç: Rubin tarafından önerilen yöntem, diğer yaklaşımlara göre oldukça iyi sonuçlar vermesine rağmen en iyi yöntem veya yöntemler konusunda fikir birliği bulunmamaktadır. En iyi yöntem veya yöntemlere ilişkin görüş birliği bulunmadığından kısıtlı ve kısıtsız yöntemler halen kullanılmaktadır.

Statistical Matching Methodology and An Empirical Evaluation of the Use of Rubin's Matching Method in Health

Aim: The aim of this study is to evaluate statistical matching methods and introduce Rubin's statistical matching method with an exemplary application in the field of health. Material and Methods: Statistical matching has received increasing popularity in the last decades as a method of creating synthetic microdata sets. Statistical matching tackles the problem of drawing information on a pair of random variables (Y,Z) which have not been observed jointly in one sample survey. In fact, Z and Y are available in two distinct and independent surveys whose sets of observed units are non overlapping. The two surveys observe also some common variables X. Statistical matching techniques are aimed at combining information available in two distinct datasets. Results: In the matching process, it is important that which data set will be the recipient and which will be the donor data set and whether it is possible to use cohort variables. Because these are decisive both in matching and determining the value of the distance measure calculated as a result of the matching. In particular, the use of cohort variables makes the value of the distance measure away from being minimum. Conclusions: Although the method proposed by Rubin gives very good results compared to other approaches, there is no consensus on the best method or methods. No consensus regarding the best method or methods has developed; both constrained and unconstrained methods are still being used.

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