Çok merkezli sağlık araştırmalarında tasarım etkisinin tahmini ve örnek büyüklüğüne etkisi

Year 2019, Volume: 17 Issue: 2, 221 - 227, 31.08.2019

İsmet Doğan , Nurhan Doğan

Abstract

Amaç: Bu makalenin amacı, tasarım
etkisi (Deff) değerinden yararlanarak, çok merkezli araştırmalarda farklı sınıf
içi korelasyon katsayıları için örneklem büyüklüğünü incelemektir.  Yöntem:
Çok merkezli çalışmalar çok sayıda metodolojik ve ekonomik avantaj sağlar.
Kümelenmiş / gruplanmış bir yapının söz konusu olduğu durumlarda küme ilişkili
veriler ile karşılaşılmaktadır. Bu tür veriler, bireylerin çok farklı
şekillerde gruplanabilir olmasından dolayı, çoğunlukla sosyal, davranış ve
sağlık bilimlerinde ortaya çıkmaktadır. Basit rasgele örnekleme yöntemine göre
kompleks bir tasarımla üretilen tahminlerin hassaslığındaki fark, tasarım
etkisi olarak bilinmektedir. Tasarım etkisi bir araştırma istatistiğidir. Büyük
ölçekli örneklem araştırmalarında, çıkarımlar genellikle araştırma yapılan
örneklemin rasgeleliği ilkesine dayanır. Böyle bir yaklaşımla, rasgeleliğin
yalnızca örneklemin oluşturulmasındaki olasılık mekanizmasından kaynaklandığı
varsayılır. Bulgular:
Tasarım etkisinin iyi bir tahmini
kümelenmenin söz konusu olduğu araştırmalarda en uygun örneklem büyüklüğünü
hesaplamak için kritik önem taşır. Kümeleme, gerçek kitle varyansını olduğundan
daha düşük tahmin eder ve bu eğer doğru tahmin edilmiş ise, aynı büyüklükteki
basit rasgele örneklemden elde edilecek standart hatalardan büyük olan standart
hatalara yansır. Sonuç: Tasarım etkisi ölçülen göstergeye göre kümeler arasındaki
heterojenliği hesaplamak için bir "düzeltme faktörü" dür. Unutulmamalıdır
ki, Deff bir çarpım faktördür, bu nedenle bir araştırmada Deff değerinin 2
olarak hesaplanması, araştırmada dikkate alınacak örneklem büyüklüğünün iki kat
daha fazla olması demektir. 

Keywords

Çok merkezli araştırma, küme tabanlı araştırma, örneklem büyüklüğü, araştırma tasarımı, tasarım etkisi

Estimation of design effect in multicentre health research and its effect on sample size

Abstract

Objective: The purpose of this
article is to investigate the sample size of different intraclass correlation
coefficients in multicenter studies, taking advantage of the Deff value. Method: Multicenter studies bring
numerous methodological and economic advantages. Cluster-correlated data arises
when there is a clustered/grouped structure to the data. Data of this kind
frequently arises in the social, behavioral, and health sciences since
individuals can be grouped in many different ways. The difference in the precision
of the estimates produced by a complex design relative to a simple random
sample is known as the design effect (Deff). The Deff is a survey statistic. In
large-scale sample surveys, inferences are usually based on the standard
randomization principle of survey sampling. Under such an approach, the
responses are treated as fixed, and the randomness is assumed to come solely
from the probability mechanism that generates the sample.  Results: A good estimate of the Deff is critical for calculating the most
efficient sample size for cluster surveys. Clustering, underestimates true
population variance and this is reflected in standard errors that are larger,
if correctly estimated, than those that would have been obtained from a simple
random sample of the same size. Conclusions: The Deff is a
“correction factor” to account for the heterogeneity between clusters with
regard to the measured indicator. It should be remembered, Deff is a
multiplying factor, so if the value of Deff in the survey is calculated as 2,
this means that the sample size to be taken into account in the survey is twice
as large. 

Keywords

Multicenter research, cluster based survey, sample size, research design, design effect

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