شماره ركورد :
116586
عنوان مقاله :
شرايط تعيين حجم كم نمونه در مطالعات متقاطع 2*2
عنوان به زبان ديگر :
Small Sample Size in 2x2 Cross Over Designs: Conditions of Determination
پديد آورندگان :
سليماني ، بهرام نويسنده ,
رتبه نشريه :
-
تعداد صفحه :
5
از صفحه :
180
تا صفحه :
184
كليدواژه :
Normality , Biastatistics , Research Methodology , Cumulant or Moment Generating Function , پزشكي , مطالعات متقاطع 2*2 , حجم كم نمونه , نرمال بودن , Small Sample Size , 2*2 cross-over study
چكيده لاتين :
Introduction. Determination of small sample size in some clinical trials is a matter of importance. In cross-over studies which are one types of clinical trials, the matter is more significant. In this article, the conditions in which determination of small sample size in cross-over studies are possible were considered, and the effect of deviation from normality on the matter has been shown. Methods. The present study has been done on such 2*2 cross-over studies that variable of interest is quantitative one and is measurable by ratio or interval scale. The method of consideration is based on use of variable and sample meanʹs distributions, central limit theorem, method of sample size determination in two groups, and cumulant or moment generating function. Results. In normal variables or transferable to normal variables, there is no restricting factors other than significant level and power of the test for determination of sample size, but in the case of non-normal variables, it should be determined such large that guarantee the normality of sample meanʹs distribution. Discussion. In such cross over studies that because of existence of theoretical base, few samples can be computed, one should not do it without taking applied worth of results into consideration. While determining sample size, in addition to variance, it is necessary to consider distribution of variable, particularly through its skewness and kurtosis coefficients. the more deviation from normality, the more need of samples. Since in medical studies most of the continuous variables are closed to normal distribution, a few number of samples often seems to be adequate for convergence of sample mean to normal distribution.
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