On the determination of sample size.
On the determination of sample size.
复制标题
关于样本量的确定。
DOI：
10.1097/01.ede.0000044327.46102.64
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Umbach,DavidM
中科院分区：
文献类型：
--
作者：
Umbach,DavidM
Epidemiologists face issues of sample size every time they design a study. Sample size determination has two distinct aspects. One is the technical aspect of how to calculate the sample size required to meet the desired Type I error rate and the power for any specified state of nature.(By “state of nature,” I mean the exact point in the relevant multidimensional parameter space for the statistical testing problem, the point that corresponds to the hypothesized effect that one seeks to detect. If the specified state of nature is “close” to any state that satisfies the null hypothesis, a relatively large sample size will be required to achieve the desired power, whereas if the specified state of nature is “far” from the null hypothesis, a relatively small sample size will do.)The second and more philosophic aspect of sample size determination is the question of how to specify the state of nature that is most relevant to the study. The paper by Yang et al. 1 is noteworthy in that it provides an avenue for thinking about this more philosophic aspect. Their core idea is that, in designing a study to detect a certain phenomenon (in this case,“gene-environment interaction”), the size of the effect under investigation can be usefully examined from multiple points of view. The authors consider two: one based on the relative odds ratio for interaction (R i), and the other based on a population attributable fraction attributable to interaction (PAF i). Both points of view describe the same phenomenon, the same state of nature. Mathematically, to fix one is to determine the other. Even so, for a given state of nature, the relation between the value of R i and the corresponding value of PAF i is not intuitively obvious. Consequently, it is useful to calculate both. A comparison of their values provides perspective on the size of interaction that one views as reasonable to detect. The authors’ core idea can be useful for design issues in addition to interaction and is worthy of epidemiologists’ attention.
