Simple, defensible sample sizes based on cost efficiency
Simple, defensible sample sizes based on cost efficiency
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DOI：
10.1111/j.1541-0420.2008.01004_1.x
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发表时间：
2008-06-01
期刊：
影响因子：
1.9
通讯作者：
Segal, Mark R.
中科院分区：
文献类型：
--
作者：
Bacchetti, Peter;McCulloch, Charles E.;Segal, Mark R.
选择样本大小以提供 80% 或更高功效的传统方法忽略了不同样本大小选择的成本影响。然而，在实际操作中，调查人员和资助者往往无法忽视成本。在这里，我们提出并论证了一种根据成本效率（一项研究的预计科学和/或实用价值与其总成本的比率）选择样本量的新方法。通过证明一项研究的预测价值随着样本量的增加而呈现出边际收益递减的趋势，对于研究价值的各种定义，我们能够开发出两种简单的选择，这些选择可以被证明比任何更大的样本量都更具成本效益。首先是选择使每个受试者的平均成本最小化的样本量。第二个是选择样本大小，以最小化总成本除以样本大小的平方根。后一种方法在理论上对于创新研究来说更合理，但也表现得相当好，并且在其他情况下也有一定的合理性。例如，如果假设预计研究价值与特定替代方案的功效成正比，并且总成本是样本量的线性函数，则可以保证这种方法能够产生超过 90% 的功效，或者比任何样本量都更具成本效益。这些方法易于实施，基于可靠的输入，并且合理，因此它们应该被视为当前传统方法的可接受的替代方案。
The conventional approach of choosing sample size to provide 80% or greater power ignores the cost implications of different sample size choices. Costs, however, are often impossible for investigators and funders to ignore in actual practice. Here, we propose and justify a new approach for choosing sample size based on cost efficiency, the ratio of a study's projected scientific and/or practical value to its total cost. By showing that a study's projected value exhibits diminishing marginal returns as a function of increasing sample size for a wide variety of definitions of study value, we are able to develop two simple choices that can be defended as more cost efficient than any larger sample size. The first is to choose the sample size that minimizes the average cost per subject. The second is to choose sample size to minimize total cost divided by the square root of sample size. This latter method is theoretically more justifiable for innovative studies, but also performs reasonably well and has some justification in other cases. For example, if projected study value is assumed to be proportional to power at a specific alternative and total cost is a linear function of sample size, then this approach is guaranteed either to produce more than 90% power or to be more cost efficient than any sample size that does. These methods are easy to implement, based on reliable inputs, and well justified, so they should be regarded as acceptable alternatives to current conventional approaches.