Using the confidence interval confidently
Using the confidence interval confidently
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DOI：
10.21037/jtd.2017.09.14
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发表时间：
2017-10-01
影响因子：
2.5
通讯作者：
Hazra, Avijit
中科院分区：
文献类型：
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作者：
Hazra, Avijit
生物医学研究很少针对整个人群进行，而是从人群中抽取样本。尽管我们使用样本，但我们的目标是描述基础人群并做出推论。可以使用样本统计量和样本中的误差估计来大致了解总体参数，不是作为单个值，而是作为一系列值。该范围是根据所需置信水平估计的置信区间 (CI)。样本统计量 CI 的计算采用一般形式：CI = 点估计 +/- 误差幅度，其中误差幅度由标准正态曲线得出的临界值 (z) 与点估计的标准误差的乘积给出。标准误差的计算会有所不同，具体取决于感兴趣的样本统计量是否是均值、比例、优势比 (OR) 等。影响 CI 宽度的因素包括所需的置信水平、样本大小和样本的变异性。尽管 95% CI 最常用于生物医学研究，但可以计算任何置信水平的 CI。对于同一样本，99% CI 将比 95% CI 更宽。临床重要性和统计显着性之间的冲突是生物医学研究中的一个重要问题。临床重要性最好通过观察效应大小来推断，即实际变化或差异有多大。然而，P 方面的统计显着性仅表明概率方面是否存在差异。 CI 的使用通过提供实际临床效果的估计来补充 P 值。最近，临床试验被专门设计为优效性、非劣效性或等效性研究。这些替代试验设计的结论基于 CI 值，而不是组间比较的 P 值。
Biomedical research is seldom done with entire populations but rather with samples drawn from a population. Although we work with samples, our goal is to describe and draw inferences regarding the underlying population. It is possible to use a sample statistic and estimates of error in the sample to get a fair idea of the population parameter, not as a single value, but as a range of values. This range is the confidence interval (CI) which is estimated on the basis of a desired confidence level. Calculation of the CI of a sample statistic takes the general form: CI = Point estimate +/- Margin of error, where the margin of error is given by the product of a critical value (z) derived from the standard normal curve and the standard error of point estimate. Calculation of the standard error varies depending on whether the sample statistic of interest is a mean, proportion, odds ratio (OR), and so on. The factors affecting the width of the CI include the desired confidence level, the sample size and the variability in the sample. Although the 95% CI is most often used in biomedical research, a CI can be calculated for any level of confidence. A 99% CI will be wider than 95% CI for the same sample. Conflict between clinical importance and statistical significance is an important issue in biomedical research. Clinical importance is best inferred by looking at the effect size, that is how much is the actual change or difference. However, statistical significance in terms of P only suggests whether there is any difference in probability terms. Use of the CI supplements the P value by providing an estimate of actual clinical effect. Of late, clinical trials are being designed specifically as superiority, non-inferiority or equivalence studies. The conclusions from these alternative trial designs are based on CI values rather than the P value from intergroup comparison.