Efird Jimmy Thomas, Nielsen Susan Searles
Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, S.9.548 (MLC 7000), Cincinnati, Ohio 45229-3039, USA.
Int J Environ Res Public Health. 2008 Dec;5(5):394-8. doi: 10.3390/ijerph5050394.
Epidemiological studies commonly test multiple null hypotheses. In some situations it may be appropriate to account for multiplicity using statistical methodology rather than simply interpreting results with greater caution as the number of comparisons increases. Given the one-to-one relationship that exists between confidence intervals and hypothesis tests, we derive a method based upon the Hochberg step-up procedure to obtain multiplicity corrected confidence intervals (CI) for odds ratios (OR) and by analogy for other relative effect estimates. In contrast to previously published methods that explicitly assume knowledge of P values, this method only requires that relative effect estimates and corresponding CI be known for each comparison to obtain multiplicity corrected CI.
流行病学研究通常会检验多个零假设。在某些情况下,使用统计方法来考虑多重性可能是合适的,而不是仅仅随着比较次数的增加而更加谨慎地解释结果。鉴于置信区间和假设检验之间存在一对一的关系,我们基于霍奇伯格逐步法推导出一种方法,以获得比值比(OR)的多重性校正置信区间(CI),并类推用于其他相对效应估计。与先前明确假设已知P值的已发表方法不同,该方法只要求知道每次比较的相对效应估计值和相应的CI,就可以获得多重性校正CI。