University Medical Center Hamburg-Eppendorf, Institute of Medical Biometry and Epidemiology, Hamburg, Germany.
Stat Methods Med Res. 2020 Oct;29(10):2958-2971. doi: 10.1177/0962280220913588. Epub 2020 Apr 16.
In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to obtain a representative sample. In this context, a general problem arises. With a low or high prevalence, the study may be overpowered in one subpopulation. One further issue is the correct pre-specification of the true prevalence. With an incorrect assumption about the prevalence, an over- or underestimated sample size will result.
To obtain the desired power independent of the prevalence, a method for an optimal sample size calculation for the comparison of a diagnostic experimental test with a prespecified minimum sensitivity and specificity is proposed. To face the problem of an incorrectly pre-specified prevalence, a blinded one-time re-estimation design of the sample size based on the prevalence and a blinded repeated re-estimation design of the sample size based on the prevalence are evaluated by a simulation study. Both designs are compared to a fixed design and additionally among each other.
The type I error rates of both blinded re-estimation designs are not inflated. Their empirical overall power equals the desired theoretical power and both designs offer unbiased estimates of the prevalence. The repeated re-estimation design reveals no advantages concerning the mean squared error of the re-estimated prevalence or sample size compared to the one-time re-estimation design. The appropriate size of the internal pilot study in the one-time re-estimation design is 50% of the initially calculated sample size.
A one-time re-estimation design of the prevalence based on the optimal sample size calculation is recommended in single-arm diagnostic accuracy studies.
在确证性诊断准确性研究中，灵敏度和特异性被视为共同的主要终点。对于样本量计算，必须考虑目标人群的患病率以获得具有代表性的样本。在这种情况下，会出现一个普遍的问题。在患病率较低或较高的情况下，研究可能会在一个亚组中过于强大。另一个问题是正确预测真实患病率。如果对患病率的假设不正确，将会导致样本量被高估或低估。
为了获得与患病率无关的所需功效，提出了一种用于比较诊断性实验测试与预设最小灵敏度和特异性的最佳样本量计算方法。为了应对患病率不正确预设的问题，通过模拟研究评估了基于患病率的单次盲重新估计样本量设计和基于患病率的盲重复重新估计样本量设计。将这两种设计与固定设计进行比较，并相互之间进行比较。
两种盲法重新估计设计的Ⅰ型错误率均未膨胀。它们的经验总功效等于所需的理论功效，并且这两种设计都提供了患病率的无偏估计。与单次重新估计设计相比，重复重新估计设计在重新估计的患病率或样本量的均方误差方面没有优势。单次重新估计设计中内部先导研究的适当大小为最初计算的样本量的 50%。
在单臂诊断准确性研究中，建议基于最佳样本量计算的患病率进行单次重新估计设计。
