McCray Gareth P J, Titman Andrew C, Ghaneh Paula, Lancaster Gillian A
Institute of Primary Care and Health Sciences, Keele University, David Weatherall Building, Stoke-on-Trent, ST5 5BG, UK.
Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA14YF, UK.
BMC Med Res Methodol. 2017 Jul 14;17(1):102. doi: 10.1186/s12874-017-0386-5.
The sample size required to power a study to a nominal level in a paired comparative diagnostic accuracy study, i.e. studies in which the diagnostic accuracy of two testing procedures is compared relative to a gold standard, depends on the conditional dependence between the two tests - the lower the dependence the greater the sample size required. A priori, we usually do not know the dependence between the two tests and thus cannot determine the exact sample size required. One option is to use the implied sample size for the maximal negative dependence, giving the largest possible sample size. However, this is potentially wasteful of resources and unnecessarily burdensome on study participants as the study is likely to be overpowered. A more accurate estimate of the sample size can be determined at a planned interim analysis point where the sample size is re-estimated.
This paper discusses a sample size estimation and re-estimation method based on the maximum likelihood estimates, under an implied multinomial model, of the observed values of conditional dependence between the two tests and, if required, prevalence, at a planned interim. The method is illustrated by comparing the accuracy of two procedures for the detection of pancreatic cancer, one procedure using the standard battery of tests, and the other using the standard battery with the addition of a PET/CT scan all relative to the gold standard of a cell biopsy. Simulation of the proposed method illustrates its robustness under various conditions.
The results show that the type I error rate of the overall experiment is stable using our suggested method and that the type II error rate is close to or above nominal. Furthermore, the instances in which the type II error rate is above nominal are in the situations where the lowest sample size is required, meaning a lower impact on the actual number of participants recruited.
We recommend multinomial model maximum likelihood estimation of the conditional dependence between paired diagnostic accuracy tests at an interim to reduce the number of participants required to power the study to at least the nominal level.
ISRCTN ISRCTN73852054 . Registered 9th of January 2015. Retrospectively registered.
在配对比较诊断准确性研究中，即比较两种检测程序相对于金标准的诊断准确性的研究中，为使研究达到名义检验效能所需的样本量取决于两种检测之间的条件依赖性——依赖性越低，所需样本量越大。事先，我们通常不知道两种检测之间的依赖性，因此无法确定所需的确切样本量。一种选择是使用最大负依赖性的隐含样本量，这会给出可能最大的样本量。然而，这可能会造成资源浪费，并给研究参与者带来不必要的负担，因为研究可能效能过高。在计划的中期分析点重新估计样本量时，可以确定更准确的样本量估计值。
本文讨论了一种基于最大似然估计的样本量估计和重新估计方法，该方法在隐含多项模型下，针对计划中期两种检测之间条件依赖性的观测值以及（如有需要）患病率进行估计。通过比较两种检测胰腺癌的程序的准确性来说明该方法，一种程序使用标准检测组合，另一种程序在标准检测组合的基础上增加PET/CT扫描，所有这些均相对于细胞活检的金标准。对所提出方法的模拟说明了其在各种条件下的稳健性。
结果表明，使用我们建议的方法，总体实验的I型错误率稳定，II型错误率接近或高于名义水平。此外，II型错误率高于名义水平的情况是在需要最小样本量的情形下，这意味着对实际招募的参与者数量影响较小。
我们建议在中期对配对诊断准确性检测之间的条件依赖性进行多项模型最大似然估计，以减少使研究达到至少名义检验效能所需的参与者数量。
ISRCTN ISRCTN73852054。于2015年1月9日注册。追溯注册。
