Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany.
Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands.
BMC Med Res Methodol. 2021 Jun 1;21(1):110. doi: 10.1186/s12874-021-01293-y.
Diagnostic accuracy studies aim to examine the diagnostic accuracy of a new experimental test, but do not address the actual merit of the resulting diagnostic information to a patient in clinical practice. In order to assess the impact of diagnostic information on subsequent treatment strategies regarding patient-relevant outcomes, randomized test-treatment studies were introduced. Various designs for randomized test-treatment studies, including an evaluation of biomarkers as part of randomized biomarker-guided treatment studies, are suggested in the literature, but the nomenclature is not consistent.
The aim was to provide a clear description of the different study designs within a pre-specified framework, considering their underlying assumptions, advantages as well as limitations and derivation of effect sizes required for sample size calculations. Furthermore, an outlook on adaptive designs within randomized test-treatment studies is given.
The need to integrate adaptive design procedures in randomized test-treatment studies is apparent. The derivation of effect sizes induces that sample size calculation will always be based on rather vague assumptions resulting in over- or underpowered study results. Therefore, it might be advantageous to conduct a sample size re-estimation based on a nuisance parameter during the ongoing trial.
Due to their increased complexity, compared to common treatment trials, the implementation of randomized test-treatment studies poses practical challenges including a huge uncertainty regarding study parameters like the expected outcome in specific subgroups or disease prevalence which might affect the sample size calculation. Since research on adaptive designs within randomized test-treatment studies is limited so far, further research is recommended.
诊断准确性研究旨在检验新的实验检测的诊断准确性，但并未涉及该检测结果在临床实践中对患者的实际诊断价值。为了评估诊断信息对后续治疗策略（与患者相关的结局）的影响，引入了随机检测-治疗研究。文献中提出了各种随机检测-治疗研究的设计，包括将生物标志物作为随机生物标志物指导治疗研究的一部分进行评估，但命名法并不统一。
目的是在预先指定的框架内，对不同的研究设计进行清晰描述，考虑到它们的基本假设、优势以及局限性，以及为样本量计算所需的效应量的推导。此外，还对随机检测-治疗研究中的适应性设计进行了展望。
显然需要将适应性设计程序整合到随机检测-治疗研究中。效应量的推导表明，样本量计算将始终基于相当模糊的假设，导致研究结果出现过度或不足的情况。因此，在试验进行期间基于干扰参数进行样本量重新估计可能是有利的。
与常规治疗试验相比，随机检测-治疗研究的实施由于其复杂性增加，存在实际挑战，包括对特定亚组或疾病流行率等预期结局的研究参数存在很大的不确定性，这可能会影响样本量计算。由于随机检测-治疗研究中的适应性设计研究目前还很有限，因此建议进行进一步的研究。
