Sitch Alice J, Dinnes Jacqueline, Hewison Jenny, Gregory Walter, Parkes Julie, Deeks Jonathan J
National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
Test and Prediction Group, Department of Applied Health Sciences, Public Health Building, University of Birmingham, Birmingham, UK.
BMC Med Res Methodol. 2024 Dec 20;24(1):315. doi: 10.1186/s12874-024-02425-w.
The aim of the study was to investigate the development of evidence-based monitoring strategies in a population with progressive or recurrent disease. A simulation study of monitoring strategies using a new biomarker (ELF) for the detection of liver cirrhosis in people with known liver fibrosis was undertaken alongside a randomised controlled trial (ELUCIDATE).
Existing data and expert opinion were used to estimate the progression of disease and the performance of repeat testing with ELF. Knowledge of the true disease status in addition to the observed test results for a cohort of simulated patients allowed various monitoring strategies to be implemented, evaluated and validated against trial data.
Several monitoring strategies ranging in complexity were successfully modelled and compared regarding the timing of detection of disease, the duration of monitoring, and the predictive value of a positive test result. The results of sensitivity analysis showed the importance of accurate data to inform the simulation. Results of the simulation were similar to those from the trial.
Monitoring data can be simulated and strategies compared given adequate knowledge of disease progression and test performance. Such exercises should be carried out to ensure optimal strategies are evaluated in trials thus reducing research waste. Monitoring data can be generated and monitoring strategies can be assessed if data is available on the monitoring test performance and the test variability. This work highlights the data necessary and the general method for evaluating the performance of monitoring strategies, allowing appropriate strategies to be selected for evaluation. Modelling work should be conducted prior to full scale investigation of monitoring strategies, allowing optimal monitoring strategies to be assessed.
本研究旨在调查针对患有进展性或复发性疾病人群的循证监测策略的发展情况。在一项随机对照试验（ELUCIDATE）的同时，开展了一项使用新型生物标志物（ELF）检测已知肝纤维化患者肝硬化情况的监测策略模拟研究。
利用现有数据和专家意见来估计疾病进展情况以及ELF重复检测的性能。除了模拟患者队列的观察测试结果外，了解真实疾病状态有助于实施、评估和对照试验数据验证各种监测策略。
成功建立了几种复杂程度各异的监测策略模型，并就疾病检测时间、监测持续时间以及阳性检测结果的预测价值进行了比较。敏感性分析结果表明准确数据对模拟的重要性。模拟结果与试验结果相似。
在充分了解疾病进展和检测性能的情况下，可以模拟监测数据并比较策略。应开展此类工作以确保在试验中评估最佳策略，从而减少研究浪费。如果有关于监测测试性能和测试变异性的数据，就可以生成监测数据并评估监测策略。这项工作突出了评估监测策略性能所需的数据和一般方法，以便选择合适的策略进行评估。在对监测策略进行全面调查之前应开展建模工作，以便评估最佳监测策略。
