PDF(Pubmed)
Abstract：
Understanding the value of genetic screening and testing for monogenic disorders requires high-quality, methodologically robust economic evaluations. This systematic review sought to assess the methodological quality among such studies and examined opportunities for improvement.
We searched PubMed, Cochrane, Embase, and Web of Science for economic evaluations of genetic screening/testing (2013-2019). Methodological rigor and adherence to best practices were systematically assessed using the British Medical Journal checklist.
Across the 47 identified studies, there were substantial variations in modeling approaches, reporting detail, and sophistication. Models ranged from simple decision trees to individual-level microsimulations that compared between 2 and >20 alternative interventions. Many studies failed to report sufficient detail to enable replication or did not justify modeling assumptions, especially for costing methods and utility values. Meta-analyses, systematic reviews, or calibration were rarely used to derive parameter estimates. Nearly all studies conducted some sensitivity analysis, and more sophisticated studies implemented probabilistic sensitivity/uncertainty analysis, threshold analysis, and value of information analysis.
We describe a heterogeneous body of work and present recommendations and exemplar studies across the methodological domains of (1) perspective, scope, and parameter selection; (2) use of uncertainty/sensitivity analyses; and (3) reporting transparency for improvement in the economic evaluation of genetic screening/testing.
We searched PubMed, Cochrane, Embase, and Web of Science for economic evaluations of genetic screening/testing (2013-2019). Methodological rigor and adherence to best practices were systematically assessed using the British Medical Journal checklist.
Across the 47 identified studies, there were substantial variations in modeling approaches, reporting detail, and sophistication. Models ranged from simple decision trees to individual-level microsimulations that compared between 2 and >20 alternative interventions. Many studies failed to report sufficient detail to enable replication or did not justify modeling assumptions, especially for costing methods and utility values. Meta-analyses, systematic reviews, or calibration were rarely used to derive parameter estimates. Nearly all studies conducted some sensitivity analysis, and more sophisticated studies implemented probabilistic sensitivity/uncertainty analysis, threshold analysis, and value of information analysis.
We describe a heterogeneous body of work and present recommendations and exemplar studies across the methodological domains of (1) perspective, scope, and parameter selection; (2) use of uncertainty/sensitivity analyses; and (3) reporting transparency for improvement in the economic evaluation of genetic screening/testing.
摘要：
了解基因筛查和检测单基因疾病的价值需要高质量，方法上稳健的经济评估。本系统综述旨在评估此类研究的方法学质量，并研究改进的机会。
我们搜索了PubMed,科克伦,Embase,和WebofScience用于遗传筛查/检测的经济评估（2013-2019年）。使用英国医学杂志检查表系统地评估了方法学的严谨性和对最佳实践的依从性。
在47项确定的研究中，建模方法有很大的不同，报告细节，和复杂。模型范围从简单的决策树到个体水平的微观模拟，比较2和>20种替代干预措施。许多研究未能报告足够的细节来实现复制，或者没有证明建模假设的合理性，特别是成本计算方法和效用值。荟萃分析，系统评价,或校准很少用于推导参数估计。几乎所有的研究都进行了一些敏感性分析，更复杂的研究实施了概率敏感性/不确定性分析，阈值分析，和信息分析的价值。
我们描述了一个异质的工作主体，并提出了（1）观点的方法论领域的建议和范例研究，范围,和参数选择；（2）使用不确定性/敏感性分析；（3）报告透明度，以改善遗传筛查/测试的经济评估。
我们搜索了PubMed,科克伦,Embase,和WebofScience用于遗传筛查/检测的经济评估（2013-2019年）。使用英国医学杂志检查表系统地评估了方法学的严谨性和对最佳实践的依从性。
在47项确定的研究中，建模方法有很大的不同，报告细节，和复杂。模型范围从简单的决策树到个体水平的微观模拟，比较2和>20种替代干预措施。许多研究未能报告足够的细节来实现复制，或者没有证明建模假设的合理性，特别是成本计算方法和效用值。荟萃分析，系统评价,或校准很少用于推导参数估计。几乎所有的研究都进行了一些敏感性分析，更复杂的研究实施了概率敏感性/不确定性分析，阈值分析，和信息分析的价值。
我们描述了一个异质的工作主体，并提出了（1）观点的方法论领域的建议和范例研究，范围,和参数选择；（2）使用不确定性/敏感性分析；（3）报告透明度，以改善遗传筛查/测试的经济评估。
