系统评价和荟萃分析的高昂成本:呼吁机器学习更多地参与评估临床试验的前景。
The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials.
作者信息
Michelson Matthew, Reuter Katja
机构信息
Evid Science, 2361 Rosecrans Ave #348, El Segundo, 90245, Los Angeles, CA, United States.
Institute for Health Promotion and Disease Prevention Research, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N Soto St, Los Angeles, CA, 90032, United States.
出版信息
Contemp Clin Trials Commun. 2019 Aug 25;16:100443. doi: 10.1016/j.conctc.2019.100443. eCollection 2019 Dec.
BACKGROUND
More than 90% of clinical-trial compounds fail to demonstrate sufficient efficacy and safety. To help alleviate this issue, systematic literature review and meta-analysis (SLR), which synthesize current evidence for a research question, can be applied to preclinical evidence to identify the most promising therapeutics. However, these methods remain time-consuming and labor-intensive. Here, we introduce an economic formula to estimate the expense of SLR for academic institutions and pharmaceutical companies.
METHODS
We estimate the manual effort involved in SLR by quantifying the amount of labor required and the total associated labor cost. We begin with an empirical estimation and derive a formula that quantifies and describes the cost.
RESULTS
The formula estimated that each SLR costs approximately $141,194.80. We found that on average, the ten largest pharmaceutical companies publish 118.71 and the ten major academic institutions publish 132.16 SLRs per year. On average, the total cost of all SLRs per year to each academic institution amounts to $18,660,304.77 and for each pharmaceutical company is $16,761,234.71.
DISCUSSION
It appears that SLR is an important, but costly mechanisms to assess the totality of evidence.
CONCLUSIONS
With the increase in the number of publications, the significant time and cost of SLR may pose a barrier to their consistent application to assess the promise of clinical trials thoroughly. We call on investigators and developers to develop automated solutions to help with the assessment of preclinical evidence particularly. The formula we introduce provides a cost baseline against which the efficiency of automation can be measured.
背景
超过90%的临床试验化合物未能证明足够的疗效和安全性。为帮助缓解这一问题,可将系统文献综述和荟萃分析(SLR)应用于临床前证据,以识别最有前景的治疗方法,SLR可综合针对某一研究问题的现有证据。然而,这些方法仍然耗时且费力。在此,我们引入一个经济公式来估算学术机构和制药公司进行SLR的费用。
方法
我们通过量化所需工作量和相关总劳动力成本来估算SLR所涉及的人工工作量。我们从实证估计开始,推导出一个量化和描述成本的公式。
结果
该公式估计每次SLR的成本约为141,194.80美元。我们发现,平均而言,十大制药公司每年发表118.71篇SLR,十大主要学术机构每年发表132.16篇SLR。平均而言,每个学术机构每年所有SLR的总成本达18,660,304.77美元,每个制药公司的总成本为16,761,234.71美元。
讨论
看来SLR是评估证据总体的一项重要但成本高昂的机制。
结论
随着出版物数量的增加,SLR的大量时间和成本可能成为其持续应用以全面评估临床试验前景的障碍。我们呼吁研究者和开发者开发自动化解决方案,特别是帮助评估临床前证据。我们引入的公式提供了一个成本基线,据此可衡量自动化的效率。
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