The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials

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.

Keywords: Artificial intelligence; Automation; Clinical research; Clinical trial; Dollar cost; Labor costs; Machine learning; Meta-analysis; Systematic review.