Clinical trial cost benchmarking: white paper

Estimating the total cost of a clinical trial before it runs is challenging. Public data on past trial costs can be hard to come by, as many companies guard this information carefully. Trials in high income countries and low and middle income countries have very different costs.

Upload your clinical trial protocol and create a cost benchmark with AI

Protocol to cost benchmark

The Clinical Trial Risk Tool uses AI and Natural Language Processing (NLP) to estimate the cost of a trial using the information contained in the clinical trial protocol.

Upload protocol PDF

The Clinical Trial Risk Tool provides a solution. You can upload the protocol or synopsis and it will generate a cost benchmark using similar comparable trials from a public database of grants, and build a basket of comparable trials. This technique is called reference class forecasting, and was developed by Daniel Kahneman and Amos Tversky and helped Kahneman win the Nobel Memorial Prize in Economic Sciences in 2002.

You can find out more in our white paper.

Try looking up your own trial

Trial Cost Benchmarking Demo

For full functionality please try uploading your protocol to the Clinical Trial Risk Tool. The tool will extract the below information and many more parameters from the PDF and use this to model the trial cost for benchmarking purposes.

Trial Name

Interventions

Conditions

Inclusion/Exclusion Criteria

Inclusion Criteria:

Documented recent HIV-1 infection, early diagnosis: clinical symptoms of acute seroconversion and incomplete Western Blot OR negative screening test within the past 6 months and incomplete Western Blot OR risk contact within the 3 months and presumable primo-infection with or without clinical symptoms and incomplete Western Blot
Able and willing to provide written informed consent
Ability to attend the complete schedule of assessments and patient visits for patients participating in option A schedule (described below), or ability to attend a partial schedule of assessments and patient visits for patients participating in option B (described below).
Ability and willingness to have blood and tissue samples collected and stored indefinitely and used for various research purposes.

Exclusion Criteria:

Previous or current history of opportunistic infection (AIDS defining events as defined in category C of the CDC clinical classification), consisting of chronic HIV-1 infection.
Evidence of active HBV infection (Hepatitis B surface antigen positive or HBV viral load positive in the past and no evidence of subsequent seroconversion (=HBV antigen or viral load negative and positive HBV surface antibody).
Evidence of active HCV infection: HCV antibody positive result within 60 days prior to study entry with positive HCV viral load or, if the HCV antibody result is negative, a positive HCV RNA result within 60 days prior to study entry.
Current or known history of cardiomyopathy or significant ischemic or cerebrovascular disease.
Current history of cancer.
Pregnancy or breastfeeding.
Any conditions, including psychiatric and psychological disorders, which will in the opinion of the investigator interfere with the trial conduct or safety of the participant.
Previous participation in a trial evaluating an immune modulating agent
Abnormal laboratory tests results at screening: confirmed Hemoglobin <11g/dl for women and <12 g:dl for men/ confirmed platelet count < 100000/l / confirmed neutrophil count <1000/μl/ confirmed AST and/or ALT > 3xULN
Active drug or alcohol use or dependence that, in the opinion of the site investigator, would interfere with adherence to study requirements.
Acute or serious illness, in the opinion of the site investigator, requiring systemic treatment and/or hospitalization within 60 days prior to entry.

Description

Enrollment

Projection

Range:

Comparable Trials

References

Theresia, Yiallourou, et al. “Cost Drivers and Predictive Modeling of Clinical Trial Costs: Analysis of 101 Global Health Trials.” VeriXiv 2.152 (2025): 152.
DiMasi JA, Hansen RW, Grabowski HG: The price of innovation: New estimates of drug development costs. J. Health Econ. 2003; 22: 151–185.
DiMasi JA, Grabowski HG, Hansen RW: Innovation in the pharmaceutical industry: New estimate of R&D costs. J. Health Econ. 2016; 47: 20–33
Young R, Bekele T, Gunn A, et al.: Developing new health technologies for neglected diseases: A pipeline portfolio review and cost model. Gates Open Research. 2020; 2.
Martin L, Hutchens M, Hawkins C, et al.: How much do clinical trials cost? Nat. Rev. Drug Discov. 2017; 16: 381–382
Flyvbjerg, Bent. Curbing optimism bias and strategic misrepresentation in planning: Reference class forecasting in practice. European planning studies 16.1 (2008): 3-21.