Evidence map›Paper›PMID 39761045›Full record

ArticleJAMA network open2025

Use of Clinical Trial Characteristics to Estimate Costs of New Drug Development.

Andrew Mulcahy, Stephanie Rennane, Daniel Schwam, Reid Dickerson, Lawrence Baker, Kanaka Shetty

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Review
  2. Article
  3. New hit compounds againstAntimicrobial agents and chemotherapy · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Review
  10. The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026
    Review
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Andrew MulcahyRAND, Arlington, Virginia.
Stephanie RennaneRAND, Santa Monica, California.
Daniel SchwamRAND, Arlington, Virginia.
Reid DickersonRAND, Santa Monica, California.
Lawrence BakerRAND, Santa Monica, California.
Kanaka ShettyRAND, Santa Monica, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Despite their importance to patients, health, and industry, the magnitude of investments in drug research and development (R&D) remain nebulous. New policies require more granular and transparent R&D cost estimates to better balance incentives for innovation and returns to developers. Objective: To estimate per-drug R&D costs using a novel, reproduceable approach and to describe firm-level R&D costs per discrete unit of R&D activity (1 patient-month). Design, Setting, and Participants: This economic evaluation used cross-sectional data to estimate 2014 to 2019 costs per patient-month. Costs per patient-month were calculated using data from 268 US publicly traded drug developers, contributing 1311 firm-year observations, that were highest ranked by assets or market capitalization, after exclusions. Per-drug costs were calculated from all R&D activity through approval for a cohort of 38 new drugs approved by the US Food and Drug Administration in 2019. Data were analyzed from January 2022 to July 2024. Exposure: R&D activity, measured in terms of clinical trial patient-months. Main Outcomes and Measures: This study used a 2-step approach to estimate R&D costs, first allocating firm-year-level total R&D spending across similarly aggregated patient-months, and then aggregating these incremental costs to estimate drug-level R&D costs per new drug. Results: Among 268 developers assessed, 20 large firms accounted for 80.8% of all patient-months and had 27.4% lower mean and 26.7% lower median costs per patient-month compared with other firms. Each 1% increase in patient-months was associated with a 0.9% increase in R&D costs. R&D costs per new drug were highly skewed, with a lower median (IQR), at $708 million ($247 million to $1.42 billion) than mean (SD), at $1.31 ($1.92) billion, after adjusting for the cost of capital and discontinued products. Without these adjustments, direct costs per new drug were a median (IQR) of $150 ($67.6-$453) million and a mean (SD) $369 of ($684) million. While estimated R&D costs varied in sensitivity analyses, mean costs were always substantially greater than median costs. Conclusions and Relevance: This economic evaluation found median per-drug R&D costs toward the lower end of the range from prior studies, with a mean closer to the middle of the existing range despite the broad scope of included costs. These findings suggest parallel development across indications, adjustment for discontinued products, and a small number of expensive development programs are particularly important drivers of R&D costs.

Indexed as

Clinical Trials as TopicDrug DevelopmentCross-Sectional StudiesDrug ApprovalDrug CostsDrug IndustryHumansUnited StatesUnited States Food and Drug Administration

Identifiers

PMID39761045
PMCPMC11704977

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.