Evidence map›Paper›PMID 35449371›Full record

ArticleThe AAPS journal2022

Machine Learning Prediction of Clinical Trial Operational Efficiency.

Kevin Wu, Eric Wu, Michael DAndrea, Nandini Chitale, Melody Lim, Marek Dabrowski, Klaudia Kantor, Hanoor Rangi, Ruishan Liu, Marius Garmhausen and 5 more

Abstract read
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Article in The AAPS journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Analysis of eligibility criteria clusters based on large language models for clinical trial design.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  10. Review
  11. Resource management and capacity planning for clinical trial sites.Journal of clinical and translational research · 2024
    Article
  12. Article
  13. Article
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

15 authors.

Kevin WuDepartment of Biomedical Data Science, Stanford University, Stanford, California, USA. kevinywu@stanford.edu.
Eric WuDepartment of Electrical Engineering, Stanford University, Stanford, California, USA.
Michael DAndreaGenentech, South San Francisco, San Francisco, California, USA.
Nandini ChitaleGenentech, South San Francisco, San Francisco, California, USA.
Melody LimGenentech, South San Francisco, San Francisco, California, USA.
Marek DabrowskiRoche Pharmaceuticals, Warsaw, Poland.
Klaudia KantorRoche Pharmaceuticals, Warsaw, Poland.
Hanoor RangiRoche Pharmaceuticals, Mississauga, Canada.
Ruishan LiuDepartment of Electrical Engineering, Stanford University, Stanford, California, USA.
Marius GarmhausenRoche Pharmaceuticals, Basel, Switzerland.
Navdeep PalGenentech, South San Francisco, San Francisco, California, USA.
Chris HarbronRoche Pharmaceuticals, Welwyn Garden City, UK.
Shemra RizzoGenentech, South San Francisco, San Francisco, California, USA.
Ryan CoppingGenentech, South San Francisco, San Francisco, California, USA.
James ZouDepartment of Biomedical Data Science, Stanford University, Stanford, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.

Indexed as

Machine LearningClinical Trials as TopicForecastingHumansPatient Selectionclinical trialsmacxhine learningoperational efficiency

Identifiers

PMID35449371

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.