Predicting clinical trial duration via statistical and machine learning models.

Joonhyuk Cho, Qingyang Xu, Chi Heem Wong, Andrew W Lo

Abstract read

In one paragraph

Article in Contemporary clinical trials communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Artificial intelligence in clinical trials-state of the evidence, gaps, and next steps.EClinicalMedicine · 2026
Review

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

4 authors.

Joonhyuk ChoMIT Laboratory for Financial Engineering, Cambridge, MA, USA.

Qingyang XuMIT Laboratory for Financial Engineering, Cambridge, MA, USA.

Chi Heem WongMIT Laboratory for Financial Engineering, Cambridge, MA, USA.

Andrew W LoMIT Laboratory for Financial Engineering, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We apply survival analysis as well as machine learning models to predict the duration of clinical trials using the largest dataset so far constructed in this domain. Neural network-based DeepSurv yields the most accurate predictions and we identify key factors that are most predictive of trial duration. This methodology may help clinical researchers optimize trial designs for expedited testing, and can also reduce the financial risk of drug development, which in turn will lower the cost of funding and increase the amount of capital allocated to this sector.

Indexed as

Clinical trialCox proportional hazards modelFeature importanceMachine learningSurvival analysis

Identifiers

PMID40248172

DOI10.1016/j.conctc.2025.101473

PMCPMC12005917

What Socratic holds

Textmetadata

LicenceCC BY

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 Socratic graph.