Evidence mapPaperPMID 42443403Full record

ReviewNature reviews. Drug discovery2026

Design, simulate, refine: simulation-guided clinical trials for accelerated drug development.

Elias Laurin Meyer, Tim Friede, Andrew P Grieve, Christopher Jennison, Michael Krams, Elizabeth Lorenzi, Husseini K Manji, Tobias Mielke, Jessica R Overbey, Kert Viele and 2 more

Abstract readReview
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In one paragraph

Review in Nature reviews. Drug discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Elias Laurin MeyerBerry Consultants, Vienna, Austria. elias@berryconsultants.com.ORCID http://orcid.org/0000-0001-5398-6334
Tim FriedeDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.ORCID http://orcid.org/0000-0001-5347-7441
Andrew P GrieveSchool of Life Course & Population Health Sciences, King's College London, London, UK.
Christopher JennisonDepartment of Mathematical Sciences, University of Bath, Bath, UK.
Michael KramsBerry Consultants, Vienna, Austria.
Elizabeth LorenziBerry Consultants LLC, Austin, TX, USA.ORCID http://orcid.org/0000-0002-7259-2696
Husseini K ManjiOxford University, Oxford, UK.ORCID http://orcid.org/0009-0007-1679-8749
Tobias MielkeJanssen Cilag GmbH, Neuss, Germany.
Jessica R OverbeyBerry Consultants LLC, Austin, TX, USA.ORCID http://orcid.org/0000-0001-7283-1661
Kert VieleBerry Consultants LLC, Austin, TX, USA.
Marc K WaltonMKWalton Consulting LLC, Philadelphia, PA, USA.
Franz KönigCenter for Medical Data Science, Medical University of Vienna, Vienna, Austria.ORCID http://orcid.org/0000-0002-6893-3304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern clinical trials are embracing innovative strategies including adaptive designs, biomarker-guided enrolment, master protocols and real-time decision-making. These approaches may accelerate discovery and improve outcomes, yet they also introduce additional complexity in study conduct and understanding of the design. Simulation-guided design can be a powerful tool for identifying the optimal design to address research questions and for understanding and communicating how the design functions in practice. Simulations can evaluate the operating characteristics of complex trials, including control of type I error and false discovery rate, power for sample size determination, and probabilities of adaptive decisions at interim analyses. Examining individual simulated trials alongside these summaries provides insight into how the design will perform in practice, allowing potential risks to be anticipated and the design refined. Here, we outline how trial simulation can empower clinical development teams, statisticians, data monitoring committees, regulators, sponsors, funders and patient advocates by improving cross-functional communication, enhancing understanding and providing objective justification for design choices. We also provide best-practice recommendations to ensure that simulation studies are valid, transparent, thorough, efficient and comparable - helping to create trial designs that are scientifically rigorous, ethically sound, operationally practical and ultimately capable of bringing safe and effective therapies to patients as efficiently as possible.

Identifiers

PMID42443403

What Socratic 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 Socratic graph.