Evidence map›Paper›PMID 41577168›Full record

ReviewDrug discovery today2026

The statistical software revolution in pharmaceutical development: challenges and opportunities in open source.

Daniel Sabanés Bové, Heidi Seibold, Anne-Laure Boulesteix, Juliane Manitz, Alessandro Gasparini, Burak K Günhan, Oliver Boix, Armin Schüler, Sven Fillinger, Sven Nahnsen and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Drug discovery today, 2026. 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.

  1. 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

12 authors.

Daniel Sabanés BovéRCONIS/inferential.biostatistics GmbH, Friedrichstrasse 12, 4055 Basel, Switzerland. Electronic address: daniel@rconis.com.
Heidi SeiboldDigital Research Academy, Bayerstrasse 77c, 80335 Munich, Germany.
Anne-Laure BoulesteixInstitute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, LMU Munich, 81377 Munich, Germany; Munich Center for Machine Learning, Ludwig-Maximilians-Universität München, Institut für Informatik, Oettingenstraße 67, 80538 München, Germany.
Juliane ManitzR Validation Hub, c/o R Consortium, 101 Spear Street, Suite 550, San Francisco, CA 94105, USA.
Alessandro GaspariniDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, 17177 Stockholm, Sweden; Red Door Analytics AB, Valfrid Palmgrens gata 4, 11369 Stockholm, Sweden.
Burak K GünhanMerck Healthcare KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany.
Oliver BoixBayer AG, Aprather Weg 18a, 42113 Wuppertal, Germany.
Armin SchülerBiostatistics and Special Pharmacokinetics Unit, Federal Institute for Drugs and Medical Devices (BfArM), 53175 Bonn, Germany.
Sven FillingerQuantitative Biology Center (QBiC), University of Tübingen, 72076 Tübingen, Germany.
Sven NahnsenQuantitative Biology Center (QBiC), University of Tübingen, 72076 Tübingen, Germany; Biomedical Data Science, Department of Computer Science, University of Tübingen 72076 Tübingen, Germany.
Anna E JacobInstitute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, LMU Munich, 81377 Munich, Germany; National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Lyngby, Denmark.
Thomas JakiFaculty of Informatics and Data Science, University of Regensburg, Bajuwarenstrasse 4, 93053 Regensburg, Germany; MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge CB2 0SR, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Open-source statistical software development is increasingly the preferred solution for leveraging new statistical methods in the pharmaceutical industry. However, with a long history of relying on licensed analysis software, there are philosophical and organizational barriers to overcome. In particular, the sustainability, reliability, usability, and feasibility of maintaining open-source statistical software long-term must be ensured. Here, we describe the open-source revolution that is emerging in the pharmaceutical industry and how it facilitates greater scaling of innovative analytical methods in statistics. We discuss challenges to open-source software adoption and propose mitigation strategies. Furthermore, we illustrate the potential for open-source software development with examples of successful projects, which highlight the roles of cross-company collaboration, career paths, education, and community building.

Indexed as

Drug DevelopmentDrug IndustrySoftwareHumansacademiaclinical trialsopen sourcepharmaceutical industrysoftwarestatistics

Identifiers

PMID41577168
PMCPMC12989278

What OpenQuestion 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 OpenQuestion graph.