Evidence map›Paper›PMID 39972404›Full record

ReviewClinical and translational science2025

Decentralized Clinical Trials in the Era of Real-World Evidence: A Statistical Perspective.

Jie Chen, Junrui Di, Nadia Daizadeh, Ying Lu, Hongwei Wang, Yuan-Li Shen, Jennifer Kirk, Frank W Rockhold, Herbert Pang, Jing Zhao and 3 more

Abstract readReview
In one paragraph

Review in Clinical and translational science, 2025. 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. Review
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  6. Review
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  10. Review
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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

13 authors.

Jie ChenData Science, ECR Global, Shanghai, China.ORCID 0000-0002-8219-2370
Junrui DiGlobal Product Development, Pfizer Inc, Cambridge, Massachusetts, USA.ORCID 0000-0001-6325-8090
Nadia DaizadehAdvanced Quantitative Sciences, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA.ORCID 0000-0002-4136-4068
Ying LuDepartment of Biomedical Data Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-7698-8962
Hongwei WangData and Statistical Sciences AbbVie, North Chicago, Illinois, USA.ORCID 0000-0003-1211-1966
Yuan-Li ShenFood and Drug Administration, Silver Spring, Maryland, USA.ORCID 0000-0002-7345-2521
Jennifer KirkFood and Drug Administration, Silver Spring, Maryland, USA.
Frank W RockholdDepartment of Biostatistics and Bioinformatics, Duke University Medical Center and Duke Clinical Research Institute, Durham, North Carolina, USA.ORCID 0000-0003-3732-4765
Herbert PangPD Data Sciences, Genentech, South San Francisco, California, USA.ORCID 0000-0002-7896-6716
Jing ZhaoBiostatistics and Research Decision Sciences, Merck & Co. Inc., North Wales, Pennsylvania, USA.ORCID 0000-0002-6585-7406
Weili HeData and Statistical Sciences AbbVie, North Chicago, Illinois, USA.ORCID 0009-0000-0989-7318
Andrew PotterFood and Drug Administration, Silver Spring, Maryland, USA.ORCID 0000-0002-0823-8035
Hana LeeFood and Drug Administration, Silver Spring, Maryland, USA.ORCID 0000-0002-8684-1040

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been a growing trend that activities relating to clinical trials take place at locations other than traditional trial sites (hence decentralized clinical trials or DCTs), some of which are at settings of real-world clinical practice. Although there are numerous benefits of DCTs, this also brings some implications on a number of issues relating to the design, conduct, and analysis of DCTs. The Real-World Evidence Scientific Working Group of the American Statistical Association Biopharmaceutical Section has been reviewing the field of DCTs and provides in this paper considerations for decentralized trials from a statistical perspective. This paper first discusses selected critical decentralized elements that may have statistical implications on the trial and then summarizes regulatory guidance, framework, and initiatives on DCTs. More discussions are presented by focusing on the design (including construction of estimand), implementation, statistical analysis plan (including missing data handling), and reporting of safety events. Some additional considerations (e.g., ethical considerations, technology infrastructure, study oversight, data security and privacy, and regulatory compliance) are also briefly discussed. This paper is intended to provide statistical considerations for decentralized trials of medical products to support regulatory decision-making.

Indexed as

Clinical Trials as TopicData Interpretation, StatisticalHumansResearch Designdigital healthcare technologyestimandremote data acquisitionstatistical analysis plan

Identifiers

PMID39972404
PMCPMC11839390

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.