Evidence map›Paper›PMID 39656431›Full record

ReviewTherapeutic innovation & regulatory science2025

Data Monitoring Committee Reports: Telling the Data's Story.

Lijuan Zeng, Toshimitsu Hamasaki, Scott R Evans

Abstract readReview
PubMed Publisher
In one paragraph

Review in Therapeutic innovation & regulatory science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. 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

3 authors.

Lijuan ZengThe Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Rockville, USA.
Toshimitsu HamasakiThe Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Rockville, USA.
Scott R EvansThe Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Rockville, USA. sevans@bsc.gwu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A Data Monitoring Committee (DMC) plays a pivotal role in monitoring participant safety and efficacy and overseeing the integrity of clinical trials. DMCs accomplish this mission by periodically reviewing accumulating data to assess benefits and harms of interventions in ongoing studies and making subsequent recommendations regarding future clinical trial conduct to the trial sponsor. Reports summarizing data from the clinical trial are prepared for the DMC by statistical and data analysis centers to inform DMC decision-making. In practice however, these reports are often disorganized, complex, and provide overwhelming detail yet insufficient information, that hinders accurate and efficient interpretation of interim data. This review paper delves into the nuances of preparing effective DMC reports, highlighting the importance of simplicity, clarity, and thoughtful relevance in data presentation. We discuss structured approaches for preparing closed reports, which deal with sensitive and sometimes messy interim data, and underscore the use of visual summaries and narrative elements that enhance comprehension and facilitate efficient assessments of trial data. The paper outlines key principles for preparing DMC reports and provides practical guidance on their structure. Ultimately, this guidance seeks to ensure that the data's story is clearly and efficiently conveyed to facilitate the DMC decision-making process.

Indexed as

Clinical Trials as TopicClinical Trials Data Monitoring CommitteesHumansBenefit-riskDMCDSMBReportingReports

Identifiers

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.