Designing trials with multiple objectives with graphical approach for multiple scenarios.

A Adam Ding, Yulin Li, Samuel S Wu

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. 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

3 authors.

A Adam DingDepartment of Mathematics, Northeastern University, Boston, MA, USA.

Yulin LiDepartment of Biostatistics, University of Florida, Gainesville, FL, USA.

Samuel S WuHealth Informatics Institute, University of Southern Florida, Tampa, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical trials with multiple objectives require careful control of the overall error rate through adjustments for multiple hypothesis testing. Design optimization must account for these adjustments across various plausible scenarios. This paper introduces a framework for optimizing trial designs in multiple scenarios using graphical approaches. The method is demonstrated in a pain study trial, highlighting improvements in overall performance across multiple scenarios.

Indexed as

Bonferroni procedureGraphical approachMultiple testingPower optimization

Identifiers

PMID41311868

DOI10.1016/j.conctc.2025.101569

PMCPMC12651839

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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.