Evidence map›Paper›PMID 39512426›Full record

ArticleMathematics (Basel, Switzerland)2022

A Surrogate Measure for Time-Varying Biomarkers in Randomized Clinical Trials.

Rui Zhuang, Fan Xia, Yixin Wang, Ying-Qing Chen

Abstract read
In one paragraph

Article in Mathematics (Basel, Switzerland), 2022. 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

4 authors.

Rui ZhuangDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-0055-4076
Fan XiaDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Yixin WangDepartment of Medicine, Stanford University, Palo Alto, CA 94305, USA.
Ying-Qing ChenDepartment of Medicine, Stanford University, Palo Alto, CA 94305, USA.ORCID 0000-0002-2726-3391

Funding

Predicting PrEP Uptake and Adherence Among Adolescent Girls and Young Women in Sub-Saharan Africa: Leveraging Programmatic and Clinical Trials Data - Administrative SupplementR01HD094682 · NICHD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING, DUERR, ANN C · 2017 to 2021
$3.0M
Statistical Methods for Adherence Issues in HIV Prevention ResearchR01AI121259 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2015 to 2018
$2.5M
Methods for Complex Longitudinal Data in HIV/AIDS Prevention ResearchR56AI140953 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2018 to 2018
$657k
NIAID NIH HHS R01 AI121259NIAID NIH HHS R56 AI140953NICHD NIH HHS R01 HD094682
6 · The paper itself

Abstract

Clinical trials with rare or distant outcomes are usually designed to be large in size and long term. The resource-demand and time-consuming characteristics limit the feasibility and efficiency of the studies. There are motivations to replace rare or distal clinical endpoints by reliable surrogate markers, which could be earlier and easier to collect. However, statistical challenges still exist to evaluate and rank potential surrogate markers. In this paper, we define a generalized proportion of treatment effect for survival settings. The measure's definition and estimation do not rely on any model assumption. It is equipped with a consistent and asymptotically normal non-parametric estimator. Under proper conditions, the measure reflects the proportion of average treatment effect mediated by the surrogate marker among the group that would survive to mark the measurement time under both intervention and control arms.

Indexed as

surrogate measuresurvival settingstime-varying internal markers

Identifiers

PMID39512426
PMCPMC11542621

What OpenQuestion holds

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Registered trials

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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.