Evidence map›Paper›PMID 24935478›Full record

ArticleJournal of biopharmaceutical statistics2014

Stratification and partial ascertainment of biomarker value in biomarker-driven clinical trials.

Richard Simon

Abstract read
In one paragraph

Article in Journal of biopharmaceutical statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Exploring the Landscape of Biomarkers in Spinal Cord Injury.Topics in spinal cord injury rehabilitation · 2025
    Review
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

1 author.

Richard Simona National Cancer Institute , Rockville , Maryland , USA.

Funding

Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

This article examines the role of stratification of treatment assignment with regard to biomarker value in clinical trials that accept biomarker-positive and -negative patients but have a primary objective of evaluating treatment effect separately for the marker-positive subset. It also examines the issue of incomplete ascertainment of biomarker value and how this affects inference about treatment effect for the biomarker-positive subset of patients. I find that stratifying the randomization for the biomarker ensures that all patients will have tissue collected but is not necessary for the validity of inference for the biomarker-positive subset if there is complete ascertainment. If there is not complete ascertainment of biomarker values, it is important to establish that ascertainment is independent of treatment assignment. Having a large proportion of cases with biomarker ascertainment is not necessary for establishing internal validity of the treatment evaluation in biomarker-positive patients; independence of ascertainment and treatment is the important factor. Having a large proportion of cases with biomarker ascertainment makes it more likely that biomarker-positive patients with ascertainment are representative of the biomarker-positive patients in the clinical trial (with and without ascertainment), but since the patients in the clinical trial are a convenience sample of the population of patients potentially eligible for the trial, requiring a large proportion of cases with ascertainment does not facilitate generalizability of conclusions.

Indexed as

Research DesignBiomarkersHumansModels, StatisticalPredictive Value of TestsProspective StudiesRandomized Controlled Trials as TopicRetrospective StudiesBiomarkersAscertainmentBiomarkerClinical trialsStratification

Identifiers

PMID24935478
PMCPMC4130784

What OpenQuestion holds

Textmetadata
LicenceTDM
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.