Evidence map›Paper›PMID 36372072›Full record

ArticleStatistics in medicine2023

Using a surrogate with heterogeneous utility to test for a treatment effect.

Layla Parast, Tianxi Cai, Lu Tian

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-weighted citation impact, top 16% of its field
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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Efficient Testing Using Surrogate Information.Biometrical journal. Biometrische Zeitschrift · 2025
    Article
  3. Article
  4. Article
  5. 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 at 3 institutions in 1 country.

Layla ParastDepartment of Statistics and Data Sciences, University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0002-7288-1009
Tianxi CaiDepartment of Biostatistics, Harvard University, Boston, Massachusetts, USA.
Lu TianDepartment of Biomedical Data Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-5893-0169
Harvard University · USStanford University · USThe University of Texas at Austin · US

Funding

Statistical Methods for Optimizing Personalized Treatment SelectionR01HL089778 · NHLBI · STANFORD UNIVERSITY · PI LU TIAN · 2008 to 2026
$4.8M
Robust Statistical Methods to Identify and Use Surrogate Markers in DiabetesR01DK118354 · NIDDK · UNIVERSITY OF TEXAS AT AUSTIN · PI PARAST, LAYLA · 2018 to 2025
$2.0M
NHLBI NIH HHS R01 HL089778NIDDK NIH HHS R01 DK118354
6 · The paper itself

Abstract

The primary benefit of identifying a valid surrogate marker is the ability to use it in a future trial to test for a treatment effect with shorter follow-up time or less cost. However, previous work has demonstrated potential heterogeneity in the utility of a surrogate marker. When such heterogeneity exists, existing methods that use the surrogate to test for a treatment effect while ignoring this heterogeneity may lead to inaccurate conclusions about the treatment effect, particularly when the patient population in the new study has a different mix of characteristics than the study used to evaluate the utility of the surrogate marker. In this article, we develop a novel test for a treatment effect using surrogate marker information that accounts for heterogeneity in the utility of the surrogate. We compare our testing procedure to a test that uses primary outcome information (gold standard) and a test that uses surrogate marker information, but ignores heterogeneity. We demonstrate the validity of our approach and derive the asymptotic properties of our estimator and variance estimates. Simulation studies examine the finite sample properties of our testing procedure and demonstrate when our proposed approach can outperform the testing approach that ignores heterogeneity. We illustrate our methods using data from an AIDS clinical trial to test for a treatment effect using CD4 count as a surrogate marker for RNA.

Indexed as

Computer SimulationBiomarkersCD4 Lymphocyte CountHumansBiomarkersheterogeneityhypothesis testnonparametric methodssurrogate markertreatment effect

Identifiers

PMID36372072
PMCPMC10259671
OpenAlexW4308958579

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.