Evidence map›Paper›PMID 32382717›Full record

ArticleEClinicalMedicine2020

Evaluating the evidence behind the surrogate measures included in the FDA's table of surrogate endpoints as supporting approval of cancer drugs.

Bishal Gyawali, Spencer P Hey, Aaron S Kesselheim

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 11 pooled it
6.4field-weighted citation impact, top 2% 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

73 citing papers in PubMed, 11 syntheses or guidelines pooled it, 141 citations in OpenAlex.

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  14. Navigating the Prescription Drug Information System.Clinical pharmacology and therapeutics · 2026
    Review
  15. Challenges in the measurement and valuation of oncological treatments in Spain: recommendations and call to action.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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13 more citing papers are in PubMed but not listed here.

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 1 institution in 2 countries.

Bishal GyawaliProgram On Regulation, Therapeutics, And Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, US.
Spencer P HeyProgram On Regulation, Therapeutics, And Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, US.
Aaron S KesselheimProgram On Regulation, Therapeutics, And Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, US.
Brigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn July 2018, the FDA first published a table listing all surrogate measures that it has used, and may accept for future use, in regulatory approval. However, the strength of surrogacy for those measures was not formally assessed. Using the case example of breast cancer, we aimed to evaluate the strength of correlation of surrogate measures listed in the FDA's Table with overall survival.

methodsThis cross-sectional study of the FDA's Table of Surrogate Endpoints was conducted in May 2019. All surrogate measures listed in the FDA table as appropriate for accelerated or regular approval for breast cancer were extracted. We identified studies evaluating the correlation of treatment benefit in the surrogate with treatment benefit in overall survival and extracted results from the correlation analysis.

findingsFive surrogate endpoints were listed for breast cancer in the FDA website: pathological complete response rates (pCR), event-free survival (EFS), disease-free survival (DFS), objective response rates (ORR), and progression-free survival (PFS), of which pCR was listed as appropriate only for accelerated approval, while the rest were considered appropriate for accelerated or regular approval. No correlation study evaluated the correlation of treatment effects on EFS with that on OS. The results from correlation studies evaluating pCR, DFS, ORR, and PFS suggest that the treatment effects on none of these surrogate measures were strongly correlated with treatment effects on OS (

interpretationUsing breast cancer as an example, we evaluated the underlying evidence for the surrogate endpoints for solid tumors listed in the FDA's Table of Surrogate Endpoints and found weak or missing correlations of treatment effects on these surrogates with treatment effects on OS . Surrogate measures should be predictive of clinical benefit to be useful in supporting regular FDA approval.

fundingWork on this project was funded by the Arnold Ventures. Dr. Kesselheim is also supported by the Harvard-MIT Center for Regulatory Science. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Indexed as

Breast cancerDisease-free survivalEvent-free survivalFDAOverall survivalPathological completeProgression-free survivalResponse ratesSurrogate endpoints

Identifiers

PMID32382717
PMCPMC7201012
OpenAlexW3015759203

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.