Evidence map›Paper›PMID 32337484›Full record

ArticleJNCI cancer spectrum2019

Assessing Clinical Equivalence in Oncology Biosimilar Trials With Time-to-Event Outcomes.

Hajime Uno, Deborah Schrag, Dae Hyun Kim, Dejun Tang, Lu Tian, Hope S Rugo, Lee-Jen Wei

Abstract read
In one paragraph

Article in JNCI cancer spectrum, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Hajime UnoDivision of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA.
Deborah SchragDivision of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA.
Dae Hyun KimMarcus Institute for Aging Research, Hebrew Senior Life, Boston, MA.
Dejun TangBiostatistics & Clinical Submission Management, Sandoz Biopharmaceuticals, Holzkirchen, Germany.ORCID 0000-0001-6111-4982
Lu TianDepartment of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA.
Hope S RugoHelen Diller Family Comprehensive Cancer Center, University of California-San Francisco, San Francisco, CA.
Lee-Jen WeiDepartment of Biostatistics, Harvard University, Boston, MA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A typical biosimilar study in oncology uses the overall response evaluated at a specific time point as the primary endpoint, which is generally acceptable regulatorily, to assess clinical equivalence between a biosimilar and its reference product. The standard primary endpoint for evaluating an anticancer therapy, progression-free or overall survival would be a secondary endpoint in a biosimilar trial. With a conventional analytic procedure via, for example, hazard ratio to quantify the group difference, it is difficult and challenging to assess clinical equivalence with respect to progression-free or overall survival because the study generally has a limited number of clinical events observed in the study. In this article, we show that an alternative procedure based on the restricted mean survival time, which has been discussed extensively for design and analysis of a general equivalence study, is readily applicable to a biosimilar trial. Unlike the hazard ratio, this procedure provides a clinically interpretable estimate for assessing equivalence. Using the restricted mean survival time as a summary measure of the survival curve will enhance better treatment decision making in adopting a biosimilar product over the reference product.

Identifiers

PMID32337484
PMCPMC7050006

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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.