Evidence map›Paper›PMID 25568226›Full record

ArticleNeuro-oncology2015

Combining progression-free survival and overall survival as a novel composite endpoint for glioblastoma trials.

Lorenzo Trippa, Patrick Y Wen, Giovanni Parmigiani, Donald A Berry, Brian M Alexander

Abstract read
In one paragraph

Article in Neuro-oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Novel Clinical Trial Designs in Neuro-Oncology.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022
    Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Unbiased estimation for response adaptive clinical trials.Statistical methods in medical research · 2017
    Article
  12. Article
  13. 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

5 authors.

Lorenzo TrippaDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts (L.T., G.P.); Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts (L.T., G.P.); Center for Neuro-Oncology Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, (P.Y.W., B.M.A.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts (B.M.A.); Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas (D.A.B.).
Patrick Y WenDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts (L.T., G.P.); Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts (L.T., G.P.); Center for Neuro-Oncology Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, (P.Y.W., B.M.A.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts (B.M.A.); Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas (D.A.B.).
Giovanni ParmigianiDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts (L.T., G.P.); Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts (L.T., G.P.); Center for Neuro-Oncology Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, (P.Y.W., B.M.A.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts (B.M.A.); Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas (D.A.B.).
Donald A BerryDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts (L.T., G.P.); Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts (L.T., G.P.); Center for Neuro-Oncology Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, (P.Y.W., B.M.A.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts (B.M.A.); Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas (D.A.B.).
Brian M AlexanderDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts (L.T., G.P.); Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts (L.T., G.P.); Center for Neuro-Oncology Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, (P.Y.W., B.M.A.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts (B.M.A.); Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas (D.A.B.).

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
6 · The paper itself

Abstract

backgroundThe use of auxiliary endpoints may provide efficiencies for clinical trial design, but such endpoints may not have intrinsic clinical relevance or clear linkage to more meaningful endpoints. The purpose of this study was to generate a novel endpoint that considers both overall survival (OS) and earlier events such as progression-free survival (PFS) and determine whether such an endpoint could increase efficiency in the design of glioblastoma clinical trials.

methodsRecognizing that the association between PFS and OS varies depending on therapy and tumor type, we developed a statistical model to predict OS based on PFS as the trial progresses. We then evaluated the efficiency of our model using simulations of adaptively randomized trials incorporating PFS and OS distributions from prior published trials in neuro-oncology.

resultsWhen treatment effects on PFS and OS are concordant, our proposed approach results in efficiency gains compared with randomization based on OS alone while sacrificing minimal efficiency compared with using PFS as the primary endpoint. When treatment effects are limited to PFS, our approach provides randomization probabilities that are close to those based on OS alone.

conclusionUse of OS as the primary endpoint, combined with statistical modeling of the relationship between OS and PFS during the course of the trial, results in more robust and efficient trial designs than using either endpoint alone.

Indexed as

Clinical Trials as TopicDisease-Free SurvivalEndpoint DeterminationGlioblastomaHumansKaplan-Meier EstimateModels, StatisticalRandomized Controlled Trials as Topicadaptive clinical trialsauxiliary endpointsclinical trialendpointsoverall survivalprogression-free survival

Identifiers

PMID25568226
PMCPMC4490868

What OpenQuestion holds

Textmetadata
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.