Evidence map›Paper›PMID 39487399›Full record

ArticleBMC medical research methodology2024

Incorporating external controls in the design of randomized clinical trials: a case study in solid tumors.

Emily M Damone, Jiawen Zhu, Herbert Pang, Xiao Li, Yinqi Zhao, Evan Kwiatkowski, Lisa A Carey, Joseph G Ibrahim

Abstract read
In one paragraph

Article in BMC medical research methodology, 2024. 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. 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

8 authors.

Emily M DamoneDepartment of Biostatistics, University of North Carolina, 3109 McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, 27599, USA.
Jiawen ZhuDepartment of Data Science, Product Development, Genentech, Inc, South San Francisco, CA, USA.
Herbert PangDepartment of Data Science, Product Development, Genentech, Inc, South San Francisco, CA, USA.
Xiao LiDepartment of Personalized Healthcare, Product Development, Genentech, Inc, South San Francisco, CA, USA.
Yinqi ZhaoDepartment of Data Science, Product Development, Genentech, Inc, South San Francisco, CA, USA.
Evan KwiatkowskiDepartment of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Lisa A CareyDivision of Oncology, University of North Carolina, Chapel Hill, NC, USA.
Joseph G IbrahimDepartment of Biostatistics, University of North Carolina, 3109 McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, 27599, USA. ibrahim@bios.unc.edu.

Funding

Biostatstics for Research in Environmental HealthT32ES007018 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Stephanie Engel, Rebecca Fry · 1985 to 2026
$31.3M
Applying novel statistical approaches to develop a decision framework for hybrid randomized controlled trial designs which combine internal control arms with patients' data from real-world data sourceU01FD007206 · FDA · GENENTECH, INC. · PI KOSOROK, MICHAEL R, PANG, HERBERT · 2020 to 2022
$768k
FDA HHS U01 FD007206NIEHS NIH HHS T32 ES007018NIH HHS T32ES007018NIH HHS U01 FD007206
6 · The paper itself

Abstract

backgroundThe use of historical external control data in clinical trials has grown in interest and needs when considering the design of future trials. Hybrid control designs can be more efficient to achieve the same power with fewer patients and limited resources. The literature is sparse on appropriate statistical methods which can account for the differences between historical external controls and the control patients in a study. In this article, we illustrate the analysis framework of a clinical trial if a hybrid control design was used after determining an RCT may not be feasible.

methodsWe utilize two previously completed RCTs in nonsquamous NSCLC and a nationwide electronic health record derived de-identified database as examples and compare 5 analysis methods on each trial, as well as a set of simulations to determine operating characteristics of such designs.

resultsIn single trial estimation, the Case Weighted Adaptive Power Prior provided estimated treatment hazard ratios consistent with the original trial's conclusions with narrower confidence intervals. The simulation studies showed that the Case Weighted Adaptive Power Prior achieved the highest power (and well controlled type-1 error) across all 5 methods with consistent study sample size.

conclusionsBy following the proposed hybrid control framework, one can design a hybrid control trial transparently and accounting for differences between control groups while controlling type-1 error and still achieving efficiency gains from the additional contribution from external controls.

Indexed as

Control GroupsRandomized Controlled Trials as TopicResearch DesignCarcinoma, Non-Small-Cell LungComputer SimulationElectronic Health RecordsFemaleHumansLung NeoplasmsMaleClinical trialsHistorical controlsHybrid clinical trialsReal world data

Identifiers

PMID39487399
PMCPMC11529009

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LicenceCC BY-NC-ND
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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.