Evidence map›Paper›PMID 32164754›Full record

ReviewOrphanet journal of rare diseases2020

A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG).

Mercedeh Ghadessi, Rui Tang, Joey Zhou, Rong Liu, Chenkun Wang, Kiichiro Toyoizumi, Chaoqun Mei, Lixia Zhang, C Q Deng, Robert A Beckman

Registry-linked trialAbstract readReview
In one paragraph

Review in Orphanet journal of rare diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06163924 (To Investigate the Effect of Multidisciplinary Care Approach on Cardiovascular Risk Modification in Prostate Cancer Patients Receiving Androgen Deprivation Therapy), which is not on this map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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.

NCT06163924 recruitingnot on this mapstarted 2023, after this paper: background citation

To Investigate the Effect of Multidisciplinary Care Approach on Cardiovascular Risk Modification in Prostate Cancer Patients Receiving Androgen Deprivation Therapy

TypeobservationalSponsorChinese University of Hong KongRan2023 to 2029Enrolled130ConditionsCardiovascular Risk, Prostate Cancer
3 · Its place in the literature

Who cites it

70 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Article
  7. Article
  8. Review
  9. Article
  10. Borrowing using historical-bias power prior with empirical Bayes.Journal of biopharmaceutical statistics · 2026
    Article
  11. Review
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. Article

10 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

10 authors.

Mercedeh GhadessiData Science & Analytics, Bayer U.S. LLC, Pharmaceuticals, 100 Bayer Boulevard, Whippany, NJ, 07981, USA.
Rui TangCenter of Excellence, Methodology and Data Visualization, Biostatistics Department, Servier pharmaceuticals, 200 Pier Four Blvd, Boston, MA, 02210, USA. sammi.tang@servier.com.ORCID 0000-0002-8524-771X
Joey ZhouBiometrics, Xcovery LLC, Pharmaceuticals, 11780 U.S. Hwy 1 N #202, Palm Beach Gardens, FL, 33408, USA.
Rong LiuBristol-Myers Squibb, 300 Connell Drive, 7th, Berkeley Heights, NJ, 07922, USA.
Chenkun WangBiostatistics department, Vertex Pharmaceuticals, Inc, 50 Northern Avenue, Boston, MA, 02210, USA.
Kiichiro ToyoizumiBiometrics, Shionogi Inc, 300 Campus Drive Florham Park, Florham Park, NJ, 07932, USA.
Chaoqun MeiInstitute for Clinical and Translational Research, Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53726, USA.
Lixia ZhangScipher Medicine, 260 Charles St Path, Waltham, MA, 02453, USA.
C Q DengUnited Therapeutic Corp, Research Triangle Park, Durham, NC, 27709, USA.
Robert A BeckmanLombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, 20007, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs.

Indexed as

Pharmaceutical PreparationsResearch DesignBiasChildHumansRandomized Controlled Trials as TopicPharmaceutical PreparationsClinical trialHistorical controlPediatric indicationRare diseaseReal world dataReal world evidenceSensitivity analysisSimulation

Identifiers

PMID32164754
PMCPMC7069184

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.