Target trial emulation to assess real-world efficacy in the Epidemiological Strategy and Medical Economics metastatic breast cancer cohort.
Journal
Journal of the National Cancer Institute
ISSN: 1460-2105
Titre abrégé: J Natl Cancer Inst
Pays: United States
ID NLM: 7503089
Informations de publication
Date de publication:
08 08 2023
08 08 2023
Historique:
received:
10
02
2023
revised:
07
04
2023
accepted:
15
05
2023
medline:
9
8
2023
pubmed:
24
5
2023
entrez:
23
5
2023
Statut:
ppublish
Résumé
Real-world data studies usually consider biases related to measured confounders. We emulate a target trial implementing study design principles of randomized trials to observational studies; controlling biases related to selection, especially immortal time; and measured confounders. This comprehensive analysis emulating a randomized clinical trial compared overall survival in patients with HER2-negative metastatic breast cancer (MBC), receiving as first-line treatment, either paclitaxel alone or combined to bevacizumab. We used data from 5538 patients extracted from the Epidemiological Strategy and Medical Economics-MBC cohort to emulate a target trial using advanced statistical adjustment techniques including stabilized inverse-probability weighting and G-computation, dealing with missing data with multiple imputation, and performing a quantitative bias analysis for residual bias due to unmeasured confounders. Emulation led to 3211 eligible patients, and overall survival estimates achieved with advanced statistical methods favored the combination therapy. Real-world effect sizes were close to that assessed in the existing E2100 randomized clinical trial (hazard ratio = 0.88, P = .16), but the increased sample size allowed to achieve a higher level of precision in real-world estimates (ie, reduced confidence intervals). Quantitative bias analysis confirmed the robustness of the results with respect to potential unmeasured confounding. Target trial emulation with advanced statistical adjustment techniques is a promising approach to investigate long-term impact of innovative therapies in the French Epidemiological Strategy and Medical Economics-MBC cohort while minimizing biases and provides opportunities for comparative efficacy through the synthetic control arms provided. clinicaltrials.gov Identifier NCT03275311.
Sections du résumé
BACKGROUND
Real-world data studies usually consider biases related to measured confounders. We emulate a target trial implementing study design principles of randomized trials to observational studies; controlling biases related to selection, especially immortal time; and measured confounders.
METHODS
This comprehensive analysis emulating a randomized clinical trial compared overall survival in patients with HER2-negative metastatic breast cancer (MBC), receiving as first-line treatment, either paclitaxel alone or combined to bevacizumab. We used data from 5538 patients extracted from the Epidemiological Strategy and Medical Economics-MBC cohort to emulate a target trial using advanced statistical adjustment techniques including stabilized inverse-probability weighting and G-computation, dealing with missing data with multiple imputation, and performing a quantitative bias analysis for residual bias due to unmeasured confounders.
RESULTS
Emulation led to 3211 eligible patients, and overall survival estimates achieved with advanced statistical methods favored the combination therapy. Real-world effect sizes were close to that assessed in the existing E2100 randomized clinical trial (hazard ratio = 0.88, P = .16), but the increased sample size allowed to achieve a higher level of precision in real-world estimates (ie, reduced confidence intervals). Quantitative bias analysis confirmed the robustness of the results with respect to potential unmeasured confounding.
CONCLUSION
Target trial emulation with advanced statistical adjustment techniques is a promising approach to investigate long-term impact of innovative therapies in the French Epidemiological Strategy and Medical Economics-MBC cohort while minimizing biases and provides opportunities for comparative efficacy through the synthetic control arms provided.
DATABASE REGISTRATION
clinicaltrials.gov Identifier NCT03275311.
Identifiants
pubmed: 37220893
pii: 7176372
doi: 10.1093/jnci/djad092
pmc: PMC10407701
doi:
Substances chimiques
Receptor, ErbB-2
EC 2.7.10.1
Paclitaxel
P88XT4IS4D
Bevacizumab
2S9ZZM9Q9V
Banques de données
ClinicalTrials.gov
['NCT03275311']
Types de publication
Randomized Controlled Trial
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
971-980Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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