» Articles » PMID: 36608311

Target Trial Emulation: A Design Tool for Cancer Clinical Trials

Overview
Date 2023 Jan 6
PMID 36608311
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: To apply target trial emulation to explore the potential impact of eligibility criteria on the primary outcome of a randomized controlled trial.

Methods: Simulations of a real-world explanatory trial of transarterial radioembolization for advanced unresectable hepatocellular carcinoma with portal vein invasion were performed to examine the effects of cohort specification on survival outcomes and patient sample size. Simulations comprised 24 different permutations of the trial varied on three disease nonspecific eligibility parameters. Treatment and control arms for these emulated trials were drawn from the National Cancer Database and matched by treatment propensity. Target trial emulation served as the causal framework for this analysis, allowing the architecture of a true controlled experiment to address forms of bias routinely encountered in comparative effectiveness studies involving real-world observational data.

Results: Twenty-four propensity score-matched cohorts comprising a wider clinical spectrum of patients than specified by the original target trial were successfully generated using the National Cancer Database. The arms for each of the emulated trials demonstrated exchangeability across all eligibility criteria and other clinical covariates. Significant treatment benefits were associated with only a narrow range of eligibility criteria, indicating that the original target trial was well specified.

Conclusion: The impact of patient selection on treatment outcomes can be studied using target trial emulation. This analytical framework can furthermore serve to leverage existing real-world data to inform the task of cohort specification for a randomized controlled trial, facilitating a more data-driven approach for this important step in clinical trial design.

Citing Articles

When to stop immunotherapy for advanced melanoma: the emulated target trials.

Amiot M, Mortier L, Dalle S, Dereure O, Dalac S, Dutriaux C EClinicalMedicine. 2024; 78:102960.

PMID: 39717261 PMC: 11664069. DOI: 10.1016/j.eclinm.2024.102960.

References
1.
DAgostino Jr R, DAgostino Sr R . Estimating treatment effects using observational data. JAMA. 2007; 297(3):314-6. DOI: 10.1001/jama.297.3.314. View

2.
Kim E, Bruinooge S, Roberts S, Ison G, Lin N, Gore L . Broadening Eligibility Criteria to Make Clinical Trials More Representative: American Society of Clinical Oncology and Friends of Cancer Research Joint Research Statement. J Clin Oncol. 2017; 35(33):3737-3744. PMC: 5692724. DOI: 10.1200/JCO.2017.73.7916. View

3.
Zhang Z, Kim H, Lonjon G, Zhu Y . Balance diagnostics after propensity score matching. Ann Transl Med. 2019; 7(1):16. PMC: 6351359. DOI: 10.21037/atm.2018.12.10. View

4.
Giobbie-Hurder A, Gelber R, Regan M . Challenges of guarantee-time bias. J Clin Oncol. 2013; 31(23):2963-9. PMC: 3732313. DOI: 10.1200/JCO.2013.49.5283. View

5.
Harvey R, Bruinooge S, Chen L, Garrett-Mayer E, Rhodes W, Stepanski E . Impact of Broadening Trial Eligibility Criteria for Patients with Advanced Non-Small Cell Lung Cancer: Real-World Analysis of Select ASCO- Recommendations. Clin Cancer Res. 2021; 27(9):2430-2434. DOI: 10.1158/1078-0432.CCR-20-3857. View