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Review
. 2025 May 10;15(5):195.
doi: 10.3390/jpm15050195.

Methodologies for the Emulation of Biomarker-Guided Trials Using Observational Data: A Systematic Review

Faye D Baldwin  1 , Rukun K S Khalaf  2 , Ruwanthi Kolamunnage-Dona  1 , Andrea L Jorgensen  1
Affiliations
Review

Methodologies for the Emulation of Biomarker-Guided Trials Using Observational Data: A Systematic Review

Faye D Baldwin et al. J Pers Med. .

Abstract

Background: Target trial emulation involves the application of design principles from randomised controlled trials (RCTs) to observational data, and is particularly useful in situations where an RCT would be unfeasible. Biomarker-guided trials, which incorporate biomarkers within their design to either guide treatment and/or determine eligibility, are often unfeasible in practice due to sample size requirements or ethical concerns. Here, we undertake a systematic review of methodologies used in target trial emulations, comparing treatment effectiveness, critically appraising them, and considering their applicability to the emulation of biomarker-guided trials. Methods: A comprehensive search strategy was developed to identify studies reporting on methods for target trial emulation comparing the effectiveness of treatments using observational data, and applied to the following bibliographic databases: PubMed, Scopus, Web of Science, and Ovid MEDLINE. A narrative description of methods identified in the review was undertaken alongside a critique of their relative strengths and limitations. Results: We identified a total of 59 papers: 47 emulating a target trial ('application' studies), and 12 detailing methods to emulate a target trial ('methods' studies). A total of 25 papers were identified as emulating a biomarker-guided trial (42%). While all papers reported methods to adjust for baseline confounding, 40% of application papers did not specify methods to adjust for time-varying confounding. Conclusions: This systematic review has identified a range of methods used to control for baseline, time-varying, and residual/unmeasured confounding within target trial emulation and provides a guide for researchers interested in emulation of biomarker-guided trials.

Keywords: biomarker-guided trials; personalised medicine; target trial emulation.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
PRISMA flow chart.
Figure 2
Figure 2
Directed acyclic graph (DAG) depicting the hypothesised causal relationships between statin use (exposure) and cardiovascular disease (outcome), accounting for time-varying statin use (at baseline, T0 and follow-up, T1) alongside time-varying variables LDL cholesterol and smoking status, and age and sex as baseline confounders.
See this image and copyright information in PMC

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