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. 2025 May;14(5):e240235.
doi: 10.57264/cer-2024-0235. Epub 2025 Apr 7.

A process to validate prognostic factors for unanchored matching-adjusted indirect comparison of single-arm trials in oncology: a proof-of-concept study

Yao Yi  1 , Yawen Jiang  2
Affiliations

A process to validate prognostic factors for unanchored matching-adjusted indirect comparison of single-arm trials in oncology: a proof-of-concept study

Yao Yi et al. J Comp Eff Res. 2025 May.

Abstract

Aim: The choice of covariates in unanchored matching-adjusted indirect comparisons (MAICs) of single-arm cancer trials with time-to-event outcomes remains a challenge. Currently, there is a lack of a systematic approach for validating the selection of covariates for bias reduction in unanchored MAIC. Materials & methods: This study proposes a validation framework to evaluate the appropriateness of selected prognostic factors before their use in unanchored MAIC. The process involves identifying potential prognostic factors from individual patient data and calculating risk scores using the prognostic factors with regression; artificially creating two groups that are unbalanced in risk such that a predetermined hazard ratio (HR) between the two groups is achieved; creating weights based on the prognostic factors; running a re-weighted Cox regression to assess the HR, the value of which should suggest balanced risks across groups to indicate the sufficiency of prognostic factors being included. We also conducted a proof-of-concept analysis using a simulated dataset to showcase this process. Results: The process successfully stratified the sample into two risk groups with a pre-determined HR of 1.8. When all covariates were included in the weighting, the HR was 0.9157 (95% CI: 0.5629-2.493), which was close to one. When one of the critical prognostic factors was omitted from the covariates, the HR became 1.671 (95% CI: 1.194-2.340), which was significantly different from one. Conclusion: Filling a gap in the existing evidence synthesis literature, the study introduces a structured data-driven approach for covariate prioritization in unanchored MAIC. The process may be a useful tool for quantitative covariate selection.

Keywords: covariates; indirect comparison; matching; single-arm; survival.

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Conflict of interest statement

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript.

Figures

Figure 1.
Figure 1.. Distribution of bias (%) in hazard ratio estimates from bootstrap samples.
Comparison between models using all covariates versus omitting age.
Figure 2.
Figure 2.. Kernel density plots of individual weights.
Comparison between models using all covariates versus omitting age.
See this image and copyright information in PMC

References

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