HISTORICAL TRIAL DATA • REAL-WORLD EVIDENCE

ADAPTIVE WEIGHTING • INFORMATION BORROWING

EXTERNAL COMPARATORS • SINGLE-ARM TRIALS

COHORT VALIDATION • STUDY-SPECIFIC ANALYSIS

Some studies cannot easily include a conventional control arm. An external comparator built from relevant historical and real-world patient data can provide context for interpreting outcomes, with its suitability assessed for each study.

DyNaSynth™ combines relevant historical trial and real-world patient data. Adaptive weighting and empirical Bayesian information borrowing help align the comparator with the treatment population while accounting for differences among data sources. Each cohort and analysis is tailored to the study.

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Combine curated historical trial records and real-world data relevant to the treatment population and outcome. Source quality and comparability guide what enters the analysis.

📊

Adaptive weighting and information borrowing construct a synthetic comparison group aligned to the study population. The method accounts for differences among source datasets.

⚕️

A study-specific external comparator can support interpretation when a randomized control arm is difficult to recruit or use. Appropriateness depends on the clinical question, available data, and design.

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Assess baseline balance, data quality, and sensitivity to design choices. Document assumptions and limitations so the comparison can be interpreted responsibly.

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Define endpoints, covariates, and statistical analyses around the study objectives. Use sensitivity analyses to examine how conclusions change under reasonable assumptions.

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Discuss the proposed comparator strategy early in development. The evidence package should make data provenance, cohort selection, methods, and limitations clear for reviewers.