Presentation (CTI)

OBJECTIVES: To propose a conceptual framework positioning the translation quality of patient-facing instruments as a measurable determinant of data validity in multinational health economics and outcomes research (HEOR), and to identify where current practice leaves this factor unaddressed.
METHODS: This conceptual paper synthesises established concepts from cross-cultural psychometrics, outcomes research, and translation studies. Multinational trials and observational studies increasingly underpin HEOR evidence, drawing on patient-reported outcomes (PROs), utility instruments, and patient diaries administered across languages. While linguistic validation procedures exist, they are frequently treated as a one-time compliance checkpoint rather than a continuous determinant of measurement equivalence. The framework articulates three dimensions through which translation quality affects downstream data: semantic fidelity (accuracy of meaning), conceptual equivalence (comparability of the underlying construct across cultures), and response equivalence (consistency of interpretation and answering across languages).
RESULTS: Each dimension is linked to specific HEOR risks. Weak semantic fidelity introduces measurement error; limited conceptual equivalence undermines cross-country comparability; and poor response equivalence biases pooled estimates. Together, these can distort utility values, produce non-comparable cross-country PRO scores, and compromise cost-effectiveness inputs, often without detection, because translation quality is rarely treated as an analyzable variable.
CONCLUSIONS: Recognizing translation quality as a validity determinant rather than a procedural step has practical consequences. Researchers can incorporate translation-quality indicators into study design and sensitivity analyses; HTA reviewers can scrutinize linguistic equivalence when appraising multinational evidence; and sponsors can treat translation quality as protection of data integrity rather than a fixed cost. The framework offers a shared vocabulary for this risk and a foundation for empirical work quantifying translation-driven variance in HEOR outcomes.