Pattern-based patient selection associated with positive clinical trial results

This new article publication from Acta Pharmaceutica Sinica B discusses the association between using traditional Chinese medicine patterns to select participants and statistically significant primary outcomes in randomized placebo-controlled trials of Chinese herbal formulas.

Traditional Chinese medicine (TCM) pattern differentiation groups patients with the same disease according to their symptoms and signs to guide treatment selection. Whether using these patterns in trial eligibility is associated with reported outcomes remains unclear. The authors of this article analyzed 378 published randomized placebo-controlled trials of Chinese herbal formulas, identified through eight databases and published between January 1, 2020, and October 21, 2025. The trials included 92,691 participants in their primary outcome analyses. Of these trials, 197 (52.1%) used TCM patterns as an inclusion criterion. Statistically significant primary outcomes were reported in 94.9% of trials using patterns, compared with 75.7% of those that did not.

After adjustment for four prespecified trial characteristics, pattern-based inclusion was associated with a 14% higher likelihood of reporting statistically significant primary outcomes (adjusted risk ratio 1.14; 95% confidence interval 1.06-1.23; P = 0.001). The association was generally consistent across prespecified subgroups and sensitivity analyses. However, only 52 trials (13.8%) were assessed as having a low risk of bias, and few published pattern-based trials reported statistically nonsignificant primary outcomes. Publication bias, selective outcome reporting, residual confounding, and differences in research and reporting practices may have influenced the findings. This trial-level association does not establish that pattern-based selection improves treatment efficacy. The research supports further evaluation in prospectively registered, methodologically rigorous trials with standardized pattern assessment and transparent reporting.