Statistical significance plays an important role in the interpretation of clinical trial results. However, on the basis of obtaining statistical significance, the assessment of clinical significance is often neglected. This study attempted to propose a simple and unambiguous new classification method for study results, focusing on studies with statistical positive findings to evaluate whether the results have clinical significance.

Our study subjects were the clinical studies in 2019 ACC and ESC annual meetings. Meta-epidemiology methods were used to extract the characteristic variable from each study. The primary evaluation indicators included target effect-size and observed effect-size. Based on the difference between the two indicators, the studies that had statistical significance were subdivided to identify studies with possible insufficient clinical significance; Furthermore, the theoretical threshold based on power analysis was proposed, which was used as the basis for the interpretation of study results.

There were 12 clinical studies included in the final analysis. All of them were published on top journals. Those studies had relative high quality on both study design and reporting. The correlation coefficient between the observed and target effect-size was 0.892. Among the 7 studies with statistical significance, two of them were classified as insufficient clinical significance. The counts was 1 (1/3) and 1 (1/4) for the studies reported in ACC and ESC respectively.

The achievement of clinical significance is critical even in the study with positive results. This paper proposes a new classification standard that combines clinical significance with statistical significance and further suggests a method to evaluate the reliability of clinical study results in order to assist researchers in identifying potential risks caused by insufficient clinical significance, and provide some reference and help for the reasonable interpretation of clinical study results.

Target effect-size; Observed effect-size; Meta-epidemiology; Publication bias among positive study