Consistency checks to improve measurement with the Hamilton Rating Scale for Anxiety (HAM-A)

Background: Mitigating rating inconsistency can improve measurement fidelity and detection of treatment response. Methods: The International Society for CNS Clinical Trials and Methodology convened an expert Working Group that developed consistency checks for ratings of the Hamilton Anxiety Rating Scale (HAM-A) and Clinical Global Impression of Severity of anxiety (CGI-S) that are widely used in studies of mood and anxiety disorders. Flags were applied to 40,349 HAM-A administrations from 15 clinical trials and to Monte Carlo-simulated data as a proxy for applying flags under conditions of inconsistency. Results: Thirty-three flags were derived these included logical consistency checks and statistical outlier-response pattern checks. Twenty-percent of the HAM-A administrations had at least one logical scoring inconsistency flag, 4 % had two or more. Twenty-six percent of the administrations had at least one statistical outlier flag and 11 % had two or more. Overall, 35 % of administrations had at least one flag of any type, 19 % had one and 16 % had 2 or more. Most of administrations in the Monte Carlo- simulated data raised multiple flags. Limitations: Flagged ratings may represent less-common presentations of administrations done correctly. Conclusions-Application of flags to clinical ratings may aid in detecting imprecise measurement. Flags can be used for monitoring of raters during an ongoing trial and as part of post-trial evaluation. Appling flags may improve reliability and validity of trial data.

Automated summary

The International Society for CNS Clinical Trials and Methodology developed consistency checks for the widely used Hamilton Anxiety Rating Scale (HAM-A) and Clinical Global Impression of Severity of anxiety (CGI-S). The checks revealed that 35% of ratings had at least one flag of inconsistency, with 19% having one flag and 16% having two or more. Applying flags to clinical ratings can aid in detecting imprecise measurement and improve the reliability and validity of trial data.