Br J Anaesth. 2025 Dec 2:S0007-0912(25)00725-1. Revista: 10.1016/j.bja.2025.09.054. Online ahead of print.

BACKGROUND: Studies in anaesthesiology frequently use generalised linear models to identify quantitatively important ‘independent predictors’ of log-normally distributed outcomes, such as surgical and anaesthesia times. However, the performance of common multiple-comparison procedures at preventing type I and II errors is unknown for these problems.

METHODS: We conducted Monte Carlo simulations to evaluate methods for controlling the familywise error rate (FWER) and false discovery rate (FDR). Simulated datasets had log-normal outcomes and three binary predictors, with varying correlation among them (independent, strong positive, or moderate negative). We applied four FWER (Bonferroni, Šidák, Holm-Bonferroni, and Hochberg) and two FDR (Benjamini-Hochberg and Benjamini-Yekutieli) procedures to the P-values derived from the generalised linear models.

RESULTS: Without adjustment for multiple comparisons, the FWER was large (12.6-14.8% instead of the correct [nominal] 5.0%). Among FWER methods, the Bonferroni adjustment was the most accurate, with rates consistently close to the nominal 5.0% level across all correlation scenarios (5.2-5.3%). For FDR control, the Benjamini-Yekutieli procedure was effective for independent and negatively correlated predictors (4.5-5.1%) but failed to control the FDR under strong positive predictor correlation (6.0-9.5%).

CONCLUSIONS: When using generalised linear models to identify predictors of log-normal outcomes, the simplest approach, Bonferroni adjustment, provided reliable control of the FWER. The Benjamini-Yekutieli procedure is the most suitable for controlling the FDR, but our findings show it can be anti-conservative (i.e. unreliable) when potential predictors of the anaesthesia times are positively correlated (i.e. precisely the conditions that would generally hold for these problems).

PubMed:41339172 | Revista:10.1016/j.bja.2025.09.054