A practical approach to a multi-level analysis with a sparse binary outcome within a large surgical trial.

Area of Science:

• Surgical outcomes research
• Clinical trial methodology
• Statistical analysis in healthcare

Background:

• Surgeon variability can influence patient outcomes in clinical trials.
• Understanding this variation is crucial for trial design and interpretation.
• Hysterectomy procedures offer a context to examine surgeon-specific effects.

Purpose of the Study:

• To evaluate the extent to which surgeons contribute to variations in patient outcomes.
• To assess surgeon effect in randomized controlled trials comparing hysterectomy methods.
• To identify potential sources of heterogeneity in surgical trial results.

Main Methods:

• Two large, parallel, multicenter randomized surgical trials comparing hysterectomy types.
• Involved 43 surgeons and 1380 patients, with major complications as the primary endpoint.
• Utilized a linear mixed logistic regression model with surgeon as a random effect to analyze hierarchical data.

Main Results:

• Convergence issues were encountered and resolved through various modeling techniques.
• No significant 'type of operation' by surgeon interaction effect was found.
• The 'type of operation' was significant in the abdominal trial, but the 'surgeon' term was not significant in either trial.

Conclusions:

• The statistical analysis did not detect a significant surgeon effect on patient outcomes.
• However, the study's power may have been insufficient to definitively rule out surgeon differences.
• Small variance estimates with large standard errors indicate uncertainty in the precision of these estimates.