A practical approach to a multi-level analysis with a sparse binary outcome within a large surgical trial.
OBJECTIVE
To assess the potential source of variation that surgeon may add to patient outcome in a clinical trial of surgical procedures.


METHODS
Two large (n = 1380) parallel multicentre randomized surgical trials were undertaken to compare laparoscopically assisted hysterectomy with conventional methods of abdominal and vaginal hysterectomy; involving 43 surgeons. The primary end point of the trial was the occurrence of at least one major complication. Patients were nested within surgeons giving the data set a hierarchical structure. A total of 10% of patients had at least one major complication, that is, a sparse binary outcome variable. A linear mixed logistic regression model (with logit link function) was used to model the probability of a major complication, with surgeon fitted as a random effect. Models were fitted using the method of maximum likelihood in SAS.


RESULTS
There were many convergence problems. These were resolved using a variety of approaches including; treating all effects as fixed for the initial model building; modelling the variance of a parameter on a logarithmic scale and centering of continuous covariates. The initial model building process indicated no significant 'type of operation' across surgeon interaction effect in either trial, the 'type of operation' term was highly significant in the abdominal trial, and the 'surgeon' term was not significant in either trial.


CONCLUSIONS
The analysis did not find a surgeon effect but it is difficult to conclude that there was not a difference between surgeons. The statistical test may have lacked sufficient power, the variance estimates were small with large standard errors, indicating that the precision of the variance estimates may be questionable.
Fountain
Jayne
J
CTRU, University of Leeds, UK. medjmf@leeds.ac.uk
Gallagher
James
J
Brown
Julia
J
D006801:Humans; D013514:Surgical Procedures, Operative; D006113:United Kingdom