title: Baseline hospital performance and the impact of medical emergency teams: modelling vs. conventional subgroup analysis.
abstract: BACKGROUND
  To compare two approaches to the statistical analysis of the relationship between the baseline incidence of adverse events and the effect of medical emergency teams (METs).
  
  
  METHODS
  Using data from a cluster randomized controlled trial (the MERIT study), we analysed the relationship between the baseline incidence of adverse events and its change from baseline to the MET activation phase using quadratic modelling techniques. We compared the findings with those obtained with conventional subgroup analysis.
  
  
  RESULTS
  Using linear and quadratic modelling techniques, we found that each unit increase in the baseline incidence of adverse events in MET hospitals was associated with a 0.59 unit subsequent reduction in adverse events (95%CI: 0.33 to 0.86) after MET implementation and activation. This applied to cardiac arrests (0.74; 95%CI: 0.52 to 0.95), unplanned ICU admissions (0.56; 95%CI: 0.26 to 0.85) and unexpected deaths (0.68; 95%CI: 0.45 to 0.90). Control hospitals showed a similar reduction only for cardiac arrests (0.95; 95%CI: 0.56 to 1.32). Comparison using conventional subgroup analysis, on the other hand, detected no significant difference between MET and control hospitals.
  
  
  CONCLUSIONS
  Our study showed that, in the MERIT study, when there was dependence of treatment effect on baseline performance, an approach based on regression modelling helped illustrate the nature and magnitude of such dependence while sub-group analysis did not. The ability to assess the nature and magnitude of such dependence may have policy implications. Regression technique may thus prove useful in analysing data when there is a conditional treatment effect.
authors.affiliation: The Simpson Centre for Health Services Research, University of New South Wales, Sydney, New South Wales, Australia. jackchen@unsw.edu.au
authors.forename: Jack
authors.initials: J
authors.lastname: Chen
authors.forename: Arthas
authors.initials: A
authors.lastname: Flabouris
authors.forename: Rinaldo
authors.initials: R
authors.lastname: Bellomo
authors.forename: Ken
authors.initials: K
authors.lastname: Hillman
authors.forename: Simon
authors.initials: S
authors.lastname: Finfer
mesh_terms: D016887:Cardiopulmonary Resuscitation / Q000009:adverse effects* / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006323:Heart Arrest / Q000401:mortality* / Q000628:therapy*; D017052:Hospital Mortality; D006801:Humans; D015994:Incidence; D007362:Intensive Care Units / Q000706:statistics & numerical data; D016014:Linear Models; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*