A lesson learnt: the importance of modelling in randomized controlled trials for complex interventions in primary care.
BACKGROUND
The Randomised Controlled Trial (RCT) is recognised as the 'gold standard' in quantitative research. However RCTs testing health care interventions can be difficult to design and implement. Health care interventions are often complex in themselves and are always applied in complex settings. Such interventions require a process of careful 'modelling' to maximize the chances of successful trials that will add to knowledge.


OBJECTIVE
To describe the terms 'complex' and 'modelling' as used in the setting of randomised controlled trials of complex interventions. To give a practical example of an RCT involving a complex intervention applied in a health care setting to illustrate how this might take place in practice.


METHODS
We describe an RCT designed and conducted by the authors. We then use our trial as an example to illustrate how complex interventions such as ours might benefit from modelling during the design of the intervention and the setting within which the intervention is to be tested.


RESULTS
Our project was designed and tested before current guidance on complex interventions was published; our RCT was therefore not 'modelled' but was based on the outcome of a single quantitative pilot study. As part of our study we ran a parallel qualitative study, which highlighted several areas of complexity both in our intervention, and in the setting within which we applied it. In this paper we show how modelling might have allowed us to recognise these complexities at an early stage and might therefore have resulted in a study more likely to have demonstrated useful outcomes.


CONCLUSIONS
Careful modelling of complex interventions is an essential step in designing trials of innovations in health care and health care services. Such a process ensures that interventions fit with and reflect the complexities of the settings within which interventions will be applied, and should ensure that the outcomes chosen are those most appropriate to demonstrate any benefits or risks.
Rowlands
Gill
G
Department of General Practice, The University of Melbourne, 200 Berkeley Street, Carlton, Victoria 3053, Australia. growland@sghms.ac.uk
Sims
Jane
J
Kerry
Sally
S
D000465:Algorithms; D006801:Humans; D017410:Practice Guidelines as Topic; D011320:Primary Health Care; D016032:Randomized Controlled Trials as Topic / Q000379:methods*