Creating groups with similar expected behavioural response in randomized controlled trials: a fuzzy cognitive map approach

BMC Medical Research Methodology

Rik Crutzen²  Philippe J Giabbanelli¹ 

[1]UKCRC Centre for Diet and Activity Research, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK

[2]Department of Health Promotion, Maastricht University/CAPHRI, Maastricht, The Netherlands

关键词: Randomization;    Computational model;    Artificial intelligence;    Allocation method;   

Others  :  1090337
DOI  :  10.1186/1471-2288-14-130

 received in 2014-03-20, accepted in 2014-12-08,  发布年份 2014

【 摘 要 】

Background

Controlling bias is key to successful randomized controlled trials for behaviour change. Bias can be generated at multiple points during a study, for example, when participants are allocated to different groups. Several methods of allocations exist to randomly distribute participants over the groups such that their prognostic factors (e.g., socio-demographic variables) are similar, in an effort to keep participants’ outcomes comparable at baseline. Since it is challenging to create such groups when all prognostic factors are taken together, these factors are often balanced in isolation or only the ones deemed most relevant are balanced. However, the complex interactions among prognostic factors may lead to a poor estimate of behaviour, causing unbalanced groups at baseline, which may introduce accidental bias.

Methods

We present a novel computational approach for allocating participants to different groups. Our approach automatically uses participants’ experiences to model (the interactions among) their prognostic factors and infer how their behaviour is expected to change under a given intervention. Participants are then allocated based on their inferred behaviour rather than on selected prognostic factors.

Results

In order to assess the potential of our approach, we collected two datasets regarding the behaviour of participants (n = 430 and n = 187). The potential of the approach on larger sample sizes was examined using synthetic data. All three datasets highlighted that our approach could lead to groups with similar expected behavioural changes.

Conclusions

The computational approach proposed here can complement existing statistical approaches when behaviours involve numerous complex relationships, and quantitative data is not readily available to model these relationships. The software implementing our approach and commonly used alternatives is provided at no charge to assist practitioners in the design of their own studies and to compare participants' allocations.

【 授权许可】

   
2014 Giabbanelli and Crutzen; licensee BioMed Central Ltd.

【 图 表 】

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