Abstract
Many complex healthcare interventions aim to change the behaviour of
patients or health professionals, e.g. stopping smoking or prescribing
fewer antibiotics. This prompts the question of which behaviour change
interventions are most effective. Synthesising evidence on the
effectiveness of a particular type of behaviour change intervention can
be challenging because of the high levels of heterogeneity in trial
design. Here we use data from a published systematic review as a case
study and compare alternative methods to address this heterogeneity. One
important sources of heterogeneity is that compliance to a desired
behaviour can be measured and reported in a variety of different ways.
In addition, interventions designed to target behaviour can be
implemented at either an individual or group level leading to trials
with varying layers of clustering.
To handle heterogeneous outcomes we can either convert all effect
estimates to a common scale (e.g. using standardised mean differences)
or have separate meta-analyses for different types of outcome measure
(binary and continuous measures).To address the clustering structure,
adjusted standard errors can be used with the inverse variance method,
or weights can be assigned based on a consistent level of clustering,
such as the number of healthcare professionals. A graphical method, the
albatross plot utilises reported p-values only, and can synthesise data
with both heterogeneous outcomes and clustering with minimal assumption
and data manipulation.
Based on these methods, we reanalysed our data in four different ways
and have discussed the strengths and weaknesses of each approach.