Individual patient data meta-analysis of trials of self-monitoring of blood glucose in non-insulin treated type 2 diabetes: protocol for a systematic review. - Farmer, A; Heneghan, C; Barnett, A; Davidson, M; Guerci, B; O'Kane, M; Schwedes, U; Perera, R (2009)
BACKGROUND AND AIM OF THE STUDY: Recent trials have provided differing estimates of the benefits of self-monitoring of blood glucose (SMBG) for non-insulin treated patients with type 2 diabetes. Designing and conducting randomised trials to demonstrate the efficacy of complex interventions is challenging. Variations in the components of the intervention delivered, target population selected, and reporting methods used have limited the conclusions drawn in recent systematic reviews. We will systematically examine these factors within the trials of SMBG to enable further investigation of the data through an individual patient data analysis (IPD). METHODS/DESIGN: The IPD analysis will include data from randomised trials comparing blood glucose self-monitoring and decisions about self-management versus a control group with standardised or routine care. The data requested for each trial will include outcomes (HbA1c, blood glucose and quality of life), potential moderators of effect (e.g. demographic variables, clinical data and psychosocial factors) and intervention descriptors. The primary outcome of interest will be HbA1c. Secondary outcomes include alternative measures of glycaemia, cardiovascular risk factors, persistence with monitoring, and measures of health status, quality of life and psychosocial factors. Analysis of patient sub-groups defined by age at randomisation, gender, prior use of monitoring and health status will be carried out. An intention to treat analysis will be performed and assessed for clinical and statistical heterogeneity. DISCUSSION: With current uncertainty about the extent of benefit from SMBG for non-insulin treated patients with type 2 diabetes, this study will provide the best estimates to date of overall effectiveness, effectiveness within potential target populations, and optimal components of the intervention.