Methods Missing-ness in the primary outcome (BMI) was explored in relation to all baseline demographic and post-randomisation variables using logistic regression. An imputation model was developed with cluster (study site) included as a factor together with significant predictors of missing outcome, variables in the primary analysis model, variables used to balance the randomisation and the out...

Background: Under-5 pneumonia mortality remains high in low-income countries. In 2014 the World Health Organization (WHO) advised that children with chest indrawing pneumonia, but without danger signs or peripheral oxygen saturation (SpO2) &lt; 90% be treated community, rather than hospitalized. Malawi there is limited pulse oximetry availability.<...

In randomised trials with missing data, it is not uncommon for the observation of the outcome to depend on the outcome itself. For example in behavioural trials on smoking cessation, weight loss, or alcohol reduction, unsuccessful participants may be less willing to disclose their outcome than those that are more successful. These Missing Not At Random (MNAR) data are problematic because they c...

The issue of missing data is present in every trial. Identification of the missing data mechanism is not straightforward; however it is essential to avoid bias. The mechanism of missing completely at random (MCAR) is seldom appropriate and the distinction between missing at random (MAR) and not at random (MNAR) is not straightforward. Follow-up reminder responses provide excellent source of inf...

PURPOSE Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA...