The analysis of clustered data in public health and healthcare research

Clustered data arise when data are sampled from clusters of individuals randomized to different interventions in a cluster randomised trial (CRT) or from groups of individuals in a population such as primary sampling units in a multistage clustered survey. Examples include: trials to compare different approaches for managing the wellbeing of elderly people clustered by general practices in the community; surveys on family planning or access to clean water where respondents are geographically clustered; and cost effectiveness studies conducted alongside CRTs.1-3 Although individual randomised trials and simple random sampling are statistically more efficient, CRTs and complex survey designs are pragmatic, and sometimes necessary alternatives in the presence of logistical, financial, or ethical constraints, or a risk of intervention contamination across different arms in a clinical trial. However, the dependence of data from the same cluster violates the critical independence assumption on which most standard statistical methods rely. Such dependence must be accounted for appropriately to enable valid inference. In this thesis of published work, I draw upon seven publications to highlight the challenges in analysing clustered data, demonstrate the application of a variety of appropriate statistical methods, and show my contribution to developing statistical methodologies for clustered data. They are (i and ii) the analysis of a large CRT, (iii) estimation of intraclass correlation coefficients, [i-iii use data from the same CRT in geriatric research in the UK], (iv) the cost effectiveness analysis (CEA) of a CRT of a non-clinical intervention to reduce caesarean rate, and (v to vii) three pieces of methodological work to improve and extend current methods and models for analysing clustered data