Statistical methods for cost-effectiveness analysis that use cluster-randomised trials

This thesis considers alternative statistical methods for cost-effectiveness analysis (CEA) that use cluster randomised trials (CRTs). The thesis has four objectives: firstly to develop criteria for identifying appropriate methods for CEA that use CRTs; secondly to critically appraise the methods used in applied CEAs that use CRTs; thirdly to assess the performance of alternative methods for CEA that use CRTs in settings where baseline covariates are balanced; fourthly to compare statistical methods that adjust for systematic covariate imbalance in CEA that use CRTs. The thesis developed a checklist to assess the methodological quality of published CEAs that use CRTs. This checklist was informed by a conceptual review of statistical methods, and applied in a systematic literature review of published CEAs that use CRTs. The review found that most studies adopted statistical methods that ignored clustering or correlation between costs and health outcomes. A simulation study was conducted to assess the performance of alternative methods for CEA that use CRTs across different circumstances where baseline covariates are balanced. This study considered: seemingly unrelated regression (SUR) and generalised estimating equations (GEEs), both with a robust standard error; multilevel models (MLMs) and a non-parametric 'two-stage' bootstrap (TS8). Performance was reported as, for example, bias and confidence interval (Cl) coverage of the incremental net benefit. The MLMs and the TSB performed well across all settings; SUR and GEEs reported poor Cl coverage in CRTs with few clusters. The thesis compared methods for CEA that use CRTs when there are systematic differences in baseline covariates between the treatment groups. In a case study and further simulations, the thesis considered SUR, MLMs, and TSB combined with SUR to adjust for covariate imbalance. The case-study showed that cost-effectiveness results can differ according to adjustment method. The simulations reported that MLMs performed well across all settings, and unlike the other methods, provided Cl coverage close to nominal levels, even with few clusters and unequal cluster sizes. The thesis concludes that MLMs are the most appropriate method across the circumstances considered. This thesis presents methods for improving the quality ofCEA that use CRTs, to help future studies provide a sound basis for policy making.