The sequential use of alternative treatments for chronic conditions represents a complex intervention; previous treatment, evolving disease, and patient characteristics affect both the choice and effectiveness of subsequent treatments. This thesis develops a new framework for conducting quantitative evidence synthesis of the effectiveness of sequential treatment options within a health technology assessment (HTA) or similar process. It covers methods for developing summary estimates of clinical effectiveness or the clinical inputs for the cost-effectiveness assessment, and can encompass any disease condition. The framework was developed through in-depth evaluation of current approaches using integrated literature reviews.
Key challenges of developing summary effect estimates of interventions conditional on previous treatments were first identified using a HTA of sciatica treatments. Network meta-analyses allowed comparison of multiple treatments, but the limitations of the evidence base, and poor reporting of previous treatments precluded the evaluation of treatment sequences.
A review of NICE guidance identified the type of challenges faced by policy makers and showed that treatment sequencing is pertinent for a wide range of clinical conditions. It also indicated that treatment sequencing was often considered as part of the economic evaluation only, and not the clinical evaluation.
A comprehensive review of quantitative evidence synthesis methods considered:
i. Meta-analytic methods for assessing the clinical effectiveness of treatment sequences
ii. Simplifying assumptions made by decision analytic modelling studies in the absence of an adequate evidence base to inform treatment effect estimates conditional on positioning in the sequence
iii. Decision analytic modelling approaches used to evaluate the effectiveness of treatment sequences
The findings of the review demonstrated that estimating the effectiveness of a sequence of treatments is not straightforward or trivial, and is severely hampered by the limitations of the evidence base. There is no single best way to evaluate treatment sequences, however some approaches could be re-used or adapted, sharing ideas across different disease conditions. Each has advantages and disadvantages, and is influenced by the evidence available, extent of treatment sequences, and complexity in the decision problem. Due to the scarcity of data, modelling studies applied simplifying assumptions to data on discrete treatments. A coding scheme for all possible assumptions was developed, providing a unique resource to aid the critique of existing models.
The thesis illuminates a significant gap in the methods development. It also demonstrates important limitations in the primary studies, which tends to focus on the evaluation of single treatments with poor reporting of any previous or subsequent treatments. The increasing use of network meta-analysis in HTA demonstrates the acknowledgment that clinical and policy decision making needs to account for the multiple treatments available for many chronic conditions. However, the sequential use of these treatments has yet to be accounted for within the clinical evaluation, with most meta-analysis being conducted of single treatments that may or may not be stratified by line of therapy. The economic modelling exposes the need to consider treatment sequences, but this is often based on the simplifying assumption of treatment independence. The use of simplifying assumptions leads to uncertainty and potential bias in estimating the effectiveness and cost effectiveness of treatments, and can lead to the wrong decision.
In summary, there has been no co-ordinated approach to the important issue of evaluating the effectiveness and cost-effectiveness of treatment sequencing. This is a major shortfall at a time when the cohort of people with complex chronic conditions, requiring sequential treatments, is increasing. The findings of the thesis will help policy makers and researchers gain traction in answering questions about the effectiveness of different treatment sequences.