Title: Cost-effectiveness analysis with informative missing data: tools and strategies
Funder: Department of Health and Social Care

Abstract: Cost effectiveness analyses (CEA) alongside randomised controlled trials are an important source of information for health commissioners to decide on how to best allocate limited resources. Randomised trials are considered as gold standard as they can estimate causal effect of an intervention. This is however assuming data on effectiveness and cost are available for all patients randomised into the trial, which is rarely the case in practice. In order to analyse the resulting partially observed data, we need to make assumptions about the missing data. Since these assumptions can typically not be tested, we need an accessible framework to explore the robustness of our conclusions to various plausible assumptions. A single nave analysis will not only typically be inefficient, it may be markedly biased. Methods commonly used in CEA simply assume missing data are uninformative (or Missing At Random (MAR)), i.e. given the observed data, the missingness is independent of the unobserved data values. However in many situations this assumption seems unrealistic. For example in a recent trial of a brief intervention for weight loss in primary care attendees, only 66% of randomised participants were still the study at the end of the 2-year assessment. It seems very likely that those who did not complete the trial differ from those who did, for example having lesser interest in the study, or not having managed to maintain the weight loss they were hoping for. It is therefore important to assess the implications of a range of plausible missingness assumptions beyond MAR, on cost effectiveness inferences. Informative missingness (or Missing Not at Random) analysis is an area of rapid development in medical statistics, but this form of uncertainty has not yet been addressed in health economics. This PhD project aims to develop a practical and accessible framework to perform trial-based cost-effectiveness analysis under informative missingness assumption. In the first part, I will organise discussions with various stakeholders (health economists, clinical experts, policy analyst, etc.) in order to clarify the practical issues around informative missingness analysis, and the barriers to its systematic use. In the light of this, the second part will identify a framework and relevant methods for informative missingness analysis in cost-effectiveness evaluation, and address statistical and practical issues associated with these methods. I will then illustrate the framework in two trials of healthy lifestyle intervention where informative missingness issues are important. The final part of my PhD will look into a more complex approach for longitudinal data, where the statistical distribution of the outcome values after drop out are constructed as a pre-specified function of observed (or external) data. Throughout, emphasis will be put on software and dissemination to maximise the impact of the work on cost-effectiveness analysis. The research will result in a framework for a more systematic, transparent and hence dependable evaluation of cost-effectiveness when trial data are incomplete. By improving the quality of cost-effectiveness analysis, which informs decisions on the provision of health interventions, this research will help ensure that scarce societal resources are allocated in the best ways to improve patients' health.

Lay abstract: Cost-effectiveness analysis concerns the evaluation of the costs and the benefits of new health interventions, such as drugs and public health programmes. Information on the value for money of new interventions is essential for the UK National Health Service (NHS), to ensure it maximises the benefits it can provide to patients for a given NHS budget. Randomised trials, where a random sample of patients are offered a new treatment and compared to a control group, are commonly used to evaluate cost-effectiveness. However, many trials have a non-trivial proportion of missing data, so that some of the information on costs or effectiveness is missing, for example because participants withdraw from the study, or are not able to complete a questionnaire. A major concern is if the missing data are directly linked to the value of data, a phenomenon referred to as informative missingness. For example, in weight loss trials, it is acknowledged that participants who did not complete the study assessments may well be those less successful in achieving and maintaining weight loss. While informative missingness is common, it is rarely considered in cost-effectiveness analysis, and the issue has not yet been properly addressed. The aim of my research project is to develop an accessible, practical framework to perform cost-effectiveness analysis with informative missingness data. In the first part of my PhD, I will engage in discussions with key professionals involved in health economics evaluation, in order to understand their needs and the barriers to the implementation of informative missingness analysis. In the light of this, in the second part I will evaluate different relevant methods to perform such analyses, and resolve issues related to the application of these methods in a cost-effectiveness context. I will apply the proposed methods to several examples. My principal example is Ten Top Tips (10TT), a trial evaluating a programme to help obese primary care patients lose weight, where one third of the participants withdrew before the end of the study. Missing data in this trial are likely to be informative, and the cost-effectiveness analysis could result in misleading conclusions if this is ignored.In the last part of my PhD, I will look into a more advanced method to address informative missingness in the case of studies with repeated measurements over time. The methods developed will be made as accessible as possible, and I will share my findings through scientific articles, software provision and attending conferences, in order to maximize the impact of my work. My proposed research will improve current practice in this important area, by providing a framework for improved cost-effectiveness decision-making when faced with incomplete cost-effectiveness data from studies. In turn, this will help ensure scarce resources are allocated in the best ways for improving patients' health.

Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE