Improving statistical methods to address confounding in the economic evaluation of health interventions

点击翻译按钮获取中文摘要

Policy makers worldwide use health economic evaluation to help decide which health care interventions to provide. For the cost-effectiveness analysis of pharmaceuticals, randomised trials are seen as the "gold standard" source of evidence for economic evaluation, because randomisation ensures that any difference between the health and economic outcomes of the treated and control patients reflects the causal effect of the treatment. In many settings, including the evaluation of new devices and of diagnostic tests, clinical guideline development, and the evaluation of new health policy initiatives, no relevant trial evidence is available. Such economic evaluations need to use non-randomised evidence, for example registry data, or cohort studies. For these studies, randomisation between treatment arms is not ensured any longer. Here, a simple comparison of treatment groups would yield selection bias in the estimated quantity of interest, due to confounding factors, i.e. patient characteristics that make it more likely for a patient to receive one treatment over the other, and also influence the health outcomes and eventual treatment costs. Selection bias due to confounding can be adjusted for if appropriate statistical methods are used. However, as a recent systematic review has demonstrated, in published economic evaluations, the quality of statistical analysis to address this problem is unsatisfactory. Results from such studies can lead to the wrong conclusions on cost-effectiveness, and to health care resources being misallocated. Methods developments for addressing selection bias in economic evaluation so far has been limited to relatively simple settings, such as comparing two treatments, which do not change over time. However, these settings might not characterise more complex evaluations. First, decision makers may require information not just about the cost-effectiveness of a binary treatment, but also about what intensity of treatment to provide. For example, when introducing a new financial incentive, the policy maker may want to know what the optimal level of incentive is. Second, in clinical practice, treatment provided can respond to the patient's characteristics, for example cancer treatment is switched according to tumour progression. Third, many interventions, typically health policy initiatives, are implemented at the level of an institution (e.g. NHS trust) or for an entire geographical region (e.g. health authority), and adjusting for confounding might be challenging due to the lack of an appropriate control group. Currently there is a lack of methodological guidance for these settings. This might lead to the use of inappropriate methods, resulting in severely biased estimates, or worse; completely discourage analysts and decision makers from exploiting non-randomised evidence for economic evaluation. Methods that can address confounding in these settings are at the forefront of developments in the causal inference literature, however these methods have yet to be translated, and potentially extended to the setting of economic evaluation. I propose to conduct a comprehensive research programme to address this gap in knowledge, using both simulation work and data from clinical and policy areas of high relevance. The research will assess and if necessary, extend alternative methods from the causal inference literature for addressing confounding in economic evaluation. This research will enable me to provide recommendations on which methods are appropriate in an economic evaluation setting, towards applied researchers and decision makers. By thorough dissemination of the methods, this research aims to improve the quality of statistical analysis in economic evaluation, leading to more accurate cost-effectiveness results, and a stronger evidence for decision making. This research will therefore help ensure that scarce resources are allocated in the best ways for improving population health in the UK.