Distributional cost effectiveness analysis of West Yorkshire low emission zone policies

Griffin, Susan; Walker, Simon; Sculpher, Mark

doi:10.1002/hec.4003

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…Recent examples, looking at policies for alcohol use disorder and air pollution policies, have shown how the impacts on multiple outcomes can be estimated, and different value judgments in their aggregation considered. 13,[49][50][51] What Decision Are We Trying to Inform?…”

Section: What Outcomes To Capture and How Should They Be Valued? Defi...mentioning

confidence: 99%

Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making

et al. 2021

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Background Policy evaluations often focus on ex post estimation of causal effects on short-term surrogate outcomes. The value of such information is limited for decision making, as the failure to reflect policy-relevant outcomes and disregard for opportunity costs prohibits the assessment of value for money. Further, these evaluations do not always consider all relevant evidence, other courses of action, or decision uncertainty. Methods In this article, we explore how policy evaluation could better meet the needs of decision making. We begin by defining the evidence required to inform decision making. We then conduct a literature review of challenges in evaluating policies. Finally, we highlight potential methods available to help address these challenges. Results The evidence required to inform decision making includes the impacts on the policy-relevant outcomes, the costs and associated opportunity costs, and the consequences of uncertainty. Challenges in evaluating health policies are described using 8 categories: 1) valuation space; 2) comparators; 3) time of evaluation; 4) mechanisms of action; 5) effects; 6) resources, constraints, and opportunity costs; 7) fidelity, adaptation, and level of implementation; and 8) generalizability and external validity. Methods from a broad set of disciplines are available to improve policy evaluation, relating to causal inference, decision-analytic modeling, theory of change, realist evaluation, and structured expert elicitation. Limitations The targeted review may not identify all possible challenges, and the methods covered are not exhaustive. Conclusions Evaluations should provide appropriate evidence to inform decision making. There are challenges in evaluating policies, but methods from multiple disciplines are available to address these challenges. Implications Evaluators need to carefully consider the decision being informed, the necessary evidence to inform it, and the appropriate methods. [Box: see text]

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Section: What Outcomes To Capture and How Should They Be Valued? Defi...mentioning

confidence: 99%

Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making

et al. 2021

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“…Unlike the abovementioned limitation of a network meta-regression model with a shared interaction-effect parameter for the IPD studies and aggregate-level studies required to "transfer" information from studies with IPD to studies involving other comparisons for which there is only aggregate-level data, ML-NMR avoids such aggregation or ecological bias. A simple ML-NMR model for dichotomous patient-related effect-modifier can be described as follows: (8) The part of the model relevant for IPD studies is the same as used in model 5 with the exception that the coefficient for the prognostic effect of the covariate, 𝛽 0 , is fixed across studies. networks of any size andimportant for decision-makingproducing estimates in any target population of interest [41].…”

Section: Multilevel Network Meta-regression With Participant-level Data and Aggregate-level Datamentioning

confidence: 99%

“…To date, however, full distributional DCEAs have been performed for a few public health interventions, and not for new drugs [5][6][7][8][9][10]. The question is whether a DCEA of a new intervention can provide meaningful results given the relative sparseness of the available evidence at the time of market introduction.…”

Section: Introductionmentioning

confidence: 99%

Health Equity Impact Evaluation of New Treatments – Evidence Synthesis Methods to Overcome Data Gaps

Jansen

2021

Preprint

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Background: Distributional cost-effectiveness analysis (DCEA) has been introduced as an extension of conventional cost-effectiveness analysis to quantify health equity impacts. Although health disparities are recognized as an important concern, the typical analyses conducted to inform health technology assessment of a new intervention do not include a formal health equity impact evaluation or DCEA. One of the reasons is that the clinical trials for new interventions frequently do not have the power or are not designed to estimate the required treatment effects for sub-populations across which you want to analyze equity. The objective of the paper is to discuss how gaps in evidence regarding equity-relevant subgroup effects for new and existing interventions can potentially be overcome with advanced Bayesian evidence synthesis methods to facilitate a credible model-based DCEA. Methods: First, the evidence needs and challenges for a model-based DCEA are outlined. Next, alternative evidence synthesis methods will be summarized, followed by an illustrative example of implementing these methods. The paper will conclude with some practical recommendations. Results: The key evidence challenges for a DCEA relate to estimating relative treatment effects due to lack of inclusion of relevant subgroups in the randomized controlled trials (RCTs), lack of access to individual patient data (IPD) for all trials, small subgroups resulting in uncertain effects, and reporting gaps. Advanced Bayesian evidence synthesis methods can help overcome evidence gaps by considering all relevant direct, indirect, and external evidence simultaneously. Methods discussed include (network) meta-analysis with shrinkage estimation, conventional (network) meta-regression analysis, multi-level (network) meta-regression analysis, and generalized evidence synthesis. For a new intervention for which only RCT evidence is available and no real-world data, estimates can be improved if the assumption of exchangeable subgroup effects or the shared or exchangeable effect-modifier assumption among competing interventions can be defended. Furthermore, formal expert elicitation is worthwhile to improve estimates. Conclusion: This paper provides an overview of advanced evidence synthesis methods that may help overcome typical gaps in the evidence base to perform model-based DCEA along with some practical recommendations. Future simulation studies are needed to assess the pros and cons of different methods for different data gap scenarios.

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“…Unlike the abovementioned limitation of a network meta-regression model with a shared interaction-effect parameter for the IPD studies and aggregate-level studies required to "transfer" information from studies with IPD to studies involving other comparisons for which there is only aggregate-level data, ML-NMR avoids such aggregation or ecological bias. A simple ML-NMR model for dichotomous patient-related effect-modifier can be described as follows: (8) The part of the model relevant for IPD studies is the same as used in model 5 with the exception that the coefficient for the prognostic effect of the covariate, M V , is fixed across studies. For the aggregate-level data part of the model, : "# is the overall expected outcome in study i with intervention k and is determined by integrating the individual-level model over the joint withinstudy distribution of the binary covariate that defines the two subgroups of interest.…”

Section: Multilevel Network Meta-regression With Participant-level Data and Aggregate-level Datamentioning

confidence: 99%

“…Health disparities are an important concern. However, the typical analyses conducted to inform HTA do not include a formal evaluation of the impact a new intervention will have on health equity, despite the availability of a quantitative framework to do so: distributional cost-effectiveness analysis (DCEA) [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Evidence Synthesis Methods to Estimate Subgroup Effects for Distributional Cost-effectiveness Analysis of New Treatments

Jansen

2021

Preprint

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Distributional cost-effectiveness analysis (DCEA) is an extension of conventional cost-effectiveness analysis to quantify health equity impacts. Although health disparities are recognized as an important concern, the typical analyses conducted to inform health technology assessment of a new intervention do not include a DCEA. One of the reasons brought forward is the relative sparseness of the available evidence for a new intervention. The objective of this paper is to review advanced evidence synthesis methods to estimate subgroup specific treatment effects relevant for a DCEA of new interventions. The paper will outline the evidence needs and gaps, present alternative evidence synthesis methods followed by an illustrative example, and conclude with some practical recommendations. Evidence challenges for estimating relative treatment effects are due to lack of inclusion of relevant subgroups in the randomized controlled trials (RCTs), lack of access to individual patient data, small subgroups resulting in uncertain effects, and reporting gaps. Evidence synthesis methods can help overcome evidence gaps by considering all relevant direct, indirect, and external evidence simultaneously. Methods of potential relevance include (network) meta-analysis with shrinkage estimation, conventional (network) meta-regression analysis, multi-level (network) meta-regression analysis, and generalized evidence synthesis. For a new intervention for which only RCT evidence is available and no real-world data, estimates can be improved if the assumption of exchangeable subgroup effects or the shared or exchangeable effect-modifier assumption among competing interventions can be defended. Future research is needed to assess the pros and cons of different methods for different data gap scenarios.

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Distributional cost effectiveness analysis of West Yorkshire low emission zone policies

Cited by 12 publications

References 27 publications

Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making

Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making

Health Equity Impact Evaluation of New Treatments – Evidence Synthesis Methods to Overcome Data Gaps

Evidence Synthesis Methods to Estimate Subgroup Effects for Distributional Cost-effectiveness Analysis of New Treatments

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