Economic Logic (4th Edition)

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Where quantification is difficult, the likely magnitude of such costs and outcomes and their impact on the results of the analysis should be discussed and disaggregated results may be presented as part of the analysis. An example of a case in which other perspectives may be considered is when the decision problem is from the perspective of the publicly funded health care payer, but the intervention permits patients to return to work sooner, which may shift costs away from patients and their informal caregivers.

In such cases, a societal perspective may be evaluated in a non-reference case analysis that allows for the full consideration of all costs and outcomes associated with the evaluation of the intervention. In the reference case, the time horizon should be long enough to capture all potential differences in costs and outcomes associated with the interventions being compared. The time horizon of the analysis should be conceptually driven, based on the natural history of the condition or anticipated impact of the intervention e.

A longer-term analysis allows for the exploration of uncertainty; this does not, however, imply that primary data must be collected from patients or affected populations over such a period.

Economic Logic Fifth Edition

When modelling chronic conditions, or when the interventions have differential effects on mortality, a lifetime horizon is most appropriate. For decision problems involving the dynamic evolution of the target population i. Shorter time horizons might be considered where there are no meaningful differences in the long-term costs and outcomes of interventions e. In these cases, justification should be provided for the duration of the time horizon selected. In some cases, multiple time horizons might be appropriate to consider how the cost-effectiveness of interventions differs in various phases of the condition, as well as in the overall condition.

When there is uncertainty in the choice of time horizon, the implications of this should be assessed by comparing the results based on the time horizon used in the reference and non-reference case analyses. This is of special relevance in instances when the majority of QALY gains from therapy occur long after treatment has been curtailed. Economic efficiency necessitates that the social discount rate measure the marginal social opportunity cost of resources allocated to government investment, which may be approximated by the real rate of interest on government bonds. In keeping with the social decision-making viewpoint adopted in these Guidelines , this rate should reflect the real rate of interest on government bonds faced by the higher authority i.

In Canada, health care is funded primarily by the provincial, territorial, and federal governments. Therefore, both provincial and federal government bonds may be considered as sources for estimating the real rate of interest. Taking into account the proportion of public health care spending by the provinces and territories relative to the federal government 19,20 and the observed uniformity between the historical returns of provincial and Canadian federal bonds, 21 the recommended discount rate for the reference case is based on provincial bond rates.

It is therefore recommended that costs occurring beyond one year be discounted using the real rate of interest on provincial government bonds. Assuming that decision-makers are likely to face exogenous budget constraints, 6 outcomes should be discounted using the same real rate of interest on provincial bonds, minus the growth rate of the cost-effectiveness threshold i.

The recommended rate for the reference case is set at 1. In principle, the appropriate discount rate to use depends upon the inter-temporal distribution of costs and outcomes over the time horizon of the analysis. In the absence of robust empirical evidence on this distribution, the Guideline recommendation is based on the real long-term cost of borrowing. The potential impact of applying non-constant discount rates i.

Table of Contents

When deciding whether to explore the use of non-constant discount rates, the researcher should be guided by the magnitude of the observed differences between short-term rates i. This section is intended to identify the key considerations that should guide the model-development process. Modelling in the context of health economic evaluations provides an important framework for synthesizing available data and assumptions from multiple sources to generate estimates of the expected costs and outcomes for the interventions of interest.

A decision-analytic model uses mathematical relationships to define a series of possible consequences that would result from the set of interventions being evaluated. The conceptualization of the model is a critical component of the model-development process.

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This process involves the development of a model structure that is defined by specific states or events and the relationships among them that together constitute the clinical or care pathway for the condition of interest and the interventions being compared. The conceptualization of the model should incorporate the potential for changes along the clinical or care pathway e. The model structure must be clinically relevant, and close collaboration with and input from those able to provide clinical expert judgment is necessary.

The model must be consistent with the current knowledge of the biology of the health condition, the causal relationships among variables that constitute the clinical or care pathway, and the expected effects of the interventions. The process of model conceptualization should not be dictated by the availability of data. In practice, however, the availability of data may constrain the options in model development, and the model structure will need to be revisited accordingly and modified within an iterative process.

Where there are well-constructed, validated models that appropriately capture the clinical or care pathway and reflect all of the components of the decision problem, researchers should consider these models when trying to conceptualize their own model structure.

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This could involve contacting and potentially collaborating with researchers who have developed previous models, or simply attempting to make similar structural assumptions, or, where they remain relevant, using similar natural history data to populate the model. Determining what constitutes an adequate level of detail in terms of the required states or events and the clinical or care pathway is one of the most difficult challenges a researcher faces when conceptualizing a decision model.

That is, the model should be conceptualized in such a way that it provides a representation of reality that captures the elements and relationships that are essential to address the decision problem, 35 but should not be more complex than is required. There are many decision-modelling techniques available to researchers when conducting economic evaluations, including decision trees, 28 cohort-level state-transition models i.

Most decision problems can be addressed with a wide variety of modelling techniques.

The choice of model type should be related to the characteristics of the decision problem, with justification provided regarding the choice of modelling approach. For any type of modelling approach chosen, the model must be methodologically sound and transparent, and researchers are encouraged to follow good modelling practice guidelines.

Researchers should think about the decision problem and whether the model is intended to address a single question, on one occasion, or if the model will be used on an ongoing basis to address multiple questions. Researchers should also anticipate the likelihood of interaction among individuals, or the possible impact of the intervention on the spread of the condition.

The decision problem may also require the ability to incorporate competition for constrained resources and the development of waiting lists or queues. Regardless of the chosen modelling technique, data are required to inform the various states and events and the movement along the clinical or care pathway. The conceptualization and incorporation of data into the model will vary depending on how the model is specified for the different modelling techniques.

Economic evaluations will often require estimates of both relative clinical effects of interventions Effectiveness section and information on the outcomes at baseline i. Where the baseline outcomes and the relative effects data are estimated from the same source e. Consequently, where there exist baseline natural history data that are independent of the relative effects data e. In all cases, researchers should define what constitutes relevant data based on the decision problem and then seek to identify data sources using a comprehensive and transparent approach that can be replicated by others.

The researcher should then determine whether a synthesis of the data sources is appropriate or possible. Where a synthesis cannot be conducted, the researcher should employ judgment in assessing the individual sources in terms of their relative fitness for purpose, credibility, and consistency in order to determine which data source represents the best trade-off among these criteria see Effectiveness section. When informing and incorporating parameter estimates for natural history, probability distributions should be derived and the associated uncertainty propagated through the model.

Where more than one data source is judged to be appropriate, this should be reflected in the reference case probabilistic analysis or using scenario analyses that consider the alternative sources for the estimates. Where Canadian data are lacking, and there is reason to believe that the available data may differ substantially from what would likely be observed in the jurisdiction of interest, this should also be incorporated into the analysis of uncertainty by examining the impact of different scenarios.

These scenarios should consider alternative estimates that may be more representative of the Canadian context. The sources of the alternative estimates e. Incomplete natural history data or inconclusive knowledge about the underlying condition will lead to structural uncertainty within the model. In such cases, the limitations of the natural history data should be acknowledged and addressed by building more than one plausible framework and examining the impact of the alternative model structures using scenario analysis.

Where the results of the analyses vary in ways that are substantive enough to potentially have an impact on the cost-effectiveness, the reasons for these differences should be identified and critically examined and, on the basis of this, researchers should recommend a particular model structure see Uncertainty section.

The incorporation into the model of parameter estimates for effectiveness, in addition to baseline natural history information, defines the movement along the clinical or care pathway of the model. Estimates of relative effect can be combined with baseline natural history data to derive the movement through the model for each intervention. Measures of effect are not limited to binary outcomes and can include other types of outcome scales e. If the effectiveness of the intervention changes over time, this should also be captured in the model.

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Researchers should be explicit about how the adverse events included in the economic evaluation were identified, and what methods were used to incorporate them. Where adverse events have a negligible impact on health effects, or no impact on costs and resources, it is often appropriate to exclude these events from the model.

Where adverse events are not included, a clear justification must be provided. Adverse events should be incorporated into the model by combining both the health condition and the associated adverse effects. In the case of utilities, the utility for a specific health state can then be adjusted by applying a disutility for an adverse event to allow the utility for the health state with an adverse event to be estimated. If effects are transitory i. Where data are available on the prevalence, costs, and disutility associated with each adverse event by intervention, this facilitates greater transparency.

Specifically, interventions that extend life may result in individuals experiencing future clinical events or costs associated with aging or other health conditions that are incurred as a result of their lives being extended. In the absence of sufficient data for informing parameter estimates, the elicitation of quantitative input from relevant experts may be useful. For the purposes of these Guidelines , the formal elicitation of quantitative input from relevant experts regarding the magnitude of a given parameter and its uncertainty i.

Expert elicitation involves key steps that researchers should implement and clearly describe. These include determining the information to be elicited, identifying a representative sample of experts, applying specific elicitation methods reflecting uncertainty in parameter estimates between and within experts , and synthesizing the elicited information across experts. For details regarding these steps, researchers are referred elsewhere. It is recommended that researchers continue to focus on identifying appropriate data sources for informing parameter estimates and, as elicitation methods continue to evolve, consider expert elicitation as a potential source of data for filling in gaps in the available information.

Model calibration methods involve the estimation of unknown model parameters by achieving agreement between the model outputs and other sources of data that are external to the model and not used to parameterize the model. Calibration should be distinguished from other sources of parameter estimation, which involve separate processes from the model itself and do not consider the overall similarity of the model outputs to the external data.

The use of calibration for informing parameter inputs is not routine and, as methods evolve, researchers should continue to focus on identifying appropriate data sources for informing parameter estimates, where calibration may be used to fill in gaps in the existing evidence base. Models should be formally validated in order to judge their accuracy.

Validation involves testing the model to confirm that it does what it is expected to do. Researchers should evaluate face validity during model conceptualization in order to ensure the validity of the model. The assessment of face validity is mostly qualitative in nature and is intended to assess whether the model structure, assumptions, and parameters accurately reflect the clinical or care pathway of interest and the potential impact of the interventions. This should be informed based on the expert judgment of those with content expertise, 33,49 and done early and iteratively throughout the analysis.

Formal internal validation of the model should be performed as a quality assurance measure for all mathematical calculations and parameter estimates. It should include testing the mathematical logic of the model and checking for errors.