Health Economics Glossary
This glossary has been designed with the intention of providing a simple overview of key topics in health economics.
Please note the majority of this glossary has been sourced from: A Glossary of Health Economic Terms. York; York Health Economics Consortium; 2016. https://yhec.co.uk/resources/glossary/
Definition
A Posteriori (Tests)
Hypotheses formed based on empirical observations and subsequent attempts to hypothesize underlying causes. These tests are conducted after study data collection and may introduce bias. Used in Bayesian analysis to update estimates.
A Priori (Tests)
Hypotheses generated prior to study data collection, based on assumed principles and deductions. Common in Bayesian analysis for integrating prior expectations with study observations.
Absolute Risk Reduction (ARR)
The change in absolute risk between exposed and non-exposed groups, fundamental to economic evaluation and used to calculate the Number Needed to Treat (NNT).
Base Case Analysis
Results obtained from an economic model using preferred assumptions and input values. Sensitivity analyses explore deviations from this base case.
Bayesian Analysis
Statistical inference method synthesizing prior knowledge with new data to estimate posterior distributions. Common in healthcare evaluation for meta-analysis and trial design.
Bias
Systematic error in a study leading to incorrect results even if replicated. Can occur at any stage of a study, such as randomization or reporting findings.
Bootstrapping
Non-parametric technique estimating distributions of statistics like ICER through resampling with replacement from original data. Used for confidence intervals and meta-analyses.
Budget Impact Analysis
Estimates changes in expenditure due to new healthcare interventions or policy changes at population levels using budget impact models over 3–5 years. Focuses on affordability rather than cost-effectiveness.
Burden of Illness
Combined costs (monetary and non-monetary) of a disease, including pain, lost wages, caretaker expenses, and mental health impacts. Measures individual, employer, payer, or societal perspectives.
Citation Index
Bibliographic database offering citation analysis features to view articles cited by or citing a given item. Examples include SCI and SSCI from Web of Science™.
Clinical Equivalence Study
Studies aiming to show minimal clinically important differences between technologies using two-sided significance testing within pre-specified margins.
Clinical Outcome Assessment (COA)
Measures describing how patients feel, function, or survive based on FDA guidelines. Types include patient-reported outcomes (PROs).
Clinical Trial
Research investigation in clinical settings testing efficacy/safety of drugs/devices with strict protocols, ethical approval, randomization, blinding, etc., governed by Good Clinical Practice standards globally.
Cochrane Collaboration
Organisation conducting systematic reviews/meta-analyses of RCTs globally to support evidence-based decision-making in healthcare interventions. Founded in response to Archie Cochrane’s call for up-to-date reviews of RCTs.
Cognitive Debriefing
A structured interview technique used in the development of Patient Reported Outcomes (PROs) where patients explain their understanding of questions and response scales while thinking aloud during completion of instruments.
Cognitive Interviewing
A method used in the development and refinement of patient-reported outcome measures (PROMs) to capture the patient perspective on a particular domain or item. A cognitive interview is a one-to-one interview with patients and may be conducted to evaluate patients’ understanding of the questions (or items), the content of the questions and whether the PROM covers all domains that are of relevance to patients with a specific condition.
Cohort Models
Economic evaluation models representing experiences of simulated patient cohorts over time using decision trees/Markov processes for estimating health events/states proportions and associated costs/utilities.
Concept Elicitation
Process in outcomes research where open-ended questions allow patients to spontaneously describe concepts like symptoms/impacts important to them during interviews.
Confidence Interval
Around a particular value gives an estimated range around the measured value that is likely to contain the true population value (given the assumptions used in estimating it). For example, a 95% confidence interval indicates that if the estimation process were repeated many times, 95% of the calculated intervals would contain the true value.
Conjoint Analysis
Conjoint analysis is the analytical technique used in discrete choice experiments, which is used in healthcare to evaluate preferences from participants (patients, payers, commissioners) for different attributes of a intervention, without directly asking them to state their preferred options.
Construct Validity
The extent to which a measure assesses the theoretical construct it is intended to measure. It involves demonstrating that the measure correlates with other measures in a way that is consistent with theoretical expectations.
Content Validity
The extent to which a patient-reported outcome measure (PROM) comprehensively assesses the concepts relevant to the target population through qualitative research methods like cognitive interviews.
Cost (Economic)
The value of resources sacrificed to acquire something, including monetary price, time, and opportunity costs. In healthcare, reflects system expenditures (treatments, staff time) and alternative uses of those resources. Includes direct/indirect, fixed/variable, average/marginal, and incremental costs.
Cost-Benefit Analysis (CBA)
Comparison of interventions where both costs and benefits are expressed in monetary terms using metrics like net monetary benefit. Uses willingness-to-pay surveys or choice experiments. Common in infrastructure projects (e.g., hospital facilities) rather than health technology assessments.
Codependent technologies
Health technologies are codependent where the patient health outcomes related to the use of one health technology (e.g. a medicine) are improved by the use of another health technology (e.g. a pathology test or an imaging technology). The use of the technologies needs to be combined (either sequentially or simultaneously) to achieve or enhance the intended clinical effect of either technology. Therefore, the net clinical benefits of the joint use of the technologies, as distinct from the net clinical benefit of each technology in isolation, needs to be determined for a health technology assessment. The cost-effectiveness and financial implications of the joint use of the technologies are also considered as part of the reimbursement decision.
Cost minimisation analysis (CMA)
Cost minimisation analysis is a method of comparing the costs of alternative interventions (including the costs of managing any consequences of the intervention), which are known, or assumed, to have an equivalent medical effect. This type of analysis can be used to determine which of the treatment alternatives provides the least expensive way of achieving a specific health outcome for a population.
Cost of Illness
Total economic burden of a disease, including direct medical costs (e.g., treatments), indirect costs (e.g., lost productivity), and intangible costs (e.g., pain). Helps quantify societal impact for policy decisions.
Cost-Benefit Analysis
In healthcare evaluation cost-benefit analysis (CBA) is a comparison of interventions and their consequences in which both costs and resulting benefits (health outcomes and others) are expressed in monetary terms. This enables two or more treatment alternatives to be compared using the summary metric of net monetary benefit, which is the difference between the benefit of each treatments (expressed in monetary units) less the cost of each. Monetary valuations of benefits are commonly obtained through willingness to pay (WTP) surveys or discrete choice experiments (DCEs). Although popular in other fields, CBA is not commonly used in health technology assessment due to difficulty of associating monetary values with health outcomes such as (increased) survival. Most commonly CBAs have been used to assess large capital development projects (new hospital facilities) or interventions that improve waiting times or location/access to services.
Cost-Effectiveness Acceptability Curve (CEAC)
The cost-effectiveness acceptability curve (CEAC) is a graph summarising the impact of uncertainty on the result of an economic evaluation, frequently expressed as an ICER (incremental cost-effectiveness ratio) in relation to possible values of the cost-effectiveness threshold. The graph plots a range of cost-effectiveness thresholds on the horizontal axis against the probability that the intervention will be cost-effective at that threshold on the vertical axis. It can usually be drawn directly from the (stored) results of a probabilistic sensitivity analysis. The CEAC helps the decision-maker to understand the uncertainty associated with making a particular decision to approve or reject a new heath technology.
Cost-Effectiveness Acceptability Frontier (CEAF)
The cost-effectiveness acceptability frontier (CEAF) is a graph summarising the uncertainty around the cost-effectiveness of the interventions compared in a model, by indicating which strategy is economically preferred at different threshold values for cost-effectiveness. Similar to the cost-effectiveness acceptability curve, the graph plots a range of possible cost-effectiveness thresholds on the horizontal axis against the probability that an intervention of interest will be cost-effective (at the given threshold value) on the vertical axis. As the threshold increases the economically preferred treatment changes, the switch point being where the threshold value increases beyond the relevant ICER reported for the intervention of interest. This type of presentation is particularly useful if there are three or more alternatives being compared, in which case there may be two or more switch points at different threshold values.
Cost-Effectiveness Analysis (CEA)
Cost-effectiveness analysis evaluates the effectiveness of two or more treatments relative to their cost. The aim of the decision maker when assessing a new intervention is to maximise outcomes (i.e. QALYs) and minimise opportunity costs. Cost-effectiveness analysis is the method used to measure these outcomes.
Cost-Effectiveness Frontier
The cost-effectiveness frontier is the line connecting successive points on a cost-effectiveness plane which each represent the effect and cost associated with different treatment alternatives. The gradient of a line segment represents the ICER of the treatment comparison between the two alternatives represented by that segment. The cost-effectiveness frontier consists of the set of points corresponding to treatment alternatives that are considered to be cost-effective at different values of the cost-effectiveness threshold. The steeper the gradient between successive points on the frontier, the higher is the ICER between these treatment alternatives and the more expensive alternative would be considered cost-effective only when a high value of the cost-effectiveness threshold is assumed. Points not lying on the cost-effectiveness frontier (usually above and to the left of the frontier) represent treatment alternatives that are not considered cost-effective (compared with a relevant alternative lying on the frontier) at any value of the cost-effectiveness threshold.
Cost-Effectiveness Plane
The cost-effectiveness plane is used to visually represent the differences in costs and health outcomes between treatment alternatives in two dimensions, by plotting the costs against effects on a graph. Health outcomes (effects) are usually plotted on the x axis and costs on the y axis. Frequently ‘current practice’ is plotted at the origin, and so the x and y values represent incremental health outcomes and incremental costs versus current practice. More than two points can be represented on the plane, with the line connecting cost-effective alternatives being called the cost-effectiveness frontier. The cost-effectiveness plane is divided into four quadrants: most cost-effectiveness analyses deliver results in the north-east (NE) quadrant, in which new interventions generate more health gains but are more expensive. Other quadrants are relevant when a new intervention generates poorer health outcomes (NW or SW) or lower costs (SW or SE). Cost-effectiveness planes are also useful to show the uncertainty around cost-effectiveness outcomes, often represented as a cloud of points on the plane corresponding to different iterations of an economic model in a (probabilistic) sensitivity analysis.
Cost-Effectiveness Threshold
The cost-effectiveness threshold is the maximum amount a decision-maker is willing to pay for a unit of health outcome. If the cost-effectiveness (ICER) of a new therapy (compared with a relevant alternative) is estimated to be below the threshold, then (other things being equal) it is likely that the decision-maker will recommend the new therapy. However for values near the threshold, the level of uncertainty may become important. Thresholds are often established by analysis of previous (reimbursement) decisions: they are not themselves outputs of cost-effectiveness analyses, but guides (or rules) to interpretation of these outputs for decision-making, and they are specific to each unit of health outcome used. They are closely related to the economic concept of ‘opportunity cost’, in which the value of an intervention is considered to be the value of what is foregone in order to implement the intervention. The threshold value stands for the health outcome that could have been achieved if the resource required to implement the intervention of interest had been used elsewhere.
Cost-Utility Analysis (CUA)
Cost-utility analysis is a type of cost-effectiveness analysis in which the (incremental) cost per quality-adjusted life year (QALY), or some other preference-based valuation of heath outcome, is estimated. Two alternative interventions are assessed by comparing how many additional QALYs are gained at what additional cost. The use of QALY as a measure of health outcome enables comparisons to be made across disease areas, particularly useful for broad-based resource allocation decision-making. Cost-utility analyses are frequently required by health technology assessment agencies.
Credibility Interval
Credibility intervals are used in Bayesian analysis to provide predictive indicators of the distribution of a given outcome. Whilst they can be analogous to frequentist-based confidence intervals, credibility intervals reflect the probabilistic nature of the analysis. Credibility intervals are commonly used to represent the degree of uncertainty in the outputs of network meta-analyses.
Criterion Validity
Criterion validity is the degree to which the scores of a PRO measure are an adequate reflection of a “gold standard.” There are two types of criterion validity: (1) Concurrent validity is demonstrated when the outcomes of the instrument are highly correlated with those of the criterion. (2) Predictive validity is demonstrated when the outcomes of the instrument are highly correlated with those of the criterion that can only be assessed at some point in the future (i.e. after the instrument has been administered). The criterion validity is only as good as the validity of the gold standard being compared against.
Critical Appraisal
Critical appraisal is the process of systematically assessing the report of a piece of research (for example, a clinical trial, meta-analysis or cost-effectiveness analysis) in terms of the validity and correct application of its methods, correct reporting of the results and justification for the interpretation of the results. Critical appraisal can be performed on many types of research reports relevant to health technology assessment, and a number of authoritative checklists are available to guide the process for each type of research (See http://www.cebm.net/).
Decision Tree
A decision tree is a form of analytical model, in which distinct branches are used to represent a potential set of outcomes for a patient or patient cohort. A decision tree consists of a series of ‘nodes’ where branches meet: each node may take the form of a ‘choice’ (a decision about which alternative intervention to use) or a ‘probability’ (an event occurring or not occurring, governed by chance). Probabilities at any specific node must always add to 1. Costs and outcomes are assigned to each segment of each branch, including the end (‘leaf’) of each branch. Outcomes and costs for each branch are combined using branch possibilities and the tree is ‘rolled back’ to a decision node, at which the expected outcome and cost for each treatment alternative can be compared. Decision trees are frequently used to model interventions that have distinct outcomes that can be measured at a specific time point, as opposed to evaluations where the timing of the outcome is important.
Deed of Agreement
A legally binding contract between a pharmaceutical company (sponsor) and the Australian Government (represented by the Department of Health) that outlines specific terms and conditions related to the listing of a medicine on the Pharmaceutical Benefits Scheme (PBS). It is used to manage risks identified by the Pharmaceutical Benefits Advisory Committee (PBAC), such as uncertainty around expenditure, cost-effectiveness, or utilization. Listing on the PBS cannot occur until the deed has been signed and executed.
Delphi Method
The Delphi method was originally developed as a structured approach for collecting opinions about the future and judging the likelihood of future events or situations. In health technology assessment (HTA) and economic evaluation this method has been adapted (sometimes referred to as ‘modified Delphi’) to assess assumptions and estimate parameters (e.g. for economic modelling where source information is lacking or may be subject to bias. In this method a group of experts reply anonymously to questionnaires, and subsequently receive feedback in the form of a summary representation of the group response, following which each may modify their response. The process repeats itself over a number of rounds until expert consensus is reached.
Disability-Adjusted Life Year (DALY)
The disability-adjusted life year (DALY) is a generic measure of health effect that can be used in cost-effectiveness analysis as an alternative to the quality-adjusted life year (QALY). They are a measure of overall disease burden, expressed as the number of years lost due to ill-health, disability or early death. A DALY represents one year of healthy life, and is usually expressed as DALYs lost compared with theoretical maximum, this being a life with maximum achievable life-expectancy and without disability or disease.
Discount Rate
Economic evaluations refer to a choice to be made between alternative interventions at a specific point in time, however the costs and health outcomes associated with each intervention occur at different points in time, present or future. Costs and health outcomes that are predicted to occur in the future are usually valued less than present costs, and so it is recommended that they be discounted in analysis. This is usually achieved by expressing the results as series (streams) of health outcomes and costs over time, applying a discounting factor to each value in the series and then aggregating to give a ‘present value’ of each stream. The discount factor increases over time, based on an underlying discount rate.
Disease Model
A disease model is a simplified mathematical representation of the course of a disease over time in a patient cohort. In health technology assessment, disease models are used to represent the progression of chronic diseases and the impact of risk factors of interest on disease incidence, progression and mortality. Often a micro-simulation approach is used, but simpler models may use a cohort Markov design (Markov model). Model inputs generally come from epidemiological studies. Disease models may be used to assess the potential impact of new therapies through varying the rates of progression or the balance of risk factors. They may form the basis of economic models, where specific treatment options are compared and treatment costs and utility values are included as inputs.
Disutility
Disutility represents the decrement in utility (valued quality of life) due to a particular symptom or complication. Disutility values are often expressed as a negative value, to represent the impact of the symptom or disease. They may be derived by subtracting utility values for a health state which includes the component (symptom, complication) of interest from a health state that is identical except for the absence of that component. Disutilities may be combined (usually additively, although occasionally multiplicative combinations are used) to provide a combined value of their collective impact on a patient’s quality of life. However, as with utilities, this needs to be done with care as there are situations where disutility (A+B) ≠ (disutility (A) + disutility (B)) when the individual and combined health states are valued independently.
Economic Evaluation
Analysis of costs and effects of interventions to inform reimbursement decisions. Includes cost-effectiveness, cost-utility, and cost-benefit analyses. Assesses incremental costs and outcomes over defined time horizons.
Economic Modelling
Economic modelling refers to the development of a model that is a simplified representation of the real world and is useful in supporting decision-making. In economic evaluation of healthcare interventions modelling synthesizes clinical, epidemiological and economical evidence from appropriate (and different) sources into an evaluation framework to derive an estimate for a specific outcome, for example an incremental cost-effectiveness ratio. Economic modelling is based on a specific design/structure, a range of modelling assumptions, and a set of input parameters. Common designs are decision trees, cohort Markov models, micro-simulations and (less frequently) discrete event simulations. Uncertainty surrounding a point estimate of the model outcome can be investigated by conducting sensitivity analysis, based on an understanding of uncertainty in the input parameters tin the model and associated with the model structure.
Effectiveness
Real-world performance of treatments outside controlled trial settings, considering practical implementation challenges.
Efficacy
Treatment performance under ideal conditions (e.g., controlled trials with optimal administration and ideal patient populations).
EQ-5D
The EuroQol Five Dimension (EQ-5D) is an example of a generic measurement of quality of life, which is used in many clinical trials and other prospective studies. The EQ-5D questionnaire consists of 5 questions relating to different domains of quality of life (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) for each of which there are 3 levels of response (no problems, some problems or severe problems). More recently, questions with 5 levels of response have been introduced (called EQ-5D-5L). The instrument is quick and easy to use, extensively researched/validated and translated into many different languages. Most importantly it is not disease-specific and therefore applicable to most disease areas (but less applicable in mental health and diseases causing specific disabilities such as blindness) and comparisons of interventions across disease areas.
Equity Weighting
Adjusts health outcomes to reflect societal preferences for prioritizing disadvantaged groups or conditions with higher disease burden.
Health Economics
Field analyzing efficiency, effectiveness, values, and behaviors in healthcare delivery. Focuses on resource allocation, cost analysis, and patient benefit optimization.
Health Economics and Outcomes Research (HEOR)
Health Economics and Outcomes Research (HEOR) is the most common label given to the function within pharmaceutical and life science companies with the responsibility for generating evidence of value of new interventions for reimbursement agencies and local health care payers. While ‘HE’ refers mostly to skills in economic evaluation ‘OR’ may refer to expertise in observational studies or in the development and use of new health outcomes measurements, especially PROs. HEOR staff will advise on the design of trials to best meet the needs of healthcare payers, as well as on other studies and analyses (meta-analyses, modelling, observational studies) that may be required.
Health Technology Assessment (HTA)
Multidisciplinary process assessing clinical effectiveness, safety, cost-effectiveness, and broader societal impacts of healthcare interventions. Guides reimbursement decisions.
Incremental Net Benefit (INB)
Monetary summary of cost-effectiveness: difference in effectiveness (converted to monetary value via willingness-to-pay threshold) minus difference in costs. Positive values indicate cost-effectiveness.
Indirect Costs
Non-medical societal costs from disease/illness: lost productivity, inability to perform daily activities, volunteering, or social engagements.
Incidence
Incidence quantifies the number of new cases of a diseases or events occurring in a specified time period, often a year, to a defined population who are at risk of the disease/event. It is given as a rate.
Incremental Cost-Effectiveness Ratio (ICER)
An incremental cost-effectiveness ratio is a summary measure representing the economic value of an intervention, compared with an alternative (comparator). It is usually the main output or result of an economic evaluation. An ICER is calculated by dividing the difference in total costs (incremental cost) by the difference in the chosen measure of health outcome or effect (incremental effect) to provide a ratio of ‘extra cost per extra unit of health effect’ – for the more expensive therapy vs the alternative.
Indirect Treatment Comparison (ITC)
An indirect treatment comparison is a method of deriving a comparative estimate between two treatments (treatment A and treatment B) which are not directly compared in head-to-head trials (or other studies), but which have both been compared to another intervention (treatment C). Treatments A and B can be indirectly compared via the common comparator C.
Intention to Treat
Intention-to-treat (ITT) analysis refers to analysis based on the initial treatment assignment, and not on the treatment eventually received. This type of analysis, now widely accepted as standard for the analysis of clinical trials, provides an unbiased comparison across the treatment groups. If cross-overs or drop-outs from the clinical trial are not random and imbalanced across treatment groups (i.e. potentially related to characteristics of the new intervention) then comparisons of groups as treated (‘On Therapy’- OT) may suffer from bias.
Literature Review
A literature review is a search and evaluation of available published studies in a chosen topic area. Occasionally the ‘grey’ literature: unpublished reports, newsfeeds, web sites etc. may be included. In economic evaluation, literature reviews are used to identify and summarise data and outcomes for a wide range of purposes, e.g. collating and summarising the results of clinical or economic evaluations of a specific health intervention, or identifying data inputs for possible use in future economic modelling. Literature reviews vary widely in their scope and quality, which may depend in part on the purpose and the resources (cost, time) available.
Managed Entry Agreements
These agreements allow conditional reimbursement based on ongoing data collection to address uncertainties around a drug's real-world effectiveness.
Market Access
Market access refers to the process of ensuring that treatments (medicines, medical devices etc.) for which marketing authorisation has been obtained from regulatory authorities are available (reimbursed, funded) to all patients who may benefit. However successful reimbursement this does not mean that all eligible patients will receive the new treatment, nor will they necessarily have ‘access’ to it. Market access addresses this problem by assessing barriers to uptake and proposing and implementing strategies overcome these barriers. These may include collection and communication of evidence relevant to different decision makers, implementing pricing strategies (discounts, payment by results), or provision of tools (apps etc.) or staff to provide expert advice to health system administrators.
Markov Model
A cohort model simulating transitions between health states over discrete time periods. Assigns probabilities, costs, and utilities to states for long-term outcome prediction.
Minimally Clinically Important Differences (MCID)
When assessing the clinical utility of therapies intended to improve subjective outcomes, the amount of improvement that is important to patients must be determined. The smallest benefit of value to patients is called the minimal clinically important difference (MCID). The MCID is a patient-centred concept, capturing both the magnitude of the improvement and the value patients place on the change. Using patient-centred MCIDs is important for studies involving patient-reported outcomes, for which the clinical importance of a given change may not be obvious to clinicians selecting treatments. The MCID defines the smallest amount an outcome must change to be meaningful to patients.
Monte-Carlo Simulation
Monte-Carlo simulation is a form of modelling used in many areas of science where model inputs are drawn from distributions and are not treated as fixed values. Key elements of a Monte-Carlo simulation are to (a) define a domain of possible inputs (parameter); (b) generate input values randomly from probability distributions across the domain; (c) perform a deterministic computation of the model output based on the selected inputs; (d) repeat for a sufficient number of ‘draws’ of input values; (e) aggregate the results. In health care evaluations micro-simulations frequently contain Monte-Carlo elements, for example using probability distributions to construct cohorts of patients with mixes of risk factors that may impact on their future experience.
Multi-Criteria Decision Analysis (MCDA)
Multi-criteria decision analysis is domain of operational research that is beginning to be used in healthcare decision-making. The technique recognises that decision-makers use multiple and disparate criteria when making decisions (for example about introducing new health care interventions or facilities etc.), and that it is important to make explicit the impact on any decision of all the criteria applied and the relative importance attached to them. In MCDA criteria affecting a decision are identified and weighted using explicit, transparent techniques. Then different options (strategies, interventions etc.) are scored against each criterion and the weights are used to provide summary scores for comparative purposes. MCDA has been found to be attractive in health technology assessment, especially in healthcare systems where there is reluctance to primarily use a single decision metric (such as the ICER). It helps to make more transparent assumptions underpinning decisions, which in principle may improve accountability and consistency of decision-making.
Network Meta-Analysis (NMA)
Network meta-analysis is a statistical method using both direct and indirect evidence (conventionally from randomised controlled trials) to estimate the comparative efficacy and/or safety of a number of interventions with each another. A network meta analysis will usually contain multiple treatments and multiple sources of evidence. Typically a systematic review is used to assemble all trial evidence for efficacy/safety of the interventions of interest in the population/condition and outcome measure of interest into an evidence network that will inform the network meta analysis. At this stage the comparability of populations, duration, outcome definitions and the feasibility of the statistical analysis for the network meta analysis is assessed. The reported differences in the outcome measure between interventions (and corresponding measure of uncertainty) in each trial are combined using Monte-Carlo Markov chain methods. In this way the benefit of randomisation in each source study is preserved when undertaking the network meta analysis.
Odds Ratio (OR)
An odds ratio (OR) is a measure of the proportional excess risk of an event in a population compared with the risk in another population. When the populations are defined by treatment choice (but otherwise identical, as in a clinical trial) this gives a measure of the relative effect of an intervention. The OR is the odds of an event occurring in the intervention group divided by the odds of the same event occurring in the comparison (control) group. (Odds are the number of subjects in a population experiencing an event divided by the number of subjects in the population not experiencing the event.) An odds ratio greater than one indicates that the event is more likely to occur in the intervention group than in the control group. An OR equal to 1 indicates that there is no difference between the groups (i.e. the event is equally likely to occur in the intervention group and control group).
Opportunity Cost
Value of the next best alternative forgone when resources are allocated to a specific intervention. Central to economic evaluations of healthcare resource use.
Patient Preference
Personal desirability rankings of health outcomes or treatments. Measured through techniques like conjoint analysis or discrete choice experiments.
Outcomes Research
Outcomes research is a term used to cover a broad range of areas of research, the primary focus being on assessing the effectiveness of health interventions and services. Examples in this domain cover healthcare delivery, cost-effectiveness, health, disease burden and so on. Patient-centred (sometimes known as patient-focused) outcomes research is an umbrella term used to summarise research into patients’ perspectives on their experiences in health care systems, for instance through development and use of patient-reported outcome (PRO) measures designed to capture the impact of treatment on symptoms and quality of life, or through measuring patient preferences to help (re)design hospital services.
Outcome-Based Contracts
Agreements linking drug payments to predefined clinical outcomes. Manufacturers may refund payers if targets aren't met.
Pareto Efficiency
A state where resources cannot be reallocated to improve one individual’s outcome without worsening another’s. A theoretical benchmark for optimal resource allocation.
Partitioned Survival Model
A partitioned survival model is a type of economic model used to follow a theoretical cohort through time as they move between a set of exhaustive and mutually exclusive heath states. Unlike a Markov model, the number of people in any state at successive points in time is not dictated by transition probabilities. Instead, the model estimates the proportion of a cohort in each state based upon parametric survival equations. These types of model are frequently used to model cancer treatments, with separate survival equations for overall survival and progression-free survival. Common functions used to describe survival are exponential, Weibull or Gaussian (amongst others). Sensitivity analysis can be undertaken by varying the parameters defining the survival equations, however if the survival equations are independent, care needs to be taken that logical fallacies are not made (e.g. overall survival exceeding progression free survival).
Patient-Centred Outcomes Research
Research prioritizing outcomes meaningful to patients, incorporating their perspectives throughout study design and implementation.
Patient-Reported Outcome Measures (PROMs)
Patient-reported outcome measures (PROMS) or patient-reported outcome instruments (more commonly used in the US) are psychometrically validated tools, such as questionnaires used to collect PROs. In clinical trials, for example, PROMs may be used to collect PRO data to enable a pharmaceutical company (“sponsor”) to support a claim in the product labelling (USA), product information (Australia) or summary of product characteristics (SmPC, Europe).
Patient-Reported Outcomes (PROs)
Patient-reported outcomes (PROs) are any reports directly from patients on their health, condition, etc. that is made solely by such patients without any input, suggestions or interpretation from their doctors, family, friends or other individuals. PRO is a blanket term relating to single or multidimensional aspects of patients’ symptoms, health, quality of life, treatment satisfaction, medication adherence, etc. PROs are often recorded in clinical trials, using validated instruments to measure the impact of the intervention as perceived by the patient.
Prevalence
Point prevalence is the proportion of individuals in a population who have a condition of interest at a specific point in time. Period prevalence is the proportion who experience the condition over a specified time-period. This differs from incidence as it includes those who already have the condition at the start if the time period. In the steady state (no epidemics) and when the prevalence in the population is quite low, Prevalence (P) and Incidence (I) are related: P = I x D, where D is the average duration of the disease. Cross-sectional surveys such as population censuses give detailed information on the prevalence of conditions or risk factors, but are less likely to be useful in describing the natural history or progression of a condition. In economic evaluation budget impact analyses tend to rely on prevalence information, whereas cost-effectiveness analyses are generally applicable to incident cohorts (with new developing or progressing disease).
Quality of Life
Quality of life is a broad, multidimensional concept of an individual’s subjective evaluation of aspects of his/her life as diverse as physical, social, spiritual and emotional well-being, as well as possibly touching on others areas such as his/her environment, employment, education and leisure time. Within this wide-ranging definition health-related quality of life (HRQOL) is used to refer to the impact a medical condition and/or treatment has on a patient’s functioning and well-being. HRQOL is increasingly being measured in clinical trials alongside other outcome measures to evaluate the full range of effects of an intervention (e.g. a new medicine) from the patients’ perspective. For instance, in oncology trials symptom burden may be measured in addition to survival and progression-free survival.
Quality-Adjusted Life Year (QALY)
The quality-adjusted life year (QALY) is a summary outcome measure used to quantify the effectiveness of a particular intervention. Since the benefits of different interventions are multi-dimensional, QALYs have been designed to combine the impact of gains in quality of life and in quantity of life (i.e. life expectancy) associated with an intervention. In this case it is the incremental (i.e. differences between two or more alternatives) QALYs, compared with the incremental costs, that provides the measure of economic value.
Relative Risk (RR)
Relative risk (RR), or risk ratio, is an estimate the magnitude of an association between an exposure and a disease, giving the likelihood of developing the disease on the exposed group compared with the unexposed. This is calculated as the ratio of the cumulative incidence of the disease in each group.
Risk Share Arrangement (RSA)
A general term referring to agreements where the financial risk associated with a medicine's listing on the PBS is shared between the pharmaceutical company and the Australian Government. Several types of deeds can be considered risk-sharing arrangements, including subsidization caps and reimbursement arrangements, data provision arrangements, and outcome-based agreements. The goal is to mitigate risks related to expenditure, utilization, or the need for further evidence on cost-effectiveness.
Sensitivity Analysis
Tests robustness of economic model results by varying input parameters (e.g., costs, utilities). Includes deterministic (one-way/two-way) and probabilistic (Monte Carlo) methods.
Special Pricing Arrangement (SPA)
A type of deed of agreement where the sponsor provides the medicine to Australia at a price recommended by the PBAC (deemed cost-effective), but due to international reference pricing or other commercial reasons, the sponsor cannot supply the medicine at a publicly available price. The Department recovers a percentage of the expenditure (through a rebate) to reflect the cost-effective recommendation made by the PBAC.
Social Return on Investment
Social return on investment is a performance measure similar to ‘Return on investment’, but which takes a broader societal perspective to valuing costs and benefits. Social and environmental factors are considered, in addition to economic variables to estimate benefits and costs. The formula used is the same as for return on investment, being benefit minus costs divided by costs, with the results expressed as a percentage. All benefits and costs must be expressed in monetary units.
Specificity
In the evaluation of diagnostic (or screening) tests specificity refers to the proportion of population without the condition who are correctly diagnosed as being without the condition. It is computed as the ratio of the true negatives to the (true negatives + false positives), and is usually expressed as a percentage. A highly specific diagnostic test is one that identifies very few healthy patients (incorrectly) as having the condition of interest (and needing further follow-up or treatment, without benefit).
Structured Review
The term ‘Structured review’ is occasionally used as an alternative term for ‘systematic review’. It may also be used to refer to a review that, while highly structured in its approach, is not as rigorous as a full systematic review.
Survival Analysis
Survival analysis is an analytical method focusing on time-to-event data. Frequently the event is death (overall survival), but many other events can be considered in this way, such as disease progression/relapse, or event occurrence (for prevention). Survival analysis is typically used in oncology, where patient survival (death from any cause) and time-to-progression are often key endpoints of a clinical trial: the analysis frequently forms the basis of associated economic evaluations using (partitioned) survival models. The attraction of survival analysis for economic evaluation is that economic endpoints such as (gains in) life-years and quality-adjusted life years are represented as areas under the (quality-adjusted) survival curve. Health outcomes are considered longitudinally over time, and not cross-sectionally at a specific point in time. The Kaplan-Meier method provides a non-parametric representation of survival over the time period that data was collected, allowing for incomplete patient records where patients are lost to follow up (censoring). Parametric representations of survival, defined using a number of different statistical distributions, such as Weibull, Gompertz or exponential, allow for survival to be extrapolated beyond measured patient experience, important for economic modelling.
Systematic Review
Systematic reviews adopt a rigorous scientific approach to identify and synthesize all the available evidence pertaining to a specific research question. They are carried out according to pre-defined protocols, which set out the scope of the systematic review, details of the methodology to be employed and reporting of findings. The objective is to ensure a comprehensive and repeatable search together with an unbiased assessment and presentation of the relevant evidence. Key components of a systematic review include: systematic and extensive searches to identify all the relevant published and unpublished literature; study selection according to pre-defined eligibility criteria; assessment of the quality and risk of bias in included studies; presentation of the findings in an independent and impartial manner and a discussion of the limitations of the evidence and of the review.
Time Horizon
The time horizon used for an economic evaluation is the duration over which health outcomes and costs are calculated. The choice of time horizon is an important decision for economic modelling, and depends on the nature of the disease and intervention under consideration and the purpose of the analysis. Longer time horizons are applicable to chronic conditions associated with on-going medical management, rather than a cure. A shorter time horizon may be appropriate for some acute conditions, for which long-term consequences are less important. The same time horizon should be used for both costs and health outcomes.
Tornado Diagram
In economic evaluations tornado diagrams are used to present the result of multiple univariate sensitivity analyses on a single graph. Each analysis is summarised using a horizontal bar which represents the variation in the model output (usually an ICER) around a central value (corresponding to the base case analysis) as the relevant parameter is varied between two plausible but extreme values. Typically the horizontal bars are ordered so that with those with the greatest spread (i.e. parameters to which the model output is most sensitive) come at the top of the diagram, and those with the lowest spread at the bottom. The resulting diagram of stacked horizontal bars has a distinctive tornado shape. Tornado diagrams are used to help the reviewer assess which of the model’s parameters have the greatest influence on its results.
Two Way Sensitivity Analysis
Two way sensitivity analysis is a technique used in economic evaluation to assess the robustness of the overall result (typically of a model-based analysis) when simultaneously varying the values of two key input variables (parameters).This is particularly useful when there is a correlation between the two variables that are tested, in which case varying them independently in univariate sensitivity analyses may give a misleading view. Examples of such correlated input parameters might be hazard ratios for progression-free survival and overall survival for cancer therapy, or utility values for moderate and severe disease states.
Univariate/One Way Sensitivity Analysis
Univariate/one way sensitivity analysis allows a reviewer to assess the impact that changes in a certain input (parameter) will have on the output results of an economic evaluation (most frequently those based on a model) – this may be referred to as assessing the robustness of the result to that parameter. The parameter of interest should be varied between plausible extremes, preferable justified by review of available evidence. This is the simplest form of sensitivity analysis since only one parameter is changed at one time, and correlations between parameters is not taken into account. Tornado diagrams are often used to summarise univariate sensitivity analyses testing a set of input variables in turn.
Uptake
In budget impact analysis, it is usual to compare a hypothetical future scenario (i.e. the approval of a new technology) against the counterfactual (i.e. no approval of the technology). Because it is rare for a new technology to be provided universally to all eligible patients, budget impact models usually make an assumption about the ‘uptake’ of the technology. This is usually characterised as the proportion of patients that would receive the technology in each consecutive year after its introduction. Often, the uptake may start slowly (with a low % of patients) and gradually increase over time. Careful consideration should be given to whether the new technology will only to new (incident) patients, to existing (prevalent) patients or to both.
Utility
In economic evaluation of healthcare interventions utilities (also called health state preference values) are used to represent the strength of individuals’ preferences for different health states. When utility values are averaged over a population of responders they can be considered to be valuations of health states. Conventionally the valuations fall between 0 and 1, with 1 representing the valuation of a state of perfect health and 0 representing the valuation of death (non-existence). In some scoring systems a negative utility value is also possible, which indicates that a (very poor) health state is valued as less preferable than death. Sequences of utility values reported over periods of time for individual patients or cohorts of patients may be aggregated to derive quality-adjusted life years, commonly used as outcomes in economic evaluation. Several methods are used to obtain health state preference values (utilities).
Value Framework
Tools evaluating treatment costs vs. benefits, often including clinical effectiveness, cost-effectiveness, and patient impact. Guides reimbursement decisions and shared decision-making.
Value-Based Healthcare
Value-based healthcare is a healthcare delivery model in which providers are paid based on patient health outcomes. Under value-based care agreements, providers are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way. Value-based care differs from a fee-for-service or capitated approach, in which providers are paid based on the amount of healthcare services they deliver. The “value” in value-based healthcare is derived from measuring health outcomes against the cost of delivering the outcomes and thus the primary focus is to improve outcomes that are delivered, and then deliver superior outcomes at a reduced cost.
Value-Based Pricing
Value-based pricing ties the price of a drug to the measurable benefits it delivers to patients, healthcare systems, and society. This approach ensures that prices reflect outcomes such as clinical efficacy, quality of life improvements, and cost savings compared to standard treatments.
Willingness to Pay (WTP)
Willingness-to-pay (WTP) is the valuation of health benefit in monetary terms, often so that this can be used in a cost-benefit analysis. The term WTP may also refer to survey techniques used to derive WTP valuations. However, in many health care system patients do not make direct purchasing choices for much of their care, and so WTP are not frequently used except in certain situations (e.g. waiting times, access arrangements to care, types of care usually obtained through direct payment) where monetary valuations may be more straightforward to make.