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ItemWhat is sensitivity analysis?(Hayward Medical Communications, 2014)While economic models are a useful tool to aid decision-making in healthcare, there remain several types of uncertainty associated with this method of analysis. One-way sensitivity analysis allows a reviewer to assess the impact that changes in a certain parameter will have on the model’s conclusions. Sensitivity analysis can help the reviewer to determine which parameters are the key drivers of a model’s results. By reporting extensive outputs from sensitivity analysis, modellers are able to consider a wide range of scenarios and, as such, can increase the level of confidence that a reviewer will have in the model. Probabilistic sensitivity analysis provides a useful technique to quantify the level of confidence that a decisionmaker has in the conclusions of an economic evaluation.
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ItemWhat is meta-analysis?(Hayward Medical Communications, 2014)Meta-analysis is a set of statistical techniques for combining data from independent studies to produce a single estimate of effect. Meta-analysis is often used within healthcare, but is also applied in other disciplines including psychology and the social sciences. Within healthcare, meta-analysis is often used to assess the clinical effectiveness of interventions it does this by combining data from two or more studies (usually randomised controlled trials). Meta-analysis of trials provides more precise estimates of treatment effect, by making use of all available data. Meta-analysis is often part of the systematic review process, many systematic reviews include one or more meta-analyses. The validity of any meta-analysis depends on the studies on which it is based. Well-conducted meta-analyses aim for complete coverage of all relevant studies, look for the presence of heterogeneity among studies, and explore the robustness of the main findings using sensitivity analysis.
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ItemWhat is indirect comparison?(Hayward Medical Communications, 2014)Indirect comparison can be used to compare treatments that have not been directly compared with each other in a head-to-head trial. It is often used when there is no evidence or insufficient evidence from headto- head trials, or when more than two treatments are of interest. Indirect comparisons are usually conducted using network meta-analysis, an extension of meta-analysis that includes more than two treatments. Network meta-analysis is also referred to as multiple-treatments metaanalysis. Network meta-analysis includes indirect treatment comparison and mixed treatment comparison, although all of these terms are often used interchangeably. Like meta-analysis, indirect comparison combines data from different studies (usually randomised controlled trials) in order to produce overall estimates of treatment effects. Basic assumptions required for indirect comparisons include a homogeneity assumption as per standard meta-analysis, a similarity assumption for indirect comparison and a consistency assumption for the combination of direct and indirect evidence. It is essential to fully understand these basic assumptions in order to produce valid indirect comparisons. Indirect comparison is often part of the systematic review process. The validity of any indirect comparison also depends on the studies on which it is based. The use of indirect comparison has increased rapidly in recent years, and indirect comparisons are now accepted by many health technology assessment agencies.
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ItemWhat is health technology assessment(Hayward Medical Communications, 2014)Health technologies include: ‘interventions used to promote health, to prevent, diagnose or treat acute or chronic disease, or for rehabilitation. They include pharmaceuticals, devices, procedures and organisational systems used in healthcare’. Health technology assessment (HTA) has been defined as ‘the systematic evaluation of the properties of a health technology, addressing its direct and intended effects, as well as its indirect and unintended consequences, to inform decision-making’. Key stakeholders in healthcare policy and decision-making include: patients, healthcare professionals, industry, third-party payers and government. HTA has groundings in different methodological streams – policy analysis, evidence-based medicine, health economic evaluation and social science. It gives context-specific input into the policy-making process. The HTA process and related research findings are just one input into the decision-making process. Other factors include: expertise and experience, lobbyists, policy context and values, and available resources. There has been a growth in HTA to support the decision-making process in many countries, in line with a reduction in available resources. Although there are many similarities in the process in terms of methodologies used for assessment and attributes evaluated (clinical, costeffectiveness, safety and quality of life), there is also considerable variability, predominantly in terms of remit and funding. The process of defining best practice in HTA has been ongoing for several years at a national level. Work has been carried out to streamline HTA processes transnationally towards a practical collaboration to bring about more effective use of national HTA. To date, this work has taken the form of improving reporting standards (the International Network of Agencies for Health Technology Assessment) development of the HTA Core Model and toolkit to aid transferability between settings (the European network for Health Technology Assessment) and
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ItemWhat is Bayesian statistics?(Hayward Medical Communications, 2014)Statistical inference concerns unknown parameters that describe certain population characteristics such as the true mean efficacy of a particular treatment. Inferences are made using data and a statistical model that links the data to the parameters. In frequentist statistics, parameters are fixed quantities, whereas in Bayesian statistics the true value of a parameter can be thought of as being a random variable to which we assign a probability distribution, known specifically as prior information. A Bayesian analysis synthesises both sample data, expressed as the likelihood function, and the prior distribution, which represents additional information that is available. The posterior distribution expresses what is known about a set of parameters based on both the sample data and prior information. In frequentist statistics, it is often necessary to rely on large-sample approximations by assuming asymptomatic normality. In contrast, Bayesian inferences can be computed exactly, even in highly complex situations.
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ItemWhat is an NNT?(Hayward Medical Communications, 2014)The number needed to treat (NNT) has become a popular measure of effectiveness of interventions. NNTs are much easier to comprehend than any statistical description, and NNTs for different agents can be easily compared. An NNT is treatment-specific and describes the difference between a treatment and a control in achieving a particular clinical outcome. It can be used to describe any outcome where event rates are available for both a treatment and a control. Clearly defining a useful clinical outcome is the best way of calculating and using NNTs. NNTs calculated from systematic reviews of randomised controlled trials provide the highest level of evidence because systematic reviews contain all the relevant information and the largest numbers of patients available for analysis. NNTs only have utility when the evidence on which they are based fulfils criteria of quality, validity and size. Even data from a systematic review can be compromised. An NNT is just one part of the information required in making a purchasing decision. There are many other factors, including adverse effects, costs, and individual, social and medical priorities.
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ItemWhat is a Cox model?(Hayward Medical Communications, 2014)A Cox model is a statistical technique for exploring the relationship between the survival of a patient and several explanatory variables. Survival analysis is concerned with studying the time between entry to a study and a subsequent event (such as death). A Cox model provides an estimate of the treatment effect on survival after adjustment for other explanatory variables. In addition, it allows us to estimate the hazard (or risk) of death for an individual, given their prognostic variables. A Cox model must be fitted using an appropriate computer program (such as SAS, STATA, SPSS or R). The final model from a Cox regression analysis will yield an equation for the hazard as a function of several explanatory variables. Interpreting the Cox model involves examining the coefficients for each explanatory variable. A positive regression coefficient for an explanatory variable means that the hazard is higher, and thus the prognosis worse. Conversely, a negative regression coefficient implies a better prognosis for patients with higher values of that variable.
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ItemWhat are the HTA processes in the UK?(Hayward Medical Communications, 2014)Health technology assessment (HTA) has a long history in the UK. In recent years, HTA has become synonymous with the activities of the National Institute for Health and Care Excellence (NICE) in England, although important entities also exist in Scotland (Scottish Medicines Consortium [SMC]) and Wales (All Wales Medicines Strategy Group [AWMSG]). The NICE technology appraisal (TA) programme develops guidance on the use of new and existing medicines, treatments and procedures within the NHS. NICE commissions so-called technology assessment groups to prepare assessment reports for consideration by the Technology Appraisal Committee (TAC), which is the primary decision-making body in the production of guidance on new health technologies. The TAC includes academics, healthcare professionals, NHS managers and commissioners, and lay members of the public the appraisal of the specific technology includes representatives of the companies and of the patients affected. Guidelines on the methods of TA, which have been issued by NICE, embody the concept of the reference case. There are two approaches to TAs: multiple technology appraisals (MTAs) for the evaluation of all the relevant technologies for the same indication and single technology appraisals (STAs) for the evaluation of single technologies for a sole indication. MTAs started as the standard approach, taking 54 weeks from process initiation. In an MTA, the independent evidence assessment group reviews the evidence base and develops its own independent effectiveness and cost-effectiveness assessment, as well as critically appraising submissions from companies. STAs are currently the most common approach, taking 37 weeks from initiation. In an STA, the company submits evidence on the technology’s effectiveness and cost-effectiveness the independent evidence review group critically appraises the submission. Advantages of NICE’s approach to TAs include its methodological rigour, the encouragement of extensive stakeholder involvement and transparen
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ItemWhat are health utilities?(Hayward Medical Communications, 2014)Utilities are cardinal values that represent the strength of an individual’s preferences for specific health-related outcomes. Measuring health utilities involves two main steps: defining a set of health states of interest, and valuing those health states. There are direct or indirectmethods of utility valuation. The methods that have been used to collect data on utilities include the standard gamble approach, the time trade-off approach and the visual analogue approach. The main indirect methods of utility measurement are: the use of generic preference instruments (EQ-5D, SF-6D and HUI) the use of disease-specific preference measures and mapping from a disease-specific health-related quality of life instrument to a generic instrument. Generic preference-based measures are increasingly being used in cost–utility analyses of pharmaceutical and other healthcare interventions. In the UK, the National Institute for Health and Care Excellence has specified the EQ-5D as its preferred method of utility measurement. Utilities have been used as the preference weights (quality levels) within the quality-adjusted life-year model – an increasingly popular outcome measure used by health technology assessment bodies in drug access decision-making.
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ItemWhat are hazard ratios?(Hayward Medical Communications, 2014)Hazard ratios are commonly used when presenting results in clinical trials involving survival data, and allow hypothesis testing. They should not be considered the same as relative risk ratios. When hazard ratios are used in survival analysis, this may have nothing to do with dying or prolonging life, but reflects the analysis of time survived to an event (the event may, in some instances, include cure). A hazard is the rate at which events happen, so that the probability of an event happening in a short time interval is the length of time multiplied by the hazard. Although the hazard may vary with time, the assumption in proportional hazard models for survival analysis is that the hazard in one group is a constant proportion of the hazard in the other group. This proportion is the hazard ratio. When expressing the results of clinical trials, the hazard ratio conveys no information about the size of clinical effect and should be considered alongside a measure of time, usually the median time to the event under scrutiny comparing active treatment and control groups (the points at which half the subjects have experienced the event in each arm of the study).