The evidence supporting HTA decisions should be comprehensive to ensure decisions are made based on all relevant evidence available at the time of the decision. Systematic reviews follow a rigorous methodology to define the scope of the decision problem, to ensure all evidence is identified and current knowledge on the subject matter is properly summarised.
Network meta-analysis (NMA) and indirect comparisons
Network meta-analysis allows data from multiple trials making different comparisons to be synthesised to produce estimates of comparative effectiveness. Matching adjusted indirect comparisons, simulated trial comparisons, propensity score matching can be used to “recreate” comparable populations across studies. Multiparameter synthesis can be used to synthesise data from different trial and study designs. Advanced methods available for time to event endpoints and synthesis of observational and randomised controlled trial (RCT) data.
Survival analysis (including extrapolation & mixture/cure models)
Clinical trials have a limited duration and it may be necessary to map from surrogate/intermediate endpoints or extrapolate beyond the end of the trial. Survival analysis involves the modelling of time until one or more events occur, such as death or progression of disease or a cardiovascular event. This enables us to describe what happened during the trial as well as to make predictions about when similar events are likely to happen after the trial observation period.
Subgroup analyses are conducted to investigate heterogeneity between patients to answer specific questions about particular patient groups, types of intervention or types of study, in order to identify patient groups where the intervention is most efficacious.
Adjustment for treatment switching
If patients switch from their randomly assigned treatment in a clinical trial set onto an alternative in a manner that is not representative of clinical practice, intention-to-treat analyses will not identify the true comparative effectiveness of the treatments under investigation. We can use statistical methods including marginal structural models, two-stage adjustment, and rank preserving structural failure time models to correct for this. Similar issues can arise in observational studies.
Real World Evidence analytics
Analysis of large scale linked Electronic Health Record (EHR) data
EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information. The EHR has the potential to support evidence-based decisions and quality management. However, EHR data are potentially ‘big data’ in every sense; they cover a large number of people with a large amount of diverse information; therefore their analysis requires special considerations.
Development of disease natural history models
The natural history of disease is the course a disease takes in individual people from its pathological onset until its eventual resolution through complete recovery or death. Understanding the natural history of a disease is an important prerequisite for designing studies that assess the impact of interventions. Identification of biomarkers that mark disease progression may provide important indicators for drug targets and surrogate outcomes for clinical trials. However, collecting and visualizing data on natural history is challenging in part because disease processes are complex and evolve in different chronological periods for different subjects and may require combination of multiple population samples. Therefore, various epidemiological designs and analyses are needed to elucidate components of the natural history process.
Cost-effectiveness / cost-utility models
Cost-effectiveness analysis (CEA) is a form of economic analysis that compares the relative costs and outcomes (effects) of different courses of action. They provide a robust analytical approach for policy makers and regulators to assess if the cost associated with an intervention is worth paying. However, not all information for a CEA may be available from a single source. In these cases, models fill in the gaps. Models provide a framework for synthesising data from disparate sources, allowing extrapolations beyond the time horizons of available data and to population subgroups and strategies not observed in studies. Our team is experienced in finding the right modelling techniques for each individual decision problem and developing different types of decision models (from simple decision tree models through Markov models to microsimulations and discrete event simulations).
Budget impact models
A budget impact analysis (BIA) is an economic assessment that estimates the financial consequences of adopting a new intervention. It is used to evaluate whether a new intervention is affordable. Similar to cost-effectiveness models, budget impact models quantify the cost of a new intervention including any changes in health care utilisation resulting from the use of the new intervention (due to changes in outcomes, symptoms, and/or adverse events). The models also include a consideration of whether the intervention is replacing the existing standard of care, is being used in addition to the existing standard of care, or is being used only in situations where there has been no existing care. The size of the population treated is also explicitly considered. As a budget impact analysis is often used for resource allocation purposes, it takes a payer’s perspective, and uses a short-term time horizon (often 1 to 5 years).
Pricing models / Value-based pricing support
The historical, unit-based pricing model for health technologies is increasingly seen as overly restrictive. However, the more innovative solutions require a careful evaluation of the value of the new intervention as well as the financial risks and impact on the cash-flow and profits of both the payers and the companies. Our models can be used to determine an economically justifiable price as well as quantify the financial implications for all stakeholders of newer pricing solutions such as indication-specific pricing, bundled payments, financial-based risk sharing and performance-based risk sharing agreements.
Early decision-making models
Early models may be able to determine much earlier in the development pipeline which drugs have a low probability of success and better focus resources. Early modelling is a useful tool for thinking in a number of ways: a model can quantify what we do not know, help predict more intelligently, can test alternatives, can integrate data from many sources, can help understand placebo and finally, a model can help identify the need for and design a future study.
Strategic HTA advice and support
HTA submission development and support
Our team can help determine the submission strategy, develop a consistent and compelling value narrative, provide support throughout the submission development process to ensure all information is presented in the appropriate format and clarity as well as ensuring all information is consistent with the agreed submission strategy and will hold up against the scrutiny of HTA agencies.
Clinical trial design for HTA/market access
Clinical trials provide the pivotal evidence on the efficacy of the new intervention. Therefore, it is crucial that they provide the relevant evidence in the right populations in a timely matter. Our team can help design clinical trials identifying the target patient populations, the required patient numbers as well as defining the protocol regarding assessment time points, monitoring frequency, comparators and subsequent treatments.
Value framework analyses
Concerns about increases in health care expenditure have led to the development of initiatives to measure the value of health care technologies. The developed tools, also known as value frameworks, focus on evaluating therapeutic options based on health outcomes, value to the patient, and effectiveness compared with other potential treatment options. The currently available frameworks, however, are widely diverse in their approaches. This inconsistency can lead to variable evaluations of treatments. Our models can determine the likely value assessment outcome based on value frameworks such as the ones developed by the American Society of Clinical Oncology, the American College of Cardiology/American Heart Association, the Institute for Clinical and Economic Review, the National Comprehensive Cancer Network, and Memorial Sloan Kettering Cancer Center, among others.
HTA and health economic advisory boards
Advisory boards are a very useful tool to understand the placement of the new intervention in the treatment pathway, the acceptability of the available evidence as well as the acceptability of the methods used to analyse/summarise/compare the available evidence.
We can support the conduct of advisory boards undertaken in connection with any of our ongoing projects. We can develop pre-read and presentation materials and identify potential HTA, health economic and clinical experts who can provide valuable external validation and comment, as well as leading the discussions.
We offer trainings from introductory level to advanced topics related to health technology assessment, including training on HTA basics, evidence generation plans, clinical trial design, many aspects related to the analysis of trials and observational data, quantitative questions on evidence synthesis and modelling.
Bespoke training on-site
We are happy to tailor our trainings to your needs. Please contact us to discuss what topic would be of interest, and we can develop customised trainings to meet your exact needs and requirements.