The Role of Quantitative Research in Health Technology Assessment

Health Technology Assessment (HTA) is a multidisciplinary process that systematically evaluates the clinical, economic, ethical and societal implications of introducing new health technologies. At its core, HTA aims to inform decision-making by providing comprehensive evidence on the potential impacts of adopting a new technology within a healthcare system. While HTA draws upon various research methodologies, quantitative research plays a pivotal role in assessing the clinical effectiveness and economic aspects of health technologies.

Assessing Clinical Effectiveness

Clinical effectiveness assessment lies at the heart of HTA, as it evaluates the ability of a new technology to improve health outcomes compared to the existing standard of care. Quantitative research methods, such as randomized controlled trials (RCTs), systematic reviews and meta-analyses, are crucial in this regard.

RCTs are considered the gold standard for evaluating the efficacy and safety of new interventions, as they minimize bias and provide robust evidence on the causal relationship between the intervention and observed outcomes. Systematic reviews and meta-analyses synthesize the results of multiple RCTs, increasing statistical power and providing more precise estimates of treatment effects. These quantitative approaches are particularly valuable in identifying and understanding differences in outcomes between the new technology and its comparator(s).

Differences between regulatory approval, HTA and patients regarding health technologies can be seen in the table below.

	Regulatory approval	HTA	Patient
Decision(s) to be made by the stakeholder	– Does the technology do more good than harm for patients with the defined target indication? – Should this technology be marketed?	– Does the technology offer useful, appropriate benefits for all or a select sub-group of patients in this healthcare system compared to what is most commonly used in the disease area? – Are the costs associated with the technology affordable and justified by its benefits?	– Is it effective? – What benefit and/or harm should I expect from taking it? – How does it compare to other treatments available? – How much will it cost to me? – How convenient is the treatment?
Type of evidence required	– Safety. – Efficacy. – Quality.	– Safety. – Effectiveness. – Economics and budgetary impact. – Social, ethical, legal, organisational impact.	– Safety. – Effectiveness.
Evidence considered	– (Pre-launch) Randomised controlled trials, with a standard-of-care or placebo comparator – (Post-launch) Safety/ pharmacovigilance (always), relative efficacy or effectiveness, when assessing a product’s benefit-risk profile in extended/long-term use.	– Randomised controlled trials, observational studies. – Systematic reviews of pertinent literature. – Relative effectiveness and costs, as assembled from trials or through analytic techniques such as meta-analysis, modelling.	– Personal and others’ experience. – Results from trials explained in lay language.
Validity	– Internal validity (can a causal conclusion be drawn without systematic bias?)	– External validity (can the results of a study be generalized to other situations and to other people?)	– Internal and external validity.
Outcomes	– Hard clinical endpoint outcomes. – Laboratory findings. – Surrogate outcomes. – Patient-relevant outcomes (increasingly).	– Quality of life. – Long-term clinical outcomes. – Patient-relevant outcomes.	– Outcomes relevant to me.
Comparator	– Standard-of-care medicinal product (active control), or – Placebo.	– Active control, ideally reflecting what might be replaced by the new technology.	– The best option available, or – What I am currently taking if switching to new medicine. – No treatment.
Time horizon	– Trial duration. – Post marketing studies. – Pharmacovigilance over the lifetime of a product.	– Lifetime; or at least the time needed to capture risks and benefits of treatment.	– Time horizon relevant to me.

Table 1: Adapted from: 1. Tsoi B, Masucci L, Campbell K, Drummond M, O’Reilly D, Goeree R. Harmonization of reimbursement and regulatory approval processes: a systematic review of international experiences. Expert Rev Pharmacoecon Outcomes Res. 2013

Moreover, quantitative research is instrumental in interpreting and valuing the observed differences in outcomes. Statistical techniques, such as confidence intervals, hypothesis testing and sensitivity analyses, help assess the magnitude, significance and robustness of these differences. Additionally, statistical modeling approaches, like mixed treatment comparisons and network meta-analyses, can indirectly compare treatments when head-to-head trials are lacking, expanding the evidence base for decision-making.

Economic Evaluation

In an era of constrained healthcare resources, economic evaluation is an integral component of HTA, as it assesses the cost-effectiveness of new technologies relative to existing alternatives. Quantitative research provides the foundational data for economic analyses, such as cost-effectiveness analyses (CEAs), cost-utility analyses (CUAs), and budget impact analyses (BIAs).

CEAs and CUAs typically rely on data from clinical trials, observational studies, and other quantitative sources to estimate the incremental costs and health outcomes associated with the new technology. These analyses often incorporate advanced techniques like decision-analytic modeling, probabilistic sensitivity analyses, and Markov models to synthesize evidence from multiple sources and account for uncertainty and variability in the data.

Budget impact analyses, which assess the financial implications of adopting a new technology within a healthcare system, heavily depend on quantitative data on factors such as the target population size, expected utilization rates, unit costs and resource use patterns. These analyses may also leverage epidemiological data, claims data and other quantitative sources to estimate the budgetary impact accurately.

Fostering Evidence-Based Decision-Making

By providing robust quantitative evidence on clinical and economic aspects, HTA equips decision-makers with the necessary information to make informed choices about the allocation of limited healthcare resources. This evidence-based approach promotes transparency, accountability, and the efficient use of resources, ultimately contributing to the sustainability and equity of healthcare systems.

Furthermore, quantitative research in HTA supports the development of guidelines and recommendations for clinical practice, reimbursement policies, and resource allocation decisions. By synthesizing and critically appraising the available quantitative evidence, HTA bodies can provide evidence-based guidance to healthcare providers, policymakers, and other stakeholders, fostering the responsible adoption and appropriate use of new health technologies.

In conclusion, quantitative research plays a crucial role in HTA by providing the empirical foundation for assessing the clinical effectiveness and economic implications of new health technologies. Through rigorous quantitative analyses, HTA can support evidence-based decision-making, promote the efficient allocation of resources, and contribute to the overall quality, equity and sustainability of healthcare systems.

Dr. David Schirmer I Head of Customer Success

He is passionate about supporting customers from the healthcare sector with digital solutions and optimizing their processes. He spends a lot of his free time with his family and his second passion, volleyball!

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