OBJECTIVES: To promote the use of decision-analytic models with benefit-harm analysis to support decision-making in HTA processes Brazil.

METHODS: The health technology incorporation process in Brazil requires from the proponent, in addition to an economic evaluation study, an assessment of the available evidence on efficacy, effectiveness, accuracy and safety of the technologies, summarized as a Technical-Scientific Report or a Systematic Review. A limitation of those summaries is that they are linked to outcomes presented in primary clinical trials, which are conducted in controlled settings that not always represent real-world conditions and, consequently, may not have external validity. Decision-analytic modeling is a systematic approach to decision making under uncertainty, which uses mathematical relationships to synthesize a series of possible consequences arising from alternative technologies being evaluated. In addition to its applicability in economic evaluations, modeling techniques allows for the systematic synthesis and evaluation of benefits and harms from short-term clinical trials, long-term epidemiological studies, and real-world evidence over a time horizon that can exceed the follow-up periods in clinical studies. Therefore, we will develop a national guideline on how to conduct such studies in Brazil.

RESULTS: The guideline will present systematic processes for choosing and building the most appropriate model for each health problem. The processes for conducting benefit-harm analyses will be described in detail. Examples of real cases of decision-analytic models with benefit-harm analysis will also be presented. We also expected to present a novel framework and dossier content for the HTA submission processes in Brazil.

CONCLUSIONS: Modeling techniques allow to combine effectiveness evidence into a framework that approximates the actual health problem. Besides, the summary measure resulting from the benefit-harm analysis facilitate the interpretation of the technologies performance, as they connect the measures of effectiveness to the health consequences for the patient, taking into account the uncertainties.