Driving the optimal evidence strategy for prescription drug pricing and reimbursement

How patient preference studies can add value to your dossier in Europe.
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Katherine Young

Senior Consultant, Global Market Access, Kantar Health

Genevieve Bonnelye

Global Head RWE & Consulting, Kantar Health

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Due to the lack of guidance on how to optimise evidence demonstration and systematically incorporate quantitative patient preference studies in Health Technology Assessment (HTA), Kantar Health conducted a study aimed to investigate how patient preference data may be supplied, its relevance in HTA, and how it has been valued in UK, France and Germany.

Three case studies were identified with the aim of identifying differences in the type and use of patient preference data, how preference data is developed, and for which purpose across indications.

The case studies covered oncology, rare metabolic, and infectious disease indications. In all cases, patient preference studies were incorporated in phase 3 trials and qualitative methods were used by means of treatment preference or satisfaction questionnaires. The attributes assessed included efficacy, side effects, mode of administration, convenience, and overall satisfaction. All patient preference data supported the primary or secondary endpoints, and QoL data. In the HTA reports, patient preference data were not assessed to replace neither clinical nor economic evidence. In contrary, it provided complementary insight on the relevance of the clinical outcomes in the daily life of a patient who suffers from the disease.

The relevance of patient preference data may rest on its purpose in how it supports the overall value proposition and the evidence base of the treatment that is relevant to the patient. Through providing complementary data that only a patient who suffers the disease knows, it may help HTA committees in evaluating, interpreting and deliberating the clinical and economic evidence in reimbursement decision making.

Manufacturers should not delay the preference of patients until after launch. Planning and implementation should happen as early as Phase 2-3, with appropriate and robust methodologies for measuring and linking data to validated clinical and economic outcomes.

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