OBJECTIVES

: Newly diagnosed patients with chronic lymphocytic leukaemia (CLL) and 17p deletion and/or TP53 mutation are subpopulations with a high risk of disease progression and poor clinical prognosis. Robust clinical evidence for the target population is the key to gain HTA bodies’ acceptance of a new product. However, for subpopulations with high unmet medical needs, some HTA bodies may be more willing to accept increased clinical data uncertainty. This study aimed to assess the willingness of HTA bodies to generalize study outcomes for subpopulations with high unmet medical needs and limited clinical data, focusing on patients with CLL and specific mutations.

METHODS

: The approach to clinical data generalizability was assessed for HTA bodies in the UK, France, Germany, Spain, and Sweden based on HTA reports for selected CLL treatments (Zydelig^®, Imbruvica^® and Venclyxto^®).

RESULTS

: In total 18 HTA reports were analysed. Key clinical evidence submitted for Venclyxto^® included two open-label, non-comparative phase-II studies. One randomised, open-label, active comparator-controlled, phase-III study was submitted for Imbruvica^®. Evidence submitted for Zydelig^® included one randomized, double-blind, placebo-controlled, phase-III study. Key submitted studies included almost exclusively relapsed/refractory patients. However for all drugs, the target population included not only relapsed/refractory patients but also untreated patients with gene mutations. Thus, there was a gap between the target population and available data. Nevertheless, agencies in the UK, Sweden, and Spain accepted the subpopulation. Clinical outcomes generalizability was usually justified by indicating high unmet medical needs in these patients. HTA bodies in France and Germany were less willing to accept high uncertainty in clinical data and usually restricted the use in this subpopulation.

CONCLUSIONS

: Higher acceptance for uncertainty could be more noticeable in agencies using economic assessment rather than in those with clinical-driven evaluation. Health economic frameworks adopt numerous assumptions and modelling and thus offer more flexibility for the assessment of additional uncertainty.