A LANDSCAPE ASSESSMENT OF THE STATISTICAL AND ECONOMIC MODELING OF CURE IN ONCOLOGY
Author(s)
Elizabeth Wehler, MPH1, Hongtao Zhang, PhD2, Shujing (Shirley) Zhang, PhD3, Annabelle Davies, MSc4, Ying Xiao, MS4, Ruifeng Xu, PhD2, Grant McCarthy, MS4;
1Merck & Co., Inc., West Point, PA, USA, 2Merck & Co., Inc., North Wales, PA, USA, 3Merck & Co., Inc., Rahway, NJ, USA, 4MSD, London, United Kingdom
1Merck & Co., Inc., West Point, PA, USA, 2Merck & Co., Inc., North Wales, PA, USA, 3Merck & Co., Inc., Rahway, NJ, USA, 4MSD, London, United Kingdom
OBJECTIVES: Economic modeling of cure is increasingly common in literature and health technology assessment (HTA) as curative therapies become more feasible in oncology. Robust clinical evidence and modeling methods are required as long-term follow-up of active trial arms are unavailable at time of launch. This research explores definitions, methodologies, requirements and limitations of modeling cure in oncology.
METHODS: A structured targeted literature review was conducted to identify 1) definitions of cure; 2) cure frameworks and outcomes; 3) cure modeling methods; and 4) National Institute of Health Care and Excellence (NICE) acceptance of cure modeling methodologies as a case example, focusing on recent appraisals (May 2020 to May 2025).
RESULTS: Cure is defined differently by stakeholder, including patient-level and population-level perspectives. Economic models consider overall survival modeling frameworks including 1. Relative survival; 2. Disease-specific survival and 3. All-cause survival. Cure modeling can also be applied to outcomes including progression-free survival, recurrence-free survival and disease-free survival, reducing confounding of subsequent treatments. Statistical cure modeling can be directly implemented in economic models (explicit cure) using 1. mixture cure modeling (MCM) (explicit modeling of cured and uncured groups) and 2. non-mixture cure modeling (NMCM) (modeled hazards converge with general population). Alternatively, time to cure and risk reduction can be applied to parametric survival estimates in the economic model (implicit cure). Among the 47 NICE submissions including cure modeling, implicit cure modeling was most common (68%), followed by MCM (19%), with ~ 91% of submissions utilizing an all-cause survival framework. Clinical plausibility and validation of cure assumptions are critical for HTA agency acceptance, including sufficient follow-up, HTA precedent and external validation.
CONCLUSIONS: As economic modeling of cure becomes more relevant and accepted in oncology HTA, the inherent uncertainty and limited trial follow-up of interventions with a propensity for cure can be mitigated through robust methods and evidence.
METHODS: A structured targeted literature review was conducted to identify 1) definitions of cure; 2) cure frameworks and outcomes; 3) cure modeling methods; and 4) National Institute of Health Care and Excellence (NICE) acceptance of cure modeling methodologies as a case example, focusing on recent appraisals (May 2020 to May 2025).
RESULTS: Cure is defined differently by stakeholder, including patient-level and population-level perspectives. Economic models consider overall survival modeling frameworks including 1. Relative survival; 2. Disease-specific survival and 3. All-cause survival. Cure modeling can also be applied to outcomes including progression-free survival, recurrence-free survival and disease-free survival, reducing confounding of subsequent treatments. Statistical cure modeling can be directly implemented in economic models (explicit cure) using 1. mixture cure modeling (MCM) (explicit modeling of cured and uncured groups) and 2. non-mixture cure modeling (NMCM) (modeled hazards converge with general population). Alternatively, time to cure and risk reduction can be applied to parametric survival estimates in the economic model (implicit cure). Among the 47 NICE submissions including cure modeling, implicit cure modeling was most common (68%), followed by MCM (19%), with ~ 91% of submissions utilizing an all-cause survival framework. Clinical plausibility and validation of cure assumptions are critical for HTA agency acceptance, including sufficient follow-up, HTA precedent and external validation.
CONCLUSIONS: As economic modeling of cure becomes more relevant and accepted in oncology HTA, the inherent uncertainty and limited trial follow-up of interventions with a propensity for cure can be mitigated through robust methods and evidence.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
MSR167
Topic
Methodological & Statistical Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Oncology