title: Modeling hard clinical end-point data in economic analyses.
abstract: OBJECTIVE
  The availability of hard clinical end-point data, such as that on cardiovascular (CV) events among patients with type 2 diabetes mellitus, is increasing, and as a result there is growing interest in using hard end-point data of this type in economic analyses. This study investigated published approaches for modeling hard end-points from clinical trials and evaluated their applicability in health economic models with different disease features.
  
  
  METHODS
  A review of cost-effectiveness models of interventions in clinically significant therapeutic areas (CV diseases, cancer, and chronic lower respiratory diseases) was conducted in PubMed and Embase using a defined search strategy. Only studies integrating hard end-point data from randomized clinical trials were considered. For each study included, clinical input characteristics and modeling approach were summarized and evaluated.
  
  
  RESULTS
  A total of 33 articles (23 CV, eight cancer, two respiratory) were accepted for detailed analysis. Decision trees, Markov models, discrete event simulations, and hybrids were used. Event rates were incorporated either as constant rates, time-dependent risks, or risk equations based on patient characteristics. Risks dependent on time and/or patient characteristics were used where major event rates were >1%/year in models with fewer health states (<7). Models of infrequent events or with numerous health states generally preferred constant event rates.
  
  
  CONCLUSIONS
  The detailed modeling information and terminology varied, sometimes requiring interpretation.
  
  
  CONCLUSIONS
  Key considerations for cost-effectiveness models incorporating hard end-point data include the frequency and characteristics of the relevant clinical events and how the trial data is reported. When event risk is low, simplification of both the model structure and event rate modeling is recommended. When event risk is common, such as in high risk populations, more detailed modeling approaches, including individual simulations or explicitly time-dependent event rates, are more appropriate to accurately reflect the trial data.
authors.affiliation: Evidera, Bethesda , MD , USA.
authors.forename: Anuraag R
authors.initials: AR
authors.lastname: Kansal
authors.forename: Ying
authors.initials: Y
authors.lastname: Zheng
authors.forename: Roberto
authors.initials: R
authors.lastname: Palencia
authors.forename: Antonio
authors.initials: A
authors.lastname: Ruffolo
authors.forename: Bastian
authors.initials: B
authors.lastname: Hass
authors.forename: Sonja V
authors.initials: SV
authors.lastname: Sorensen
mesh_terms: D002318:Cardiovascular Diseases / Q000191:economics* / Q000453:epidemiology*; D003362:Cost-Benefit Analysis; D003924:Diabetes Mellitus, Type 2 / Q000191:economics* / Q000453:epidemiology*; D006801:Humans; D008390:Markov Chains; D018803:Models, Economic; D009369:Neoplasms / Q000191:economics / Q000453:epidemiology; D011322:Primary Prevention; D012107:Research Design