title: Long-term medical costs and life expectancy of acute myeloid leukemia: a probabilistic decision model.
abstract: BACKGROUND
  Acute myeloid leukemia (AML) can be diagnosed at any age and treatment, which can be given with supportive and/or curative intent, is considered expensive compared with that for other cancers. Despite this, no long-term predictive models have been developed for AML, mainly because of the complexities associated with this disease.
  
  
  OBJECTIVE
  The objective of the current study was to develop a model (based on a UK cohort) to predict cost and life expectancy at a population level.
  
  
  METHODS
  The model developed in this study combined a decision tree with several Markov models to reflect the complexity of the prognostic factors and treatments of AML. The model was simulated with a cycle length of 1 month for a time period of 5 years and further simulated until age 100 years or death. Results were compared for two age groups and five different initial treatment intents and responses. Transition probabilities, life expectancies, and costs were derived from a UK population-based specialist registry-the Haematological Malignancy Research Network (www.hmrn.org).
  
  
  RESULTS
  Overall, expected 5-year medical costs and life expectancy ranged from £8,170 to £81,636 and 3.03 to 34.74 months, respectively. The economic and health outcomes varied with initial treatment intent, age at diagnosis, trial participation, and study time horizon. The model was validated by using face, internal, and external validation methods. The results show that the model captured more than 90% of the empirical costs, and it demonstrated good fit with the empirical overall survival.
  
  
  CONCLUSIONS
  Costs and life expectancy of AML varied with patient characteristics and initial treatment intent. The robust AML model developed in this study could be used to evaluate new diagnostic tools/treatments, as well as enable policy makers to make informed decisions.
authors.affiliation: Epidemiology & Cancer Statistics Group, University of York, York, UK. Electronic address: Han-I.Wang@ecsg.york.ac.uk.
authors.forename: Han-I
authors.initials: HI
authors.lastname: Wang
authors.affiliation: Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.
authors.forename: Eline
authors.initials: E
authors.lastname: Aas
authors.affiliation: Epidemiology & Cancer Statistics Group, University of York, York, UK.
authors.forename: Debra
authors.initials: D
authors.lastname: Howell
authors.affiliation: Epidemiology & Cancer Statistics Group, University of York, York, UK.
authors.forename: Eve
authors.initials: E
authors.lastname: Roman
authors.affiliation: Queens Centre for Oncology and Haematology, Castle Hill Hospital, Hull, UK.
authors.forename: Russell
authors.initials: R
authors.lastname: Patmore
authors.affiliation: Haematological Malignancy Diagnostic Service, St James's University Hospital, Leeds, UK.
authors.forename: Andrew
authors.initials: A
authors.lastname: Jack
authors.affiliation: Epidemiology & Cancer Statistics Group, University of York, York, UK.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Smith
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D003663:Decision Trees; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000191:economics / Q000473:pathology / Q000628:therapy*; D008017:Life Expectancy; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D011379:Prognosis; D012042:Registries; D013534:Survival; D013997:Time Factors; D006113:United Kingdom; D055815:Young Adult
keywords: acute myeloid lymphoma; costs; decision analytic model; life expectancy