title: A review and meta-analysis of utility values for lung cancer.
abstract: BACKGROUND
  Published utility estimates for lung cancer are plentiful and vary greatly. The reason for this variability is unclear, but may result from differences in the methods used to elicit each utility.
  
  
  OBJECTIVE
  To identify a set of pooled lung cancer utility estimates reflective of the available literature and determine which methodological factors significantly influence the value of lung cancer utility.
  
  
  METHODS
  Searches of PubMed, the NHS Economic Evaluation Database, and the Cost Effectiveness Analysis Registry from the Center for the Evaluation of Value and Risk in Health.
  
  
  METHODS
  English-language studies were included if they presented at least one previously unpublished lung cancer utility value, noted the elicitation technique and utility value provider.
  
  
  METHODS
  Two trained readers independently reviewed each article and extracted information for analysis. A hierarchical linear model (HLM) was used to perform a meta-regression with cancer stage, lower bound of scale, upper bound of scale, respondent, elicitation method, and lung cancer subtype as explanatory variables.
  
  
  RESULTS
  . Twenty-three articles containing 223 unique utility values were included. Lung cancer stage and subtype, the upper bound label of the utility scale, and respondent identity were significant predictors of utility (P < 0.05), while the lower bound label of utility scale was not. The HLM provided a set of pooled utility values for metastatic (0.57), mixed or nonspecified stage (0.77), and nonmetastatic lung cancer (0.87)-for the case of standard gamble as method, patients as respondents, non-small-cell lung cancer and scale labeled death to perfect health.
  
  
  CONCLUSIONS
  Methodological factors significantly affect lung cancer utilities; therefore, analysts should avoid direct comparisons of lung cancer utility values elicited with dissimilar methods.
authors.affiliation: Office of Policy, Economics and Innovation, US Environmental Protection Agency, Washington, DC, USA. sturza.julie@epa.gov
authors.forename: Julie
authors.initials: J
authors.lastname: Sturza
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000191:economics / Q000523:psychology*; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D006801:Humans; D016014:Linear Models; D008175:Lung Neoplasms / Q000191:economics / Q000523:psychology*; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D012044:Regression Analysis; D018570:Risk Assessment; D013222:State Medicine; D006113:United Kingdom