title: Propensity score matching: an illustrative analysis of dose response.
abstract: BACKGROUND
  Health services researchers are often interested in the effect of a treatment or a service in situations in which randomization is difficult or impossible. One useful alternative involves propensity score methods, a means for matching members of different groups based on a range of characteristics. Under certain assumptions, comparisons of the matched groups reveal the impact of the treatment of interest.
  
  
  OBJECTIVE
  This article reviews propensity score methods and illustrates their use in an analysis of dose response, the relationship between the volume of services received, and treatment outcomes. In mental health policy, this question is central to key issues such as parity.
  
  
  METHODS
  Data for the illustrative analysis are taken from a well-known study of children's mental health services. This analysis estimates the impact of outpatient therapy based on comparisons of individuals receiving different treatment doses. Those comparisons are adjusted for preexisting observed differences among the groups using propensity score methods.
  
  
  METHODS
  The study includes 301 participants aged 5 to 18 years treated at the study sites.
  
  
  METHODS
  The analyses are based on family characteristics and the mental health status of children and adolescents reported in interviews with parents as well as administrative data on service use.
  
  
  RESULTS
  Analyses using propensity score matching suggest that added services improve treatment outcomes, especially child functioning. However, at least for the services and outcomes considered, the marginal benefits to high levels of treatment are limited.
  
  
  CONCLUSIONS
  These analyses illustrate the potential value of propensity score methods to health services researchers.
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008605:Mental Health Services / Q000706:statistics & numerical data*; D009657:North Carolina; D010043:Outcome and Process Assessment, Health Care / Q000379:methods*; D011336:Probability