title: An evaluation of exact matching and propensity score methods as applied in a comparative effectiveness study of inhaled corticosteroids in asthma.
abstract: BACKGROUND
  Cohort matching and regression modeling are used in observational studies to control for confounding factors when estimating treatment effects. Our objective was to evaluate exact matching and propensity score methods by applying them in a 1-year pre-post historical database study to investigate asthma-related outcomes by treatment.
  
  
  METHODS
  We drew on longitudinal medical record data in the PHARMO database for asthma patients prescribed the treatments to be compared (ciclesonide and fine-particle inhaled corticosteroid [ICS]). Propensity score methods that we evaluated were propensity score matching (PSM) using two different algorithms, the inverse probability of treatment weighting (IPTW), covariate adjustment using the propensity score, and propensity score stratification. We defined balance, using standardized differences, as differences of <10% between cohorts.
  
  
  RESULTS
  Of 4064 eligible patients, 1382 (34%) were prescribed ciclesonide and 2682 (66%) fine-particle ICS. The IPTW and propensity score-based methods retained more patients (96%-100%) than exact matching (90%); exact matching selected less severe patients. Standardized differences were >10% for four variables in the exact-matched dataset and <10% for both PSM algorithms and the weighted pseudo-dataset used in the IPTW method. With all methods, ciclesonide was associated with better 1-year asthma-related outcomes, at one-third the prescribed dose, than fine-particle ICS; results varied slightly by method, but direction and statistical significance remained the same.
  
  
  CONCLUSIONS
  We found that each method has its particular strengths, and we recommend at least two methods be applied for each matched cohort study to evaluate the robustness of the findings. Balance diagnostics should be applied with all methods to check the balance of confounders between treatment cohorts. If exact matching is used, the calculation of a propensity score could be useful to identify variables that require balancing, thereby informing the choice of matching criteria together with clinical considerations.
authors.affiliation: Observational and Pragmatic Research Institute Pte Ltd, Singapore.
authors.forename: Anne
authors.initials: A
authors.lastname: Burden
authors.affiliation: University Paris Descartes (EA2511), Cochin Hospital Group (AP-HP), Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Roche
authors.affiliation: Observational and Pragmatic Research Institute Pte Ltd, Singapore.
authors.forename: Cristiana
authors.initials: C
authors.lastname: Miglio
authors.affiliation: Observational and Pragmatic Research Institute Pte Ltd, Singapore.
authors.forename: Elizabeth V
authors.initials: EV
authors.lastname: Hillyer
authors.affiliation: Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen.
authors.forename: Dirkje S
authors.initials: DS
authors.lastname: Postma
authors.affiliation: PHARMO Institute for Drug Outcomes Research, Utrech, the Netherlands.
authors.forename: Ron Mc
authors.initials: RM
authors.lastname: Herings
authors.affiliation: PHARMO Institute for Drug Outcomes Research, Utrech, the Netherlands.
authors.forename: Jetty A
authors.initials: JA
authors.lastname: Overbeek
authors.affiliation: Takeda Development Centre Europe Ltd, London, UK.
authors.forename: Javaria Mona
authors.initials: JM
authors.lastname: Khalid
authors.affiliation: Takeda Pharmaceuticals International GmbH, Zurich, Switzerland.
authors.forename: Daniela
authors.initials: D
authors.lastname: van Eickels
authors.affiliation: Observational and Pragmatic Research Institute Pte Ltd, Singapore; Academic Primary Care, University of Aberdeen, Aberdeen, UK.
authors.forename: David B
authors.initials: DB
authors.lastname: Price
keywords: asthma; exact matching; observational; propensity score