title: Identifying patient subgroups who benefit most from a treatment: using administrative claims data to uncover treatment heterogeneity.
abstract: OBJECTIVE
  To illustrate how claims data can be used to (1) develop outcome scores that predict response to a traditional treatment and (2) estimate the economic impact of individualized assignment to a newer treatment based on the outcome score. An example application is based on two treatments for attention deficit hyperactivity disorder (ADHD): osmotic-release oral system methylphenidate (OROS-MPH) and lisdexamfetamine dimesylate (LDX).
  
  
  METHODS
  Adolescents with ADHD initiating OROS-MPH (n=6320) or LDX (n=6394) were selected from the MarketScan claims database. A model was developed for predicting risk of switching/augmentation with OROS-MPH using multiple baseline characteristics. The model was applied to an independent sample to stratify patients by their predicted risk and, within each stratum, risk of switching/augmentation and ADHD-related total costs were compared between OROS-MPH and LDX patients using inverse probability of treatment weighting.
  
  
  RESULTS
  The prediction model resulted in substantial stratification, showing risk of switching/augmentation with OROS-MPH ranging from 11.3-42.1%. In the two strata where OROS-MPH had highest risk of switching/augmentation, LDX had significantly lower risk of switching/augmentation than OROS-MPH (by 7.0-8.2%) and lower ADHD-related annual total costs (by $264-$625 per patient).
  
  
  CONCLUSIONS
  The current study has used the risk of switching/augmentation as a proxy measure for treatment efficacy to establish the prediction model. Future research using a clinical measure for ADHD symptoms is warranted to verify the findings.
  
  
  CONCLUSIONS
  Combining multiple patient characteristics into a predicted score for treatment outcomes with a traditional treatment can help identify subgroups of patients who benefit most from a new treatment. In this analysis, ADHD patients with a high predicted score for switching/augmentation with OROS-MPH had a lower rate of switching/augmentation with LDX. Assigning OROS-MPH and LDX treatments based on the predicted scores that are heterogeneous in a patient population may help improve clinical outcomes and the cost-effectiveness of care.
authors.affiliation: Shire Development LLC, Wayne, PA, USA.
authors.forename: M Haim
authors.initials: MH
authors.lastname: Erder
authors.forename: James E
authors.initials: JE
authors.lastname: Signorovitch
authors.forename: Juliana
authors.initials: J
authors.lastname: Setyawan
authors.forename: Hongbo
authors.initials: H
authors.lastname: Yang
authors.forename: Kejal
authors.initials: K
authors.lastname: Parikh
authors.forename: Keith A
authors.initials: KA
authors.lastname: Betts
authors.forename: Jipan
authors.initials: J
authors.lastname: Xie
authors.forename: Paul
authors.initials: P
authors.lastname: Hodgkins
authors.forename: Eric Q
authors.initials: EQ
authors.lastname: Wu
mesh_terms: D000293:Adolescent; D001289:Attention Deficit Disorder with Hyperactivity / Q000188:drug therapy*; D000697:Central Nervous System Stimulants / Q000008:administration & dosage / Q000191:economics* / Q000627:therapeutic use*; D057186:Comparative Effectiveness Research / Q000379:methods; D003365:Costs and Cost Analysis; D003913:Dextroamphetamine / Q000008:administration & dosage / Q000191:economics / Q000627:therapeutic use*; D005260:Female; D006296:Health Services / Q000191:economics / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D000069478:Lisdexamfetamine Dimesylate; D008297:Male; D008774:Methylphenidate / Q000008:administration & dosage / Q000191:economics / Q000627:therapeutic use*; D015233:Models, Statistical
chemical_list: D000697:Central Nervous System Stimulants; D008774:Methylphenidate; D000069478:Lisdexamfetamine Dimesylate; D003913:Dextroamphetamine