title: An evaluation of three statistical estimation methods for assessing health policy effects on prescription drug claims.
abstract: BACKGROUND
  While the choice of analytical approach affects study results and their interpretation, there is no consensus to guide the choice of statistical approaches to evaluate public health policy change.
  
  
  OBJECTIVE
  This study compared and contrasted three statistical estimation procedures in the assessment of a U.S. Food and Drug Administration (FDA) suicidality warning, communicated in January 2008 and implemented in May 2009, on antiepileptic drug (AED) prescription claims.
  
  
  METHODS
  Longitudinal designs were utilized to evaluate Oklahoma (U.S. State) Medicaid claim data from January 2006 through December 2009. The study included 9289 continuously eligible individuals with prevalent diagnoses of epilepsy and/or psychiatric disorder. Segmented regression models using three estimation procedures [i.e., generalized linear models (GLM), generalized estimation equations (GEE), and generalized linear mixed models (GLMM)] were used to estimate trends of AED prescription claims across three time periods: before (January 2006-January 2008); during (February 2008-May 2009); and after (June 2009-December 2009) the FDA warning.
  
  
  RESULTS
  All three statistical procedures estimated an increasing trend (P < 0.0001) in AED prescription claims before the FDA warning period. No procedures detected a significant change in trend during (GLM: -30.0%, 99% CI: -60.0% to 10.0%; GEE: -20.0%, 99% CI: -70.0% to 30.0%; GLMM: -23.5%, 99% CI: -58.8% to 1.2%) and after (GLM: 50.0%, 99% CI: -70.0% to 160.0%; GEE: 80.0%, 99% CI: -20.0% to 200.0%; GLMM: 47.1%, 99% CI: -41.2% to 135.3%) the FDA warning when compared to pre-warning period.
  
  
  CONCLUSIONS
  Although the three procedures provided consistent inferences, the GEE and GLMM approaches accounted appropriately for correlation. Further, marginal models estimated using GEE produced more robust and valid population-level estimations.
authors.affiliation: Department of Pharmacy, Clinical and Administrative Sciences, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. Electronic address: manish.mittal@abbvie.com.
authors.forename: Manish
authors.initials: M
authors.lastname: Mittal
authors.affiliation: Department of Pharmacy, Clinical and Administrative Sciences, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
authors.forename: Donald L
authors.initials: DL
authors.lastname: Harrison
authors.affiliation: Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Thompson
authors.affiliation: Department of Pharmacy, Clinical and Administrative Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Miller
authors.affiliation: Department of Pharmacy, Clinical and Administrative Sciences, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Farmer
authors.affiliation: Department of Neurology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
authors.forename: Yu-Tze
authors.initials: YT
authors.lastname: Ng
mesh_terms: D000293:Adolescent; D000328:Adult; D000927:Anticonvulsants / Q000627:therapeutic use; D003627:Data Interpretation, Statistical; D004827:Epilepsy / Q000188:drug therapy; D005260:Female; D006291:Health Policy / Q000639:trends*; D006801:Humans; D007356:Insurance, Pharmaceutical Services / Q000706:statistics & numerical data*; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data*; D001523:Mental Disorders / Q000188:drug therapy; D008875:Middle Aged; D009825:Oklahoma; D055553:Prescription Drugs; D012959:Socioeconomic Factors; D014481:United States; D055815:Young Adult
chemical_list: D000927:Anticonvulsants; D055553:Prescription Drugs
keywords: Antiepileptic drugs; FDA alert; Generalized estimation equations; Generalized linear mixed models; Generalized linear model