title: Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques.
abstract: OBJECTIVE
  Despite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction.
  
  
  METHODS
  We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin.
  
  
  METHODS
  A total of 643 variables were identified at baseline from prior claims and linked Census data. In addition, we identified three postbaseline predictors, indicators of adherence to statins during each of the first 3 months of follow-up. We estimated 10 models predicting subsequent adherence, using logistic regression and boosted logistic regression, a nonparametric data-mining technique. Models were also estimated within strata defined by the index days supply.
  
  
  RESULTS
  In 77,703 statin initiators, prediction using baseline variables only was poor with maximum cross-validated C-statistics of 0.606 and 0.577 among patients with index supply ≤30 days and >30 days, respectively. Using only indicators of initial statin adherence improved prediction accuracy substantially among patients with shorter initial dispensings (C = 0.827/0.518), and, when combined with investigator-specified variables, prediction accuracy was further improved (C = 0.842/0.596).
  
  
  CONCLUSIONS
  Observed adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short.
authors: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
authors: Jessica M
authors: http://orcid.org/0000-0002-8941-4116
authors: JM
authors: Franklin
authors: CVS Caremark, Woonsocket, RI.
authors: William H
authors: WH
authors: Shrank
authors: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
authors: Joyce
authors: J
authors: Lii
authors: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
authors: Alexis K
authors: AK
authors: Krumme
authors: CVS Caremark, Woonsocket, RI.
authors: Olga S
authors: OS
authors: Matlin
authors: CVS Caremark, Woonsocket, RI.
authors: Troyen A
authors: TA
authors: Brennan
authors: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
authors: Niteesh K
authors: NK
authors: Choudhry
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D003072:Cognition Disorders / Q000453:epidemiology; D005260:Female; D006304:Health Status; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000008:administration & dosage* / Q000627:therapeutic use; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D015233:Models, Statistical; D019338:Polypharmacy; D057216:Propensity Score; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012737:Sex Factors; D012959:Socioeconomic Factors; D013997:Time Factors; D014481:United States; D016903:Drug Monitoring
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors
keywords: Adherence; boosting; comparative effectiveness; epidemiologic methods; prediction