Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques

ObjectiveDespite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction. Data SourcesWe identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin. Study DesignA 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 3months 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. ResultsIn 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 30days and >30days, 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). ConclusionsObserved adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short.