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.

Data sources: We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin.

Study design: 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.

Keywords: Adherence; boosting; comparative effectiveness; epidemiologic methods; prediction.

Figure 1

Study Design

Figure 2

The Number of Patients with Each Initial Adherence Pattern and the Percent of Patients with Each Pattern That Were Fully Adherent during Days 1–365 (1‐year Adherence) or 91–365 (Delayed Adherence) after Statin Initiation, Separately by Index Days Supply