Statistical considerations for medication adherence research.

OBJECTIVE: Medication non-adherence is a widespread problem and has been known to be associated with worse health outcomes and increased healthcare costs. Although many measures of adherence have been developed, their usage is not consistent across studies. Furthermore, statistical methods for analyzing adherence measures have not been rigorously evaluated.
METHODS: Using Proportion of Days Covered (PDC), a commonly used adherence measure, we examine the variability inherent to study inclusion criteria and several variations of the PDC calculation method using a motivating data example. We illustrated via sensitivity analyses the potential for flawed inference when modeling PDC as an outcome measure. We also performed simulation studies to investigate the statistical properties of three statistical models: logistic regression, negative binomial, and ordinal logistic regression models.
RESULTS: Our sensitivity analysis showed that parameter estimates can vary greatly depending on the rules for determining the study end date in calculating PDC, or the minimum number of fills in defining the cohort. In simulation studies, logistic regression had lower power than ordinal logistic and negative binomial regression models. Naivete to treatment was an important predictor of adherence and omitting it from statistical models can lead to inflated type I errors.
CONCLUSIONS: We discourage dichotomizing adherence data as it results in low power. The negative binomial model offers advantages in modeling adherence data, as it avoids the problematic use of a ratio in regression models. The ordinal logistic regression is robust to distributional assumptions with greater power, but naivete to treatment should be adjusted to reserve type I error rate. We also provide a recommendation for defining the observation window in calculating PDC.