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. 
Proportion of Days Covered
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.  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.  We also provide a recommendation for defining the observation window in calculating PDC.
PDC
We also performed simulation studies to investigate the statistical properties of three statistical models: logistic regression, negative binomial, and ordinal logistic regression models.


 In simulation studies, logistic regression had lower power than ordinal logistic and negative binomial 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. 
logistic regression
We also performed simulation studies to investigate the statistical properties of three statistical models: logistic regression, negative binomial, and ordinal logistic regression models.


 In simulation studies, logistic regression had lower power than ordinal logistic and negative binomial regression models.  The negative binomial model offers advantages in modeling adherence data, as it avoids the problematic use of a ratio in regression models. 
negative binomial
We also performed simulation studies to investigate the statistical properties of three statistical models: logistic regression, negative binomial, and ordinal logistic 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. 
ordinal logistic regression