Medication non-adherence impacts public health by impeding the evaluation of medication efficacy, decreasing improvement and/or increasing morbidity in patients, while increasing health care costs. As a result, intervention studies are designed to improve adherence rates. Medication adherence is J-shaped in nature with many people taking their medication completely, a significant proportion taking no medication, and a substantial proportion taking their medication on some intermittent schedule. Therefore, descriptive statistics and standard statistical techniques (e.g., parametric t-tests, non-parametric Wilcoxon Rank Sum tests, and dichotomization) can provide misleading results. This study developed and evaluated a method to more accurately assess interventions designed to improve adherence. Better evaluation could lead to identifying new interventions that decrease morbidity, mortality, and health care costs.Parametric techniques utilizing a Gaussian distribution are inappropriate as J-shaped adherence distributions violate the normality assumption and transformations fail to induce normality. Additionally, measures of central tendency fail to provide an adequate depiction of the distribution. While non-parametric techniques overcome distributional problems, they fail to adequately describe the distributions shape. Similarly, dichotomizing data results in a loss of information, making small improvements impossible to detect.Using a mixture of beta distributions to describe adherence measures and the expectation-maximization algorithm, parameter and standard error estimates of this distribution were produced. This technique is advantageous as it allows one to both describe the shape of the distribution and compare parameter estimates. We assessed, via simulation studies, α-levels and power for this new method as compared to standard methods. Additionally, we applied the technique to data obtained from studies designed to increase medication adherence in rheumatoid arthritis patients.Via simulations, the mixed beta model was shown to adequately depict adherence distributions. This technique performed better at distinguishing datasets, exhibiting power ranging from 66% to 92% across samples sizes. Additionally, α-levels for the new technique were reasonable, ranging from 3.4% to 5.4%. Finally, application to the Adherence in Rheumatoid Arthritis: Nursing Interventions studies produced parameters estimates and allowed for the comparison of interventions. The p-value for this new test was 0.0597, compared to 0.20 for the t-test.
