Medication adherence: tailoring the analysis to the data

Abstract

The purpose of this paper is to explore more comprehensive methods to analyze antiretroviral non-adherence data. Using illustrative data and simulations, we investigated the value of using binary logistic regression (LR; dichotomized at 0% non-adherence) versus a hurdle model (combination of LR plus generalized linear model for >0% non-adherence) versus a zero-inflated negative binomial (ZINB) model (simultaneously modeling 0% non-adherence and >0% non-adherence). In simulation studies, the hurdle and ZINB models had similar power but both had higher power in comparison to LR alone. The hurdle model had higher power than ZINB in settings where covariate effects were restricted to one or the other part of the model (0% non-adherence or degree of non-adherence). Use of the hurdle and ZINB models are powerful and valuable approaches in analyzing adherence data which yield a more complete picture than LR alone. We recommend adoption of this methodology for future antiretroviral adherence research.

Figure 1

Probability of type I error with logistic, hurdle, and ZINB models in the Sample Size Scenario using samples sizes 200 to 3,000. Dark green color () corresponds to results using data generated under the hurdle model, orange color () represents results using data generated under the ZINB model. Long dashes () are results of the LR analysis, short dashes () are results of the hurdle model analysis with the bonferroni test, short dash dots () are results of the hurdle model analysis with the omnibus test, and solid lines () are results of the ZINB model analysis.

Figure 2

Comparison of power between logistic, hurdle, and ZINB models in the Sample Size Scenario using samples sizes 200 to 3,000. Dark green color () corresponds to results using data generated under the hurdle model, orange color () represents results using data generated under the ZINB model. Long dashes () are results of the LR analysis, short dashes () are results of the hurdle model analysis with the bonferroni test, short dash dots () are results of the hurdle model analysis with the omnibus test, and solid lines () are results of the ZINB model analysis.