01: A marginalized zero- and N-inflated binomial regression model for fractional outcomes

Thomas M. Menino Convention & Exhibition Center 

In health outcomes research, many clinical endpoints are fractional outcomes bounded in the [0,1] interval, such as medication adherence, measured as the proportion of prescribed doses taken. In many cases, fractional outcome distributions exhibit a high proportion of 0s and 1s, corresponding with non-engagement and consistent engagement, respectively. We developed a marginalized zero- and N-inflated binomial (MZNIB) regression model to capture a mixture distribution comprising structural 0s and 1s, and a binomial component for intermediate outcomes. Covariates in the MZNIB model are linked to the marginal mean via logistic regression, yielding straightforward, population-average interpretations. We developed score-based estimating equations derived from a working likelihood to estimate model parameters. Statistical inference can be made using a modified bootstrap approach, in which p-values are derived by inverting percentile bootstrap confidence intervals. Numerical studies were conducted to assess the feasibility and validity of the MZNIB model, which demonstrated good performance across varying data conditions. Real-data analyses also demonstrated satisfactory performance.

fractional outcome

marginal mean

floor and ceiling effect

marginalized zero- and N-inflated binomial regression model

mixture distribution 

Biometrics Section