Anthony A. Mangino

Collaborative biostatistics units within universities and academic medical centres operate under a wide range of different funding models; common to many of these models is the challenge of allocating time to activities that are not linked to a specific research project, such as professional development, mentorship and administrative tasks. The purpose of this paper is to describe a proposed model for ‘flexible funding’, that is, funding that is not linked to a specific research project, within a collaborative biostatistics unit and to detail the benefits and challenges associated with the proposed model. We present results from a qualitative study representing the perspectives of collaborative biostatisticians working under the proposed flexible funding model. In addition to providing examples of activities undertaken as part of time allocated to flexible funding, the qualitative results reveal several benefits of flexible funding both for a collaborative biostatistician (e.g., job satisfaction and professional development) and for the collaborative biostatistics unit as a whole (e.g., retention, process improvement, and leadership).

This study seeks to compare fixed and mixed effects models for the purposes of predictive classification in the presence of multilevel data. The first part of the study utilizes a Monte Carlo simulation to compare fixed and mixed effects logistic regression and random forests. An applied examination of the prediction of student retention in the public-use U.S. PISA data set was considered to verify the simulation findings. Results of this study indicate fixed effects models performed comparably with mixed effects models across both the simulation and PISA examinations. Results broadly suggest that researchers should be cognizant of the type of predictors and data structure being used, as these factors carried more weight than did the model type.