Comparing Campus Discipline Rates: A Multivariate Approach for Identifying Schools with Significantly Different than Expected Exclusionary Discipline Rates

Campus behavior management is important for ensuring classroom order and promoting positive academic outcomes. Previous studies have shown the importance of individual student and campus personnel characteristics and campus context for explaining campus discipline rates (e.g., rates of suspension and expulsion). Assessing campus discipline rates, while controlling for these individual and campus characteristics, is important for the monitoring, evaluation, and intervention role of policymakers as well as state and federal level education agencies. Systems or metrics exist that measure other student outcomes (i.e., academic performance) with controls for individual and campus characteristics, but none exist that monitor these differences for discipline rates across campuses. In this paper, we use a multivariate model to analyze a longitudinal, statewide dataset for all secondary students in Texas from 2000 to 2008 in order to examine how campus discipline rates differ across schools with statistically similar students, teachers, and campus characteristics. The findings are important for understanding that some schools with similar characteristics have significantly different exclusionary discipline rates, and they are important for informing policy and agency level decision-making. The methodology described can easily be used by monitoring agencies as well as local school districts.

Comparative Disciplinary Rates as a Tool for Reducing Exclusionary Discipline and Eliminating the School to Prison Pipeline

Invited Commentary on "Comparing Campus Discipline Rates: A Multivariate Approach for Identifying Schools with Significantly Different than Expected Exclusionary Discipline Rates" by Eric Booth and colleagues.

Long-term central venous catheter-related candidemia: A new approach towards diagnosis and management

A case-series analysis of approximately 811 cancer patients who developed Candidemia between 1989 and 1998 and seen at M. D. Anderson Cancer Center, was studied to assess the impact and timing of central venous catheter (CVC) removal on the outcome of fungal bloodstream infections in cancer patients with primary catheter-related Candidemia as well as secondary infections. ^ This study explored the diagnosis and the management of vascular catheter-associated fungemia in patients with cancer. The microbiologic and clinical factors were determined to predict catheter-related Candidemia. Those factors included, in addition to basic demographics, the underlying malignancy, chemotherapy, neutropenia, and other salient data. Statistical analyses included univariate and multivariate logistic regression to determine the outcome of Candidemia in relation to the timing of catheter removal, type of species, and to identify predictors of catheter-related infections. ^ The conclusions of the study aim at enhancing our mastery of issues involving CVC removal and potentially will have an impact on the management of nosocomial bloodstream infections related to timing of CVC removal and the optimal duration of treatment of catheter-related Candidemia. ^

A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^