On generalised estimating equations for vector regression

Generalised estimating equations (GEE) for regression problems with vector‐valued responses are examined. When the response vectors are of mixed type (e.g. continuous–binary response pairs), the GEE approach is a semiparametric alternative to full‐likelihood copula methods, and is closely related to Prentice & Zhao's mean‐covariance estimation equations approach. When the response vectors are of the same type (e.g. measurements on left and right eyes), the GEE approach can be viewed as a ‘plug‐in’ to existing methods, such as the vglm function from the state‐of‐the‐art VGAM package in R. In either scenario, the GEE approach offers asymptotically correct inferences on model parameters regardless of whether the working variance–covariance model is correctly or incorrectly specified. The finite‐sample performance of the method is assessed using simulation studies based on a burn injury dataset and a sorbinil eye trial dataset. The method is applied to data analysis examples using the same two datasets, as well as to a trivariate binary dataset on three plant species in the Hunua ranges of Auckland.     

A powerful and efficient algorithm for breaking the links between aliased effects in asymmetric designs

Fractional factorial (FF) designs are no doubt the most widely used designs in experimental investigations due to their efficient use of experimental runs. One price we pay for using FF designs is, clearly, our inability to obtain estimates of some important effects (main effects or second order interactions) that are separate from estimates of other effects (usually higher order interactions). When the estimate of an effect also includes the influence of one or more other effects the effects are said to be aliased. Folding over an FF design is a method for breaking the links between aliased effects in a design. The question is, how do we define the foldover structure for asymmetric FF designs, whether regular or nonregular? How do we choose the optimal foldover plan? How do we use optimal foldover plans to construct combined designs which have better capability of estimating lower order effects? The main objective of the present paper is to provide answers to these questions. Using the new results in this paper as benchmarks, we can implement a powerful and efficient algorithm for finding optimal foldover plans which can be used to break links between aliased effects.     

Increasing Competency,Self-Confidence,and Connectedness Among Foster Care Alumni Entering a 4-Year University: Findings from an Early-Start Program

Research shows that few foster care alumni enroll and complete post-secondary education. For those who do enroll, many experience challenges associated with academic and social adjustment and are at risk of dropping out. Many college-based programs are being developed and are available to foster care alumni to support them during their post-secondary education. This study used pre-tests, post-tests, and journal entries of seventeen participants to evaluate a strengths-based, resilience-oriented early-start program for newly enrolled students at a large public university that provides knowledge about the institution, resources (e.g., tutoring), and social support necessary for foster care alumni to make a smooth transition into college life and to increase short and long-term student success. Quantitative and qualitative findings indicate that students who participated in the early-start program showed increased confidence and competency in academic and social adjustment. Findings also showed personal growth (e.g., building resiliency) and an increased connectedness among students who participated in the program. Study findings can inform the development of new on-campus support programs and help enhance existing programming. Future research should address short and long-term outcomes and experiences of students who are former foster youth who have participated in on-campus support programs.     

Improving the Efficacy of Administrative Data for Evaluation of Holistic Defense

A college of social work developed a partnership with a state commission on indigent defense to examine existing data-collection procedures, potential case outcomes, and practical implications of implementing holistic defense programs. The holistic defense model responds to the complex challenges of justice-system-involved defendants by providing social services in public defense offices. Using chi-square and logistic regression analyses of administrative data for a sample of 15,994 public defendants from a single judicial circuit, this research study examined case outcomes before and after implementation of a holistic defense program. Results were mixed regarding effectiveness of holistic defense in mitigating the effect of justice involvement for indigent defendants. Implementation of holistic defense was associated with a decrease in case dismissals. However, defendants receiving holistic representation were less likely to be indicted than defendants prior to program implementation. Once indicted, there were no changes in levels of diversions, and more defendants were held on bond, convicted as guilty, and incarcerated. After program implementation, fewer defendants were sentenced to alternatives to incarceration; more defendants were sentenced to time served, avoiding further incarcerative penalty. These findings suggested the need for further research to determine whether holistic defense practices are producing desired legal and social service outcomes.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

Parametric non-mixture cure models for schedule finding of therapeutic agents

We propose a phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast with traditional phase I trials that are designed solely to find the maximum tolerated dose of the agent, our design instead identifies a maximum tolerated schedule that includes a maximum tolerated dose as well as a vector of recommended administration times. Our model is based on a non-mixture cure model that constrains the probability of dose limiting toxicity for all patients to increase monotonically with both dose and the number of administrations received. We assume a specific parametric hazard function for each administration and compute the total hazard of dose limiting toxicity for a schedule as a sum of individual administration hazards. Throughout a variety of settings motivated by an actual study in allogeneic bone marrow transplant recipients, we demonstrate that our approach has excellent operating characteristics and performs as well as the only other currently published design for schedule finding studies. We also present arguments for the preference of our non-mixture cure model over the existing model.