The Factor Structure of the Shortened Version of the Working Alliance Inventory

1st Place in Denman Undergraduate Research Forum for PsychologyIn research on the process of change in psychotherapy, perhaps no variable has received more attention than the therapeutic alliance. Measures of the alliance characterize the level of agreement between therapist and client on treatment goals, the level of agreement on how to accomplish those goals, and the affective bond between therapist and client. One of the most widely used measures of the alliance is the 12-item Working Alliance Inventory (WAI-S, shortened version). However, the factor structure underlying the WAI-S remains unclear. Most often researchers have used a total score from the WAI-S, implying a single latent factor. The authors of the WAI-S originally suggested the WAI-S was composed of three distinct factors (i.e., Task, Goal, and Bond). An exploratory factor analysis of the WAI-S in a relatively small sample suggested two factors: Agreement and Relationship (Andrusyna, Tang, DeRubeis, & Luborsky, 2001). To examine the different factor structures proposed, we drew data from three independent samples of depressed patients participating in cognitive therapy for depression. In this combined sample of 207 patients, we used confirmatory factor analyses to compare the fit of the previously proposed one, two, and three factor models of the WAI-S. Using item scores from the third therapy session, our results support a two-factor solution consisting of Agreement and Relationship factors. All fit indices examined favored the two-factor model over competing models. Additional analyses suggest this factor structure applied to ratings of the alliance made by therapists, clients and observers. Our results clarify the factor structure of the WAI-S and should inform future research on the therapeutic alliance.A five-year embargo was granted for this item.Academic Major: Psycholog

A Hierarchical Temporal Memory Sequence Classifier for Streaming Data

Real-world data streams often contain concept drift and noise. Additionally, it is often the case that due to their very nature, these real-world data streams also include temporal dependencies between data. Classifying data streams with one or more of these characteristics is exceptionally challenging. Classification of data within data streams is currently the primary focus of research efforts in many fields (i.e., intrusion detection, data mining, machine learning). Hierarchical Temporal Memory (HTM) is a type of sequence memory that exhibits some of the predictive and anomaly detection properties of the neocortex. HTM algorithms conduct training through exposure to a stream of sensory data and are thus suited for continuous online learning. This research developed an HTM sequence classifier aimed at classifying streaming data, which contained concept drift, noise, and temporal dependencies. The HTM sequence classifier was fed both artificial and real-world data streams and evaluated using the prequential evaluation method. Cost measures for accuracy, CPU-time, and RAM usage were calculated for each data stream and compared against a variety of modern classifiers (e.g., Accuracy Weighted Ensemble, Adaptive Random Forest, Dynamic Weighted Majority, Leverage Bagging, Online Boosting ensemble, and Very Fast Decision Tree). The HTM sequence classifier performed well when the data streams contained concept drift, noise, and temporal dependencies, but was not the most suitable classifier of those compared against when provided data streams did not include temporal dependencies. Finally, this research explored the suitability of the HTM sequence classifier for detecting stalling code within evasive malware. The results were promising as they showed the HTM sequence classifier capable of predicting coding sequences of an executable file by learning the sequence patterns of the x86 EFLAGs register. The HTM classifier plotted these predictions in a cardiogram-like graph for quick analysis by reverse engineers of malware. This research highlights the potential of HTM technology for application in online classification problems and the detection of evasive malware

A uniformly ergodic Gibbs sampler for Bayesian survival analysis

Finite sample inference for Cox models is an important problem in many settings, such as clinical trials. Bayesian procedures provide a means for finite sample inference and incorporation of prior information if MCMC algorithms and posteriors are well behaved. On the other hand, estimation procedures should also retain inferential properties in high dimensional settings. In addition, estimation procedures should be able to incorporate constraints and multilevel modeling such as cure models and frailty models in a straightforward manner. In order to tackle these modeling challenges, we propose a uniformly ergodic Gibbs sampler for a broad class of convex set constrained multilevel Cox models. We develop two key strategies. First, we exploit a connection between Cox models and negative binomial processes through the Poisson process to reduce Bayesian computation to iterative Gaussian sampling. Next, we appeal to sufficient dimension reduction to address the difficult computation of nonparametric baseline hazards, allowing for the collapse of the Markov transition operator within the Gibbs sampler based on sufficient statistics. We demonstrate our approach using open source data and simulations

STOP, LOOK, AND LISTEN TO WHAT YOUR DATA IS TELLING YOU!

Join us for a ride on the Data Train where you will STOP, LOOK, and LISTEN to what your data is telling you and use the information to develop a process of continuous improvement in your after-school program to be an effective co-collaborator of closing the achievement gap. This workshop will provide information and strategies to be used in K – 12. Participants will learn the importance of data analysis in afterschool. Participants will learn how to work with regular day school professionals in determining what data sources to use. Participants will be able to analyze sample data and develop an action plan. Participants will develop a process of continuous improvement which utilizes student data

The Cystic Duct Remnant: An Unusual Case of a Biliary Intraluminal Filling Defect

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72892/1/j.1572-0241.1988.tb06086.x.pd

Prediction of Sublingual Bioavailability of Buprenorphine in Newborns with Neonatal Abstinence Syndrome—a case study on physiological and developmental changes using NONMEM and SIMCYP

Poster presented at 2009 American College of Clinical Pharmacology conference in Orlando. April 24-28. Background: About 55 to 94% of infants born to opioid dependent mothershave neonatal abstinence syndrome (NAS). Buprenorphine (BUP) is usedclinically as an analgesic and a detoxification agent and a maintenancetreatment for opioid dependence. No data, however, has been reported about the use of sublingual administration of BUP below the age of 4 year, especially for term infants with NAS. Objectives: Characterize pharmacokinetics (PK) of BUP in newborn patients;Evaluate the developmental changes in newborns in order to assist dosingoptimization in ongoing clinical studies. Methods: In silico prediction of PK behavior and physiological development in newborn patients were evaluated using SIMCYP. Intravenous clearance was predicted through physiologically based simulation method in SIMCYP. Basedon sublingual clearance obtained from a one compartmental model developedpreviously using NONMEM, individual changes of sublingual bioavailability were evaluated with physiological development in the first one and half month during the newborn period. Results: Intrinsic clearance of BUP in newborns were incorporated into enzymekinetic data obtained from literature. Change of sublingual bioavailability fornewborns was evaluated with bioavailability-postmenstrual age profiles.Sublingual bioavailability of BUP was estimated as 8.9--56.6% in newborn patients studied during the first one and half postnatal month. Conclusion: Developmental considerations for the PK of BUP in newborns are important for the characterization of the dose-exposure relationship. We have evaluated this from “bottom-up” and “top-down” approaches with SIMCYP and NONMEM respectively and found these approaches to be complementary andvaluable for clinical trial design and routine clinical care. Presumably theywould facilitate rational decision making in pediatric drug development as well