Identifying and promoting effective spiritual leadership through the role of district superintendent in the Northeast Jurisdiction of the United Methodist Church

https://place.asburyseminary.edu/ecommonsatsdissertations/1233/thumbnail.jp

Satisfaction Level of Parents of Children without Disabilities on the Idea of Inclusion

As a result of inclusion becoming more prevalent, it is crucial to know parents\u27 perspectives toward inclusion. Therefore, the research question is: How satisfied are parents of children without disabilities on the idea of inclusion? In order to answer this question, the researcher developed an inclusion questionnaire, and sent it to all parents in a fourth and fifth grade class in upstate New York. Being a special educator, it is crucial to know how parents feel about inclusion. It is also important for parents to know the benefits and possible drawbacks of having their child in an. inclusive setting. The data that the researcher obtained from this inclusion questionnaire revealed what parents know about inclusion and how they feel about inclusion. The results show that parents of children without disabilities do not believe that the advantages outweigh the disadvantages for their child in an inclusive setting. These findings lay the foundation for future research to be conducted on parents\u27 attitudes towards inclusion

Parallel NFS Block Layout Module for Linux

This position statement presents CITI's Linux prototype of NFSv4.1 pNFS client block layout module and reviews our implementation approach. CITI's prototype implements the IETF draft specification draft-ietf-nfsv4-pnfs-block and is one of three layout modules being developed along with the Linux pNFS generic client, which implements the draft-ietf-nfsv4-minorversion1 specification. The block layout module provides for an I/O data path over iSCSI directly to client SCSI devices identified by the pNFS block server.http://deepblue.lib.umich.edu/bitstream/2027.42/107895/1/citi-tr-08-1.pd

Indirect estimation of a discrete-state discrete-time model using secondary data analysis of regression data

Multi-state models of chronic disease are becoming increasingly important in medical research to describe the progression of complicated diseases. However, studies seldom observe health outcomes over long time periods. Therefore, current clinical research focuses on the secondary data analysis of the published literature to estimate a single transition probability within the entire model. Unfortunately, there are many difficulties when using secondary data, especially since the states and transitions of published studies may not be consistent with the proposed multi-state model. Early approaches to reconciling published studies with the theoretical framework of a multi-state model have been limited to data available as cumulative counts of progression. This paper presents an approach that allows the use of published regression data in a multi-state model when the published study may have ignored intermediary states in the multi-state model. Colloquially, we call this approach the Lemonade Method since when study data give you lemons, make lemonade. The approach uses maximum likelihood estimation. An example is provided for the progression of heart disease in people with diabetes. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63056/1/3599_ftp.pd

Computer modeling of diabetes and Its transparency: a report on the Eighth Mount Hood Challenge

Objectives The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes. Methods Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed. Results Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed. Conclusions Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data

Objective: The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. Design: Retrospective. Setting and participants A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. Outcome We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. Results: Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. Conclusions: While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions

Evaluating the ability of economic models of diabetes to simulate new cardiovascular outcomes trials : a report on the Ninth Mount Hood Diabetes Challenge

Objectives The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. Methods Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. Results Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME–calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. Conclusions The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed