The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples

Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple imputation (MI). We evaluate two types of MI: the usual Bayesian approach, which we call posterior draw (PD) imputation, and a little-used alternative, which we call ML imputation, in which values are imputed conditionally on an ML estimate. We find that observed-data ML is more efficient and has lower mean squared error than either type of MI. Between the two types of MI, ML imputation is more efficient than PD imputation, and ML imputation also has less potential for bias in small samples. The bias and efficiency of PD imputation can be improved by a change of prior.Comment: 32 pages, 3 figures, 3 tables, 2 Appendice

A joint regression modeling framework for analyzing bivariate binary data in R

We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets

Images of psychiatry and psychiatrists.

OBJECTIVE: This study surveyed medical teaching faculty to determine their attitudes toward psychiatry and psychiatrists. METHOD: We conducted a multisite survey of a probability sample of 1057 teaching medical faculty members from 15 academic teaching centers in the United Kingdom, Europe, and Asia stratified by early, middle, and late career stage. The average response rate across countries was 65%. RESULTS: The outstanding findings were that 90% of respondents considered that psychiatrists were not good role models for medical students, 84% thought psychiatric patients were unsuitable to be treated outside of specialized facilities, and 73% thought psychiatric patients were emotionally draining. We noted statistically significant differences by country, gender, career stage, and specialty. CONCLUSION: These results highlight why recruitment into psychiatry is problematic in many countries and suggest that greater attention should be given to improving the perception of psychiatrists as good role models and the efficacy of psychiatric treatments

Do Provider Service Networks Result in Lower Expenditures Compared with HMO s or Primary Care Case Management in Florida's Medicaid Program?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106942/1/hesr12129-sup-0001-Author-appendix.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106942/2/hesr12129.pd

Have you had bleeding from your gums? : Self-report to Identify giNGival inflammation (The SING diagnostic accuracy and diagnostic model development study)

Acknowledgements We would like to acknowledge all study participants in IQuaD and INTERVAL for contributing with their information, as well as the trial teams involved in planning, conducting and delivering both trials. Funding information: IQuaD was funded by the NIHR HTA (project 09/01/45). INTERVAL was funded by the NIHR HTA (project 06/35/99). No additional funding was obtained to conduct the diagnostic accuracy and prediction studies presented in this paper. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates.Peer reviewedPublisher PD

Changes in the Prevalence of Child and Youth Mental Disorders and Perceived Need for Professional Help between 1983 and 2014: Evidence from the Ontario Child Health Study

© The Author(s) 2019. Objectives: To examine: 1) changes in the prevalence of mental disorders and perceived need for professional help among children (ages 4 to 11) and youth (ages 12 to 16) between 1983 and 2014 in Ontario and 2) whether these changes vary by age and sex, urban-rural residency, poverty, lone-parent status, and immigrant background. Methods: The 1983 (n = 2836) and 2014 (n = 5785) Ontario Child Health Studies are provincially representative cross-sectional surveys with identical self-report checklist measures of conduct disorder, hyperactivity, and emotional disorder, as well as perceived need for professional help, assessed by integrating parent and teacher responses (ages 4 to 11) and parent and youth responses (ages 12 to 16). Results: The overall prevalence of perceived need for professional help increased from 6.8% to 18.9% among 4- to 16-year-olds. An increase in any disorder among children (15.4% to 19.6%) was attributable to increases in hyperactivity among males (8.9% to 15.7%). Although the prevalence of any disorder did not change among youth, conduct disorder decreased (7.2% to 2.5%) while emotional disorder increased (9.2% to 13.2%). The prevalence of any disorder increased more in rural and small to medium urban areas versus large urban areas. The prevalence of any disorder decreased for children and youth in immigrant but not nonimmigrant families. Conclusions: Although there have been decreases in the prevalence of conduct disorder, increases in other mental disorders and perceived need for professional help underscore the continued need for effective prevention and intervention programs

Prevalence of and risk factors for degenerative mitral valve disease in dogs attending primary-care veterinary practices in england

Background To date, epidemiological studies on degenerative mitral valve disease (DMVD) in dogs have largely reported referral caseloads or been limited to predisposed breeds. Analysis of primary‐care data to identify factors associated with DMVD would help clinicians identify high‐risk individuals and improve understanding. Objectives To estimate the prevalence of and identify risk factors for DMVD in dogs attending primary‐care veterinary practices in England. Animals Cases were identified within the electronic patient records of 111,967 dogs attending 93 practices. Four hundred and 5 dogs were diagnosed with DMVD (diagnosed cases) and a further 3,557 dogs had a heart murmur (HM) consistent with DMVD (possible cases). Methods Retrospective cross‐sectional study design. Prevalence was adjusted for the sampling approach. Mixed effects logistic regression models identified factors associated with DMVD. Results Prevalence estimates of diagnosed DMVD and HMs consistent with DMVD (both diagnosed and possible cases) were 0.36% (95% confidence interval [CI]: 0.29–0.45) and 3.54% (95% CI: 3.26–3.84) respectively. In the multivariable analysis, males had higher odds of diagnosed DMVD than did females (odds ratio [OR] 1.40, 95% CI: 1.12–1.74). Insured dogs had increased odds of DMVD compared with noninsured dogs (OR 3.56, 95% CI: 2.79–4.55) and dogs ≥20 kg had approximately half the odds of DMVD diagnosis compared with dogs(OR 0.51, 95% CI: 0.36–0.74). Strong associations between a DMVD diagnosis and individual breeds and age were identified. Conclusions and Clinical Importance Degenerative mitral valve disease was a common disorder in practice‐attending dogs. Knowledge of identified risk factors for DMVD could improve clinical diagnosis and direct future research

Changes in Per Member Per Month Expenditures after Implementation of Florida's Medicaid Reform Demonstration

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86945/1/j.1475-6773.2010.01226.x.pd

Implementation of Design Rules for Perception Into a Tool for Three-Dimensional Shape Generation Using a Shape Grammar and a Parametric Model

The user experience of a product is recognized as having an increasing importance in particular in consumer products. Current approaches to designing user experiences are not easily translated to languages that a computer can understand. This paper examines a particular aspect of user experience, namely perception of the aesthetics of a product, to formalize this to rules, which are embedded into a tool to generate design. Investigating the perception of consumers is key to designing for their aesthetic preferences. Previous research has shown that consumers and designers often perceive the same products differently. This paper aims to embed rules on perception into a tool to support designers during design synthesis. Aesthetic design rules connecting perceptions with aesthetic features were integrated into a set grammar and a parametric modeling tool, and applied to the particular case of vases. The generated tool targeted the creation of vases with the perception of beautiful, elegant, and exciting. Results show that it is possible to generate beautiful, elegant, and exciting vases following the three aesthetic design rules, i.e., tall, simple, and curvy. The main contribution of this paper is the method used to incorporate information on perception into the set grammar and the parametric model. The tool is additionally proposed for supporting designers during design synthesis of shapes. The results are valid for vases but the method can be applied to other perceptions and product categories