A Simulation Study of Missing Data with Multiple Missing X’s

When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type of missingness (MCAR, MAR), degree of missingness (10%, 25%, and 50%), and where missingness occurs (one predictor, two predictors, or two predictors with overlap). Using a real dataset, cells are systematically deleted to create various scenarios of missingness so that parameter estimates from listwise deletion and multiple imputation may be compared to the true estimates from the full dataset. Results suggest the multiple imputation works well, even when the imputation model itself is missing data. Accessed 7,222 times on https://pareonline.net from August 12, 2014 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD

A confirmatory factor analysis of the novice nurse practitioner role transition scale

As interest in supporting new nurse practitioners\u27 (NPs) transition to practice increases, those interested in measuring the concept will need an instrument with evidence of reliability and validity. The Novice NP Role Transition (NNPRT) Scale is the first instrument to measure the concept. The preliminary exploratory factor analysis revealed a five-factor structure: organizational alignment, mentorship, sense of purpose, perceived competence and self-confidence, and compensation. Using a cross-sectional design and data from 210 novice NPs, the purpose of this study was to confirm the NNPRT Scale\u27s internal factor structure via confirmatory factor analysis (CFA). The sample was primarily female (97.5%), White (75.9%), and certified in primary care (53.5%). The CFA confirmed the five-factor structure, and model fit was improved by moving and omitting items (χ [619] = 1277.799, p \u3c 0.001; Root Mean Square Error of Approximation = 0.071 [0.066-0.077]). The final NNPRT Scale includes 37-items, and internal consistency reliability was calculated at 0.95. Convergent validity evidence was supported by a positive, significant correlation with receiving a formal orientation in the first NP position; a negative, significant correlation with turnover intention; and a lack of a relationship with years of prior registered nurse experience. The NNPRT Scale is an instrument with sound evidence of reliability and validity. The NNPRT Scale will be useful for researchers, administrators, and clinicians looking to explore factors that affect NNPRT, as well as by clinicians and administrators implementing programs to support novice NPs\u27 transition to practice

Bayesian Prior Choice in IRT Estimation Using MCMC and Variational Bayes

This study investigates the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models

Why are spousal caregivers more prevalent than nonspousal caregivers as study partners in AD dementia clinical trials?

ObjectivesMost Alzheimer disease (AD) caregivers are not spouses and yet most AD dementia trials enroll spousal study partners. This study examines the association between caregiver relationship to the patient and willingness to enroll in an AD clinical trial and how caregiver burden and research attitudes modify willingness.DesignInterviews with 103 AD caregivers who met criteria for ability to serve as a study partner.ResultsA total of 54% of caregivers were spouses or domestic partners and the remaining were adult children. Willingness to enroll a patient in a clinical trial was associated with being a spouse [odds ratio (OR)=2.53, P=0.01], increasing age (OR=1.39, P=0.01), and increasing scores on the Research Attitudes Questionnaire (OR=1.39, P&lt;0.001). No measures of caregiver burden or patient health were significant predictors of willingness. In multivariate models both research attitudes (OR=1.37, P&lt;0.001) and being a spouse, as opposed to an adult child, (OR=2.06, P=0.048) were independently associated with willingness to participate.ConclusionsSpousal caregivers had both a higher willingness to participate and a more positive attitude toward research. Caregiver burden had no association with willingness to participate. The strongest predictor of willingness was research attitudes

Why are spousal caregivers more prevalent than nonspousal caregivers as study partners in AD dementia clinical trials?

ObjectivesMost Alzheimer disease (AD) caregivers are not spouses and yet most AD dementia trials enroll spousal study partners. This study examines the association between caregiver relationship to the patient and willingness to enroll in an AD clinical trial and how caregiver burden and research attitudes modify willingness.DesignInterviews with 103 AD caregivers who met criteria for ability to serve as a study partner.ResultsA total of 54% of caregivers were spouses or domestic partners and the remaining were adult children. Willingness to enroll a patient in a clinical trial was associated with being a spouse [odds ratio (OR)=2.53, P=0.01], increasing age (OR=1.39, P=0.01), and increasing scores on the Research Attitudes Questionnaire (OR=1.39, P&lt;0.001). No measures of caregiver burden or patient health were significant predictors of willingness. In multivariate models both research attitudes (OR=1.37, P&lt;0.001) and being a spouse, as opposed to an adult child, (OR=2.06, P=0.048) were independently associated with willingness to participate.ConclusionsSpousal caregivers had both a higher willingness to participate and a more positive attitude toward research. Caregiver burden had no association with willingness to participate. The strongest predictor of willingness was research attitudes

Bayesian Prior Choice in IRT Estimation Using MCMC and Variational Bayes

Publisher's PDFThis study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (MCMC) and the relatively new, Variational Bayesian (VB). Conditional (CML) and Marginal Maximum Likelihood (MML) estimates were used as baseline methods for comparison. Vague priors produced large errors or convergence issues and are not recommended. For both MCMC and VB, the hierarchical and matched priors showed the lowest root mean squared errors (RMSEs) for ability estimates: RMSEs of difficulty estimates were similar across estimation methods. For the standard errors (SEs), MCMC-hierarchical displayed the largest values across most conditions. SEs from the VB estimation were among the lowest in all but one case. Overall, VB-hierarchical, VB-matched, and MCMC-matched performed best. VB with hierarchical priors are recommended in terms of their accuracy, and cost and (subsequently) time effectiveness.University of Delaware, School of Educatio