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Williams R.,American Institutes for Research | Murray A.,University of Memphis
Archives of Physical Medicine and Rehabilitation | Year: 2015

Objectives: To use meta-analysis to synthesize point prevalence estimates of depressive disorder diagnoses for persons who have sustained a spinal cord injury (SCI).Data Sources: We searched PsycINFO, PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Dissertation Abstracts International (DAI) for studies examining depression after SCI through 2013. We also conducted a manual search of the reference sections of included studies.Study Selection: Included studies contained persons with SCI; used a diagnostic measure of depression (ie, an unstructured, semi-structured, or structured clinical interview, and/or a clinician diagnosis); and provided a diagnosis of major or minor depressive episodes for the subjects in the study. Diagnostic criteria were based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, or the Diagnostic and Statistical Manual of Mental Disorders-Third Edition (including Research Diagnostic Criteria) criteria.Data Extraction: The 2 authors of this study screened the titles and abstracts of 1053 unique studies for inclusion in this meta-analysis. Nineteen studies, containing 35,676 subjects and 21 effect size estimates, were included.Data Synthesis: The mean prevalence estimate of depression diagnosis after SCI was 22.2%, with a lower-bound estimate of 18.7% and an upper bound estimate of 26.3%. Random effects and mixed effects models were used in this work. A small number of study moderators were explored, including sample sex composition, Diagnostic and Statistical Manual of Mental Disorders version used, data collection method (primary vs secondary), sample traumatic etiology composition, sample injury level and completeness composition, and sample diagnostic composition. Data collection method, Diagnostic and Statistical Manual of Mental Disorders version, and diagnostic composition significantly predicted variation in observed effect size estimates, with primary data collection studies having lower estimates compared with secondary data analysis studies, studies using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, diagnostic criteria having higher estimates compared with studies using Diagnostic and Statistical Manual of Mental Disorders, Third Edition, criteria, and samples comprising individuals diagnosed only with major depression having lower prevalence estimates.Conclusions: The existing data on depression after SCI indicate that the prevalence of depression after SCI is substantially greater than that in the general medical population. These results underscore the importance of continued research on measuring depression in persons with SCI and on treatments for depression after SCI. © 2015 American Congress of Rehabilitation Medicine. Source

Petras H.,American Institutes for Research
Prevention Science | Year: 2016

In evaluating randomized control trials (RCTs), statistical power analyses are necessary to choose a sample size which strikes the balance between an insufficient and an excessive design, with the latter leading to misspent resources. With the growing popularity of using longitudinal data to evaluate RCTs, statistical power calculations have become more complex. Specifically, with repeated measures, the number and frequency of measurements per person additionally influence statistical power by determining the precision with which intra-individual change can be measured as well as the reliability with which inter-individual differences in change can be assessed. The application of growth mixture models has shown that the impact of universal interventions is often concentrated among a small group of individuals at the highest level of risk. General sample size calculations were consequently not sufficient to determine whether statistical power is adequate to detect the desired effect. Currently, little guidance exists to recommend a sufficient assessment design to evaluating intervention impact. To this end, Monte Carlo simulations are conducted to assess the statistical power and precision when manipulating study duration and assessment frequency. Estimates were extracted from a published evaluation of the proximal of the Good Behavior Game (GBG) on the developmental course of aggressive behavior. Results indicated that the number of time points and the frequency of assessments influence statistical power and precision. Recommendations for the assessment design of longitudinal studies are discussed. © 2016 Society for Prevention Research Source

Zumeta R.O.,American Institutes for Research
Remedial and Special Education | Year: 2015

Despite years of school reform intended to help students reach high academic standards, students with disabilities continue to struggle, suggesting a need for more intensive intervention as a part of special education and multi-tiered systems of support. At the same time, greater inclusion of students with disabilities in large-scale assessment, expanding knowledge of evidence-based practices, and improving assessment technology in recent decades provide important points of progress. This article summarizes this progress, notes potential areas for expansion, and suggests future implementation and policy research questions as they relate to observed challenges with provision of intensive intervention for students with disabilities. © Hammill Institute on Disabilities 2014 Source

Seaton E.K.,University of North Carolina | Upton R.,American Institutes for Research | Gilbert A.,University of North Carolina | Volpe V.,University of North Carolina
Child Development | Year: 2014

This study examined a moderated mediation model among 314 Black adolescents aged 13-18. The model included general coping strategies (e.g., active, distracting, avoidant, and support-seeking strategies) as mediators and racial identity dimensions (racial centrality, private regard, public regard, minority, assimilationist, and humanist ideologies) as moderators of the relation between perceived racial discrimination and depressive symptoms. Moderated mediation examined if the relation between perceived racial discrimination and depressive symptoms varied by the mediators and moderators. Results revealed that avoidant coping strategies mediated the relation between perceptions of racial discrimination and depressive symptoms. The results indicated that avoidant coping strategies mediated the relation between perceived racial discrimination and depressive symptoms among youth with high levels of the minority/oppressive ideology. © 2013 Society for Research in Child Development, Inc. Source

Yang M.,American Institutes for Research | Maxwell S.E.,University of Notre Dame
Psychological Methods | Year: 2014

Randomized longitudinal designs are commonly used in psychological and medical studies to investigate the treatment effect of an intervention or an experimental drug. Traditional linear mixed-effects models for randomized longitudinal designs are limited to maximum-likelihood methods that assume data are missing at random (MAR). In practice, because longitudinal data are often likely to be missing not at random (MNAR), the traditional mixed-effects model might lead to biased estimates of treatment effects. In such cases, an alternative approach is to utilize pattern-mixture models. In this article, a Monte Carlo simulation study compares the traditional mixed-effects model and 2 different approaches to patternmixture models (i.e., the differencing-averaging method and the averaging-differencing method) across different missing mechanisms (i.e., MAR, random-coefficient-dependent MNAR, or outcome-dependent MNAR) and different types of treatment-condition-based missingness. Results suggest that the traditional mixed-effects model is well suited for analyzing data with the MAR mechanism whereas the proposed pattern-mixture averaging-differencing model has the best overall performance for analyzing data with the MNAR mechanism. No method was found that could provide unbiased estimates under every missing mechanism, leading to a practical suggestion that researchers need to consider why data are missing and should also consider performing a sensitivity analysis to ascertain the extent to which their results are consistent across various missingness assumptions. Applications of different estimation methods are also illustrated using a real-data example. © 2013 American Psychological Association. Source

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