Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.

Automated Analysis of Fluency Behaviors in Aphasia.

PURPOSE: This study explored the use of an automated language analysis tool, FLUCALC, for measuring fluency in aphasia. The purpose was to determine whether CLAN's FLUCALC command could produce efficient, objective outcome measures for salient aspects of fluency in aphasia. METHOD: The FLUCALC command was used on CHAT transcripts of Cinderella stories from people with aphasia (PWA; n = 281) and controls (n = 257) in the AphasiaBank database. RESULTS: PWA produced significantly fewer total words, fewer words per minute, more pausing, more repetitions, more revisions, and more phonological fragments than controls, with only one exception: The Wernicke's group was similar to the control group in percentage of filled pauses. Individuals with Broca's aphasia had significantly longer inter-utterance pauses and fewer total words than all other aphasia groups. Both the Broca's and conduction aphasia groups had higher percentages of phrase repetitions than the NABW (NotAphasicByWAB) group. The conduction aphasia group also had a higher percentage of phrase revisions than the NABW and the anomic aphasia groups. Principal components analysis revealed two principal components that accounted for around 60% of the variance and related to quantity of output, rate of speech, and quality of output. The Gaussian mixture models showed that the participants clustered in three groups, which corresponded predominantly to the controls, the nonfluent aphasia group, and the remaining aphasia groups (all classically fluent aphasia types). CONCLUSIONS: FLUCALC is an efficient way to measure objective fluency behaviors in language samples in aphasia. Automated analyses of objective fluency behaviors on large samples of adults with and without aphasia can produce measures that can be used by researchers and clinicians to better understand and track salient aspects of fluency in aphasia. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25979863.

The Case of the Cookie Jar: Differences in Typical Language Use in Dementia.

Background: Findings from language sample analyses can provide efficient and effective indicators of cognitive impairment in older adults. Objective: This study used newly automated core lexicon analyses of Cookie Theft picture descriptions to assess differences in typical use across three groups. Methods: Participants included adults without diagnosed cognitive impairments (Control), adults diagnosed with Alzheimer's disease (ProbableAD), and adults diagnosed with mild cognitive impairment (MCI). Cookie Theft picture descriptions were transcribed and analyzed using CLAN. Results: Results showed that the ProbableAD group used significantly fewer core lexicon words overall than the MCI and Control groups. For core lexicon content words (nouns, verbs), however, both the MCI and ProbableAD groups produced significantly fewer words than the Control group. The groups did not differ in their use of core lexicon function words. The ProbableAD group was also slower to produce most of the core lexicon words than the MCI and Control groups. The MCI group was slower than the Control group for only two of the core lexicon content words. All groups mentioned a core lexicon word in the top left quadrant of the picture early in the description. The ProbableAD group was then significantly slower than the other groups to mention a core lexicon word in the other quadrants. Conclusions: This standard and simple-to-administer task reveals group differences in overall core lexicon scores and the amount of time until the speaker produces the key items. Clinicians and researchers can use these tools for both early assessment and measurement of change over time.

On becoming a Bayesian: early correspondences between J. Cornfield and L. J. Savage.

Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. The goals of this paper are as follows: (i) to explore how and why this transformation came about and (ii) to provide some sense as to who Cornfield was and the context in which he worked.

Enhancing the Classification of Aphasia: A Statistical Analysis Using Connected Speech.

Background: Large shared databases and automated language analyses allow for the application of new data analysis techniques that can shed new light on the connected speech of people with aphasia (PWA). Aims: To identify coherent clusters of PWA based on language output using unsupervised statistical algorithms and to identify features that are most strongly associated with those clusters. Methods & Procedures: Clustering and classification methods were applied to language production data from 168 PWA. Language samples were from a standard discourse protocol tapping four genres: free speech personal narratives, picture descriptions, Cinderella storytelling, procedural discourse. Outcomes & Results: Seven distinct clusters of PWA were identified by the K-means algorithm. Using the random forests algorithm, a classification tree was proposed and validated, showing 91% agreement with the cluster assignments. This representative tree used only two variables to divide the data into distinct groups: total words from free speech tasks and total closed class words from the Cinderella storytelling task. Conclusion: Connected speech data can be used to distinguish PWA into coherent groups, providing insight into traditional aphasia classifications, factors that may guide discourse research and clinical work.

A Comparison of Manual Versus Automated Quantitative Production Analysis of Connected Speech.

Purpose Analysis of connected speech in the field of adult neurogenic communication disorders is essential for research and clinical purposes, yet time and expertise are often cited as limiting factors. The purpose of this project was to create and evaluate an automated program to score and compute the measures from the Quantitative Production Analysis (QPA), an objective and systematic approach for measuring morphological and structural features of connected speech. Method The QPA was used to analyze transcripts of Cinderella stories from 109 individuals with acute-subacute left hemisphere stroke. Regression slopes and residuals were used to compare the results of manual scoring and automated scoring using the newly developed C-QPA command in CLAN, a set of programs for automatic analysis of language samples. Results The C-QPA command produced two spreadsheet outputs: an analysis spreadsheet with scores for each utterance in the language sample, and a summary spreadsheet with 18 score totals from the analysis spreadsheet and an additional 15 measures derived from those totals. Linear regression analysis revealed that 32 of the 33 measures had good agreement; auxiliary complexity index was the one score that did not have good agreement. Conclusions The C-QPA command can be used to perform automated analyses of language transcripts, saving time and training and providing reliable and valid quantification of connected speech. Transcribing in CHAT, the CLAN editor, also streamlined the process of transcript preparation for QPA and allowed for precise linking of media files to language transcripts for temporal analyses.

Optimism versus pessimism as predictors of physical health: A comprehensive reanalysis of dispositional optimism research.

Prior research has related dispositional optimism to physical health. Traditionally, dispositional optimism is treated as a bipolar construct, anchored at one end by optimism and the other by pessimism. Optimism and pessimism, however, may not be diametrically opposed, but rather may reflect 2 independent, but related dimensions. This article reports a reanalysis of data from previously published studies on dispositional optimism. The reanalysis was designed to evaluate whether the presence of optimism or the absence of pessimism predicted positive physical health more strongly. Relevant literatures were screened for studies relating dispositional optimism to physical health. Authors of relevant studies were asked to join a consortium, the purpose of which was to reanalyze previously published data sets separating optimism and pessimism into distinguishable components. Ultimately, data were received from 61 separate samples (N = 221,133). Meta-analytic analysis of data in which optimism and pessimism were combined into an overall index (the typical procedure) revealed a significant positive association with an aggregated measure of physical health outcomes (r = .026, p < .001), as did meta-analytic analyses with the absence of pessimism (r = .029, p < .001) and the presence of optimism (r = .011, p < .018) separately. The effect size for pessimism was significantly larger than the effect size for optimism (Z = -2.403, p < .02). Thus, the absence of pessimism was more strongly related to positive health outcomes than was the presence of optimism. Implications of the findings for future research and clinical interventions are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Association Between the Release of Netflix's 13 Reasons Why and Suicide Rates in the United States: An Interrupted Time Series Analysis.

OBJECTIVE: To estimate the association between the release of the Netflix series 13 Reasons Why and suicide rates in the United States. METHOD: Using segmented quasi-Poisson regression and Holt-Winters forecasting models, we assessed monthly rates of suicide among individuals aged 10 to 64 years grouped into 3 age categories (10-17, 18-29, and 30-64 years) between January 1, 2013, and December 31, 2017, before and after the release of 13 Reasons Why on March 31, 2017. We also assessed the impact of the show's release on a control outcome, homicide deaths. RESULTS: After accounting for seasonal effects and an underlying increasing trend in monthly suicide rates, the overall suicide rate among 10- to 17-year-olds increased significantly in the month immediately following the release of 13 Reasons Why (incidence rate ratio [IRR], 1.29; 95% CI, 1.09-1.53); Holt-Winters forecasting revealed elevated observed suicide rates in the month after release and in two subsequent months, relative to corresponding forecasted rates. Contrary to expectations, these associations were restricted to boys. Among 18- to 29-year-olds and 30- to 64-year-olds, we found no significant change in level or trend of suicide after the show's release, both overall and by sex. The show's release had no apparent impact in the control analyses of homicide deaths within any age group. CONCLUSION: The release of 13 Reasons Why was associated with a significant increase in monthly suicide rates among US youth aged 10 to 17 years. Caution regarding the exposure of children and adolescents to the series is warranted.

Optimism and physical health: a meta-analytic review.

BACKGROUND: Prior research links optimism to physical health, but the strength of the association has not been systematically evaluated. PURPOSE: The purpose of this study is to conduct a meta-analytic review to determine the strength of the association between optimism and physical health. METHODS: The findings from 83 studies, with 108 effect sizes (ESs), were included in the analyses, using random-effects models. RESULTS: Overall, the mean ES characterizing the relationship between optimism and physical health outcomes was 0.17, p < .001. ESs were larger for studies using subjective (versus objective) measures of physical health. Subsidiary analyses were also conducted grouping studies into those that focused solely on mortality, survival, cardiovascular outcomes, physiological markers (including immune function), immune function only, cancer outcomes, outcomes related to pregnancy, physical symptoms, or pain. In each case, optimism was a significant predictor of health outcomes or markers, all p < .001. CONCLUSIONS: Optimism is a significant predictor of positive physical health outcomes.

Integrating statistical and clinical research elements in intervention-related grant applications: summary from an NIMH workshop.

OBJECTIVE: The authors summarize points for consideration generated in a National Institute of Mental Health (NIMH) workshop convened to provide an opportunity for reviewers from different disciplines-specifically clinical researchers and statisticians-to discuss how their differing and complementary expertise can be well integrated in the review of intervention-related grant applications. METHODS: A 1-day workshop was convened in October, 2004. The workshop featured panel presentations on key topics followed by interactive discussion. This article summarizes the workshop and subsequent discussions, which centered on topics including weighting the statistics/data analysis elements of an application in the assessment of the application's overall merit; the level of statistical sophistication appropriate to different stages of research and for different funding mechanisms; some key considerations in the design and analysis portions of applications; appropriate statistical methods for addressing essential questions posed by an application; and the role of the statistician in the application's development, study conduct, and interpretation and dissemination of results. RESULTS: A number of key elements crucial to the construction and review of grant applications were identified. It was acknowledged that intervention-related studies unavoidably involve trade-offs. Reviewers are helped when applications acknowledge such trade-offs and provide good rationale for their choices. Clear linkage among the design, aims, hypotheses, and data analysis plan and avoidance of disconnections among these elements also strengthens applications. CONCLUSION: The authors identify multiple points to consider when constructing intervention-related grant applications. The points are presented here as questions and do not reflect institute policy or comprise a list of best practices, but rather represent points for consideration.

Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health.

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

Rates and risk factors for nonadherence to the medical regimen after adult solid organ transplantation.

BACKGROUND: Despite the impact of medical regimen nonadherence on health outcomes after organ transplantation, there is mixed and conflicting evidence regarding the prevalence and predictors of posttransplant nonadherence. Clinicians require precise information on nonadherence rates in order to evaluate patients' risks for this problem. METHODS: A total of 147 studies of kidney, heart, liver, pancreas/kidney-pancreas, or lung/heart-lung recipients published between 1981 and 2005 were included in a meta-analysis. Average nonadherence rates were calculated for 10 areas of the medical regimen. Correlations between nonadherence and patient psychosocial risk factors were examined. RESULTS: Across all types of transplantation, average nonadherence rates ranged from 1 to 4 cases per 100 patients per year (PPY) for substance use (tobacco, alcohol, illicit drugs), to 19 to 25 cases per 100 PPY for nonadherence to immunosuppressants, diet, exercise, and other healthcare requirements. Rates varied significantly by transplant type in two areas: immunosuppressant nonadherence was highest in kidney recipients (36 cases per 100 PPY vs. 7 to 15 cases in other recipients). Failure to exercise was highest in heart recipients (34 cases per 100 PPY vs. 9 to 22 cases in other recipients). Demographics, social support, and perceived health showed little correlation with nonadherence. Pretransplant substance use predicted posttransplant use. CONCLUSIONS: The estimated nonadherence rates, overall and by transplant type, allow clinicians to gauge patient risk and target resources accordingly. Nonadherence rates in some areas--including immunosuppressant use--appear unacceptably high. Weak correlations of most patient psychosocial factors with nonadherence suggest that attention should focus on other classes of variables (e.g., provider-related and systems-level factors), which may be more influential.

The role of care location in diagnosis and treatment of pediatric psychosocial conditions.

Racial differences in diagnosis and treatment of psychosocial conditions have been well documented. It is unclear if these differences represent variance in prevalence or are actually disparities in care driven by social factors, income-related differences, or differences in the actual location of care. We used 4 years of National Ambulatory Medical Care Surveys and National Hospital Ambulatory Medical Care Surveys as source for data on visits to private offices and hospital-based clinics. In addition to the standard practice of combining surveys across years, we used a "supersurvey" approach to combining the 2 distinct surveys. In our roughly 20,000 sampled visits, we confirmed the higher concentration of low-income children in visits to hospital-based clinics (p <.001), but saw no significant racial difference (p =.104). After controlling for race, income, and other demographics, we found that visits to hospital-based clinics were significantly more likely to include a diagnosis of depression (odds ratio [OR], 4.4; p =.011), but that there was no statistically significant difference in other psychosocial diagnoses. Once a diagnosis is made, there is no evidence of differences in treatment or follow-up between office-based or hospital clinic-based providers. Our analyses support previously gathered evidence for differences in economic status of the clientele of private offices and hospital-based clinics. We surprisingly found visits to clinics to be more likely to include a depression diagnosis, but this may be an artifact of the data reflecting visits rather than patients. We found no evidence that treatment or follow-up is different for the disadvantaged who use clinics rather than private offices.

Automated Proposition Density Analysis for Discourse in Aphasia.

Purpose: This study evaluates how proposition density can differentiate between persons with aphasia (PWA) and individuals in a control group, as well as among subtypes of aphasia, on the basis of procedural discourse and personal narratives collected from large samples of participants. Method: Participants were 195 PWA and 168 individuals in a control group from the AphasiaBank database. PWA represented 6 aphasia types on the basis of the Western Aphasia Battery-Revised (Kertesz, 2006). Narrative samples were stroke stories for PWA and illness or injury stories for individuals in the control group. Procedural samples were from the peanut-butter-and-jelly-sandwich task. Language samples were transcribed using Codes for the Human Analysis of Transcripts (MacWhinney, 2000) and analyzed using Computerized Language Analysis (MacWhinney, 2000), which automatically computes proposition density (PD) using rules developed for automatic PD measurement by the Computerized Propositional Idea Density Rater program (Brown, Snodgrass, & Covington, 2007; Covington, 2007). Results: Participants in the control group scored significantly higher than PWA on both tasks. PD scores were significantly different among the aphasia types for both tasks. Pairwise comparisons for both discourse tasks revealed that PD scores for the Broca's group were significantly lower than those for all groups except Transcortical Motor. No significant quadratic or linear association between PD and severity was found. Conclusion: Proposition density is differentially sensitive to aphasia type and most clearly differentiates individuals with Broca's aphasia from the other groups.

Comparing the effects of antidepressants: consensus guidelines for evaluating quantitative reviews of antidepressant efficacy.

With increasing numbers of treatment options available for patients with major depression over the last decade and the growing body of evidence describing their efficacy and safety, clinicians often find it difficult to determine the best and most appropriate evidence-based treatment for each patient. Systematic reviews utilizing statistical methods that synthesize and evaluate data from a number of studies have become increasingly more available over the past decade. We review major findings and lessons learned from salient examples of quantitative analyses of antidepressant research and provide recommendations for meta-analysts, journal and grant reviewers, and research 'consumers' (ie, clinicians) for conducting, reporting, and evaluating such analyses.

Suicide Trends Among Elementary School-Aged Children in the United States From 1993 to 2012.

IMPORTANCE: Suicide is a leading cause of death among school-aged children younger than 12 years but little is known about the epidemiology of suicide in this age group. OBJECTIVE: To describe trends in suicide among US children younger than 12 years by sociodemographic group and method of death. DESIGN, SETTING, AND PARTICIPANTS: Period trend analysis of national mortality data on suicide in children aged 5 to 11 years in the United States from January 1, 1993, to December 31, 2012. Data were analyzed per 5-year periods, between 1993 to 1997 and 2008 to 2012. MAIN OUTCOMES AND MEASURES: Number of suicide deaths and crude suicide rates. Period trends in rates of suicide were estimated using negative binomial regression incidence rate ratios (IRRs). RESULTS: The overall suicide rate among children aged 5 to 11 years remained stable between 1993 to 1997 and 2008 to 2012 (from 1.18 to 1.09 per 1 million; IRR = 0.96; 95% CI, 0.90-1.03). However, the suicide rate increased significantly in black children (from 1.36 to 2.54 per 1 million; IRR = 1.27; 95% CI, 1.11-1.45) and decreased in white children (from 1.14 to 0.77 per 1 million; IRR = 0.86; 95% CI, 0.79-0.94). The overall firearm suicide rate (IRR = 0.69; 95% CI, 0.57-0.85) and firearm suicide rate among white boys (IRR = 0.72; 95% CI, 0.59-0.88) decreased significantly during the study. The rate of suicide by hanging/suffocation increased significantly in black boys (IRR = 1.35; 95% CI, 1.14-1.61), although the overall change in suicide rates by hanging/suffocation or other suicide methods did not change during the study. CONCLUSIONS AND RELEVANCE: The stable overall suicide rate in school-aged children in the United States during 20 years of study obscured a significant increase in suicide incidence in black children and a significant decrease in suicide incidence among white children. Findings highlight a potential racial disparity that warrants attention. Further studies are needed to monitor these emerging trends and identify risk, protective, and precipitating factors relevant to suicide prevention efforts in children younger than 12 years.