Multiple Outcome Variables Research Articles

Systematic review and meta-analysis of safety and efficacy of high-intensity focused ultrasound (HIFU) for face and neck rejuvenation.

High-intensity focused ultrasound (HIFU) is widely used for skin rejuvenation and tightening. The aim of this study was to determine the safety and efficacy of HIFU for face and neck rejuvenation. A systematic search of peer reviewed articles published before December 2017 was performed using Medline, Web of Science, and Scopus databases. Retrieved studies were screened, and clinical trials or follow-up studies which were evaluated the effect of HIFU on rejuvenation treatments of face and neck area were included. Risk of bias for each study was assessed and was reported. Safety and efficacy variables, which were reported more often in papers, were included in meta-analysis. Seventeen studies involving 477 participants were included in the analysis. Meta-analysis showed moderate improvement as scores of 2.74 (95% CI 2.06-3.43) and 2.68 (95% CI 1.92-3.45), out of 5 maximum score, for objective improvement score and subjective satisfaction score, respectively. Meta-analysis of mean pain score, assessed by a 0-10 Likert score, was 4.2 (95% CI 4.27-5.19). Edema and erythema showed considerable heterogeneity, and no hyperpigmentation was reported by included studies. Included studies used multiple and different outcome variables with different scoring at various time points. Long follow-up was not reported by majority of studies. HIFU is safe procedure in short term and has a moderate effect on rejuvenation of face and neck areas.

C443: a Methodology to See a Forest for the Trees

Often tree-based accounts of statistical learning problems yield multiple decision trees which together constitute a forest. Reasons for this include examining tree instability, improving prediction accuracy, accounting for missingness in the data, and taking into account multiple outcome variables. A key disadvantage of forests, unlike individual decision trees, is their lack of transparency. Hence, an obvious challenge is whether it is possible to recover some of the insightfulness of individual trees from a forest. In this paper, we will propose a conceptual framework and methodology to do so by reducing forests into one or a small number of summary trees, which may be used to gain insight into the central tendency as well as the heterogeneity of the forest. This is done by clustering the trees in the forest based on similarities between them. By means of simulated data, we will demonstrate how and why different similarity types in the proposed methodology may lead to markedly different conclusions, and explain when and why certain approaches may be recommended over other ones. We will finally illustrate the methodology with an empirical data set on the prediction of cocaine use on the basis of personality characteristics.

Response cost intervention program for Kannada-speaking preschool children who stutter: Case series

Introduction: Stuttering is reported to be highly prevalent in both children and adults, and has always drawn the attention of the clinicians and researchers. A vast body of literature exists on the intervention of stuttering in adults. However, research in the case of children who stutter (CWS) has gained momentum in the recent past. Research investigations in the past and clinical anecdotes have proven the potential benefits of early intervention. However, only a handful of the techniques have been explored. Response cost (RC) treatment is a widely adopted clinical approach with some positive evidence. The current study explored the effectiveness of RC treatment in preschool CWS. Methods: Participants were five children who stuttered in the age range of 3– 6 years. Spontaneous speech samples of all children were recorded both pre- and post-treatment. Recorded samples were analyzed for percentage of syllables stuttered (%SS), parent and clinician severity ratings, stuttering severity instrument (SSI)-4 scores, and naturalness ratings. Results: The results revealed a significant decrease in the %SS, severity ratings, and SSI-4 scores, and significant improvement in speech naturalness ratings in all the five participants. Conclusions: The study is the first one to document significant clinical improvement following RC intervention in Kannada-speaking preschool CWS. Future investigations with an experimental design, larger sample sizes, and monolingual as well as bilingual participants who stutter and multiple outcome variables are warranted.

A Multiattribute Benefits-Based Choice Model with Multiple Mediators: New Insights for Positioning

Previous research has demonstrated that consumers evaluate products according to their perceived benefits when making a choice. This article extends prior work by proposing a method that evaluates the degree to which multiple a priori defined benefits mediate product choices. The model is the first to consider process heterogeneity—that is, heterogeneity in how consumers perceive multiple attributes to positively or negatively affect multiple benefits simultaneously and the contribution of each benefit to product utility. The authors propose discrete choice experiments to holistically measure the link between attributes and benefits, as well as between attributes and choice, resulting in data that can be analyzed with a generalized probit model. The approach contributes to mediation research by offering an alternative method of handling multiple multinomial mediators and dichotomous outcome variables. An empirical illustration of bread choices shows how consumer judgments about health and value perceptions of products mediate purchase decisions. The authors demonstrate how the method can help managers (1) confirm and test existing knowledge about latent benefits, including whether they explain all the variation in choice, and (2) consider process heterogeneity to inform market segmentation strategies.

Learning Curves of Laparoscopic Roux-en-Y Gastric Bypass and Sleeve Gastrectomy in Bariatric Surgery: a Systematic Review and Introduction of a Standardization

The most commonly performed bariatric procedures are laparoscopic Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (LSG). Impact of learning curves on operative outcome has been well shown, but the necessary learning curves have not been clearly defined. This study provides a systematic review of the literature and proposes a standardization of phases of learning curves for RYGB and LSG. A systematic literature search was performed using PubMed, Web of Science, and CENTRAL databases. All studies specifying a number or range of approaches to characterize the learning curve for RYGB and LSG were selected. A total of 28 publications related to learning curves for 27,770 performed bariatric surgeries were included. Parameters used to determine the learning curve were operative time, complications, conversions, length of stay, and blood loss. Learning curve range was 30-500 (RYGB) and 30-200 operations (LSG) according to different definitions and respective phases of learning curves. Learning phases described the number of procedures necessary to achieve predefined skill levels, such as competency, proficiency, and mastery. Definitions of learning curves for bariatric surgery are heterogeneous. Introduction of the three skill phases competency, proficiency, and mastery is proposed to provide a standardized definition using multiple outcome variables to enable better comparison in the future. These levels are reached after 30-70, 70-150, and up to 500 RYGB, and after 30-50, 60-100, and 100-200 LSG. Training curricula, previous laparoscopic experience, and high procedure volume are hallmarks for successful outcomes during the learning curve.

Microarray and RNA in situ hybridization assay for recurrence risk markers of breast carcinoma and ductal carcinoma in situ: Evidence supporting the use of diverse pathways panels.

Breast tumor stratification by recurrence-risk is critical for deciding patient treatment. Here an approach combining cancer pathways microarray data complemented by RNA in situ hybridization (ISH) was investigated as a means for recurrence marker discovery and visualization in pathology specimens. LncRNA and mRNA expressions in breast carcinomas with low (n = 8) vs intermediate/high (n = 10) recurrence-scores as estimated by 21-gene assay and pathology review were compared by microarray assay. Tissue microarrays were prepared from breast carcinomas (n = 20) and ductal carcinoma in situ (DCIS) specimens (n = 84 patients) with known outcomes. Thirteen RNA ISH assays were performed: lncRNAs (BBC3-1, FER3, RAD21-AS1, ZEB1-2) and mRNAs (GLO1, GLTSCR2, TGFB1, TLR2) (implicated by the microarray data); MKI67; a pooled panel of recurrence-associated proliferation markers (BIRC5, Cyclin B1, MKI67, MYBL2, STK15); a pooled panel of non-proliferation recurrence-associated markers (CEACAM5, HTF9C, NDRG1, TP53, SLC7A5); and lncRNAs H19 and HOTAIR. Seven lncRNAs and 10 mRNAs showed significantly (P < .05) altered upregulation or downregulation by microarray assay: carcinoma RNA ISH staining did not mirror these patterns. HOTAIR staining was associated with a higher breast cancer recurrence score (P = .0152); qualitatively, H19 was massively expressed in a metaplastic triple negative breast carcinoma. Among the DCIS cohort, significant associations with multiple outcome variables were noted for TGFB1 and the non-proliferation panel (P-value range: .0001 to .047); proliferation panel staining showed an association with increasing DCIS grade (P = .0269) but not with outcomes. The findings support recurrence-risk estimation by the use of multi-marker panels that are representative of diverse cellular pathways rather than over-reliance on proliferation targets. H19, HOTAIR, and TGFB1 RNA ISH show potential for selective diagnostics.

Analyzing Processing Time Data in Applied Linguistics and Second Language Research: A Multivariate Mixed-effects Approach

In this paper, I introduce the use of an extension of the popular linear mixed-effects models: multivariate mixed models which can handle multiple outcome variables. This technique is especially useful for applied linguists and second language researchers who use processing time data (e.g., accuracy and reaction times) obtained from timed decision tasks, text-based eye tracking, and self-paced reading. It can address the long-standing issue of multiple comparisons as a result of having multiple outcomes (e.g., first fixation durations and total reading times in text-based eye tracking, and time spent in different regions in self-paced reading). This technique also provides exciting opportunities for researchers to ask new questions that could not be addressed in a straightforward manner with traditional statistics. With this technique, researchers are able to investigate differential effects of a predictor on different outcomes. Through a demonstration in R using published, open eye-tracking data, I contextualize my discussion of the technique, offering also practical, step-by-step, and annotated guidelines for interested researchers.

Multivariate-t linear mixed models with censored responses, intermittent missing values and heavy tails.

Multivariate longitudinal data arisen in medical studies often exhibit complex features such as censored responses, intermittent missing values, and atypical or outlying observations. The multivariate-t linear mixed model (MtLMM) has been recognized as a powerful tool for robust modeling of multivariate longitudinal data in the presence of potential outliers or fat-tailed noises. This paper presents a generalization of MtLMM, called the MtLMM-CM, to properly adjust for censorship due to detection limits of the assay and missingness embodied within multiple outcome variables recorded at irregular occasions. An expectation conditional maximization either (ECME) algorithm is developed to compute parameter estimates using the maximum likelihood (ML) approach. The asymptotic standard errors of the ML estimators of fixed effects are obtained by inverting the empirical information matrix according to Louis' method. The techniques for the estimation of random effects and imputation of missing responses are also investigated. The proposed methodology is illustrated on two real-world examples from HIV-AIDS studies and a simulation study under a variety of scenarios.

Inclusiveness and effectiveness of agricultural cooperatives: recent evidence from Ethiopia

PurposeUsing a recent rural farm household survey, the purpose of this paper is to investigate inclusiveness and effectiveness of agricultural cooperatives in Ethiopia.Design/methodology/approachThe study employs a logit model to examine inclusiveness and an endogenous switching regression (ESR) model to evaluate the effectiveness of agricultural cooperatives.FindingsThe results show that agricultural cooperatives are less inclusive of land-poor and illiterate households. On the other hand, the estimated results indicate that cooperatives effectively improved agricultural performance and welfare of its member households – i.e. membership in cooperatives increases yield and income by 1.37 quintal/hectare and 1,804 birr, respectively. Moreover, the result shows that marketing cooperatives effectively increased marketed surplus of their members by 34 percent.Research limitations/implicationsThe study has important implications regarding the ways to improve the effectiveness and/or inclusiveness of agricultural cooperatives.Originality/valueWhile accounting for the collective behavior of cooperatives, this study uses multiple outcome variables in examining the effectiveness of cooperatives in Ethiopia. Furthermore, this paper employs the ESR model and accounts for potential problems in estimating impact using non-experimental data.

Lessons learned from multisite implementation and evaluation of Project SHARE, a teen health information literacy, empowerment, and leadership program.

BackgroundThis case study describes the implementation and evaluation of a multisite teen health information outreach program. The objectives of the program were to increase health knowledge, health information literacy, interest in health careers, community engagement, and leadership skills of teens in disadvantaged communities.Case PresentationTeens at six sites across the country participated in a multi-week curriculum that focused on various aspects of health literacy, information literacy, and leadership. Lesson topics addressed personal health, social determinants of health, information quality, and communication and advocacy skills. Program evaluation included both quantitative and qualitative components and focused on multiple knowledge and skills outcome variables. Results suggested that while teens at all sites showed improvement, particularly with respect to engagement and interest in the topics, the degree of gains in knowledge and information literacy measures varied significantly from site to site.ConclusionOn-site implementation planning, cohesive integration of added activities, and emphasis on retention can contribute to implementation and evaluation effectiveness. This work also underscores the limitation of a purely quantitative approach to capturing the impact of health information and stresses the importance of supplementing numerical scores and statistics with qualitative data.

Consumer Perceptions of Green Marketing Claims: An Examination of the Relationships with Type of Claim and Corporate Credibility

Greenwashing practices can lead to consumer skepticism and negative perceptions of green marketing claims. Firms need to better understand how their claims and their corporate credibility play a role in consumers’ perceptions. This study features an experiment that examines different green marketing claim types and corporate credibility in terms of consumer skepticism and other consumer perceptual variables that are meaningful to firms. Results show that each of the manipulated variables have significant direct effects on multiple outcome variables. Additionally, results show an interaction between green marketing claim type and corporate credibility on consumer skepticism. Finally, implications and limitations are discussed.

Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets

Abstract We extend the inference procedure for the synthetic control method in two ways. First, we propose parametric weights for the p-value that includes the equal weights benchmark of Abadie et al. [1]. By changing the value of this parameter, we can analyze the sensitivity of the test’s result to deviations from the equal weights benchmark. Second, we modify the RMSPE statistic to test any sharp null hypothesis, including, as a specific case, the null hypothesis of no effect whatsoever analyzed by Abadie et al. [1]. Based on this last extension, we invert the test statistic to estimate confidence sets that quickly show the point-estimates’ precision, and the test’s significance and robustness. We also extend these two tools to other test statistics and to problems with multiple outcome variables or multiple treated units. Furthermore, in a Monte Carlo experiment, we find that the RMSPE statistic has good properties with respect to size, power and robustness. Finally, we illustrate the usefulness of our proposed tools by reanalyzing the economic impact of ETA’s terrorism in the Basque Country, studied first by Abadie and Gardeazabal [2] and Abadie et al. [3].

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

BackgroundWe previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. New MethodGraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications. ResultsIn addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. Comparison with existing methodsCompared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. ConclusionsGraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.

College, mental health, and a violent student: employing numerical formats to communicate risk on campus

Non-experts, including college administrators, professors, and students are often faced with risk assessment decisions on campus. Despite this fact, there are few accepted professional standards for presenting statistical risk and far fewer recommendations for the communication of violence risk. Existing systematic reviews are both incomplete and limited in scope and more troubling are the contradictory conclusions and competing recommendations for the communication of information that is essential to decision-making. In this article, three directions in the violence risk communication literature were given attention. First, non-experts were asked to make risk estimates using information from an expert source. Second, this project moves research attention to an important domain, campus violence. Finally, multiple outcome variables were assessed in an effort to extend the scope of research beyond Bayesian reasoning. Measured outcomes included: risk judgments, linguistic features, and affective processing as put forth by the affective processing theories. When the effect of estimating or communicating risk as a probability was compared to a frequency: frequency evidence was rated as less confusing and easier to understand.

A Comparison of Variable- and Person-Oriented Approaches in Evaluating a Universal Preventive Intervention.

Evaluations of prevention programs, such as the PAX Good Behavior Game (PAX), often have multiple outcome variables (e.g., emotional, behavioral, and relationship problems). These are often reported for multiple time points (e.g., pre- and post-intervention) where data are multilevel (e.g., students nested in schools). In this paper, we present both variable-oriented and person-oriented statistical approaches, to evaluate an intervention program with multilevel, longitudinal multivariate outcomes. Using data from the Manitoba PAX Study, we show how these two approaches provide us with different information that can be complementary. Data analyses with the variable-oriented approach (multilevel linear regression model) provided us with overall PAX program effects for each outcome variable; the person-oriented approach (latent transition analysis) allowed us to explore the transition of multiple outcomes across multiple time points and how the intervention program affects this transition differently for students with different risk profiles. We also used both approaches to examine how gender and socio-economic status related to the program effects. The implications of these results and the use of both types of approaches for program evaluation are discussed.

Analysis of the influences on plumage condition in laying hens: How suitable is a whole body plumage score as an outcome?

An important indicator of the health and behavior of laying hens is their plumage condition. Various scoring systems are used, and various risk factors for feather damage have been described. Often, a summarized score of different body parts is used to describe the overall condition of the plumage of a bird. However, it has not yet been assessed whether such a whole body plumage score is a suitable outcome variable when analyzing the risk factors for plumage deterioration. Data collected within a German project on farms keeping laying hens in aviaries were analyzed to investigate whether and the extent to which information is lost when summarizing the scores of the separate body parts. Two models were fitted using multiblock redundancy analysis, in which the first model included the whole body score as one outcome variable, while the second model included the scores of the individual body parts as multiple outcome variables. Although basically similar influences could be discovered with both models, the investigation of the individual body parts allowed for consideration of the influences on each body part separately and for the identification of additional influences. Furthermore, ambivalent influences (a factor differently associated with 2 different outcomes) could be detected with this approach, and possible dilutive effects were avoided. We conclude that influences might be underestimated or even missed when modeling their explanatory power for an overall score only. Therefore, multivariate methods that allow for the consideration of individual body parts are an interesting option when investigating influences on plumage condition.

The Cognitive Orientation to Daily Occupational Performance Approach and Transfer: A Scoping Review.

Transferring learning from therapy to everyday life skills is a necessary step for successful therapy outcomes, yet rarely addressed and achieved. However, a cognitive approach to skill acquisition, and the Cognitive Orientation to daily Occupational Performance (CO-OP), explicitly identifies transfer as an objective and incorporates elements into the intervention to support transfer. A scoping review was undertaken to explore the nature and extent of the research regarding CO-OP and transfer. An online search of 10 databases was conducted to identify and examine research studies reporting on CO-OP and transfer. The search yielded 25 documents that addressed CO-OP and transfer. The studies used a variety of approaches to evaluate transfer; all reported transfer on at least one and, in many cases, multiple transfer outcome variables. The CO-OP literature addresses transfer across a variety of populations and settings using a variety of approaches. Further work is required to establish a common approach to examining transfer in the CO-OP literature and the literature in general.

Using Structural Equation Modeling to Assess the Links between Tobacco Smoke Exposure, Volatile Organic Compounds, and Respiratory Function for Adolescents Aged 6 to 18 in the United States.

Asthma is an inflammatory airway disease that affects 22 million Americans in the United States. Research has found associations between impaired respiratory function, including asthma and increased symptoms among asthmatics, and common indoor air pollutants, including tobacco smoke exposure and volatile organic compounds (VOCs). However, findings linking VOC exposure and asthma are inconsistent and studies are of mixed quality due to design limitations, challenges measuring VOC exposure, small sample sizes, and suboptimal statistical methodologies. Because of the correlation between tobacco smoke exposure and VOCs, and associations between both tobacco smoke and VOCs with respiratory function, it is crucial that statistical methodology employed to assess links between respiratory function and individual air pollutants control for these complex relationships. This research uses Structural Equation Modeling (SEM) to assess the relationships between respiratory function, tobacco smoke exposure, and VOC exposure among a nationally-representative sample of adolescents. SEM allows for multiple outcome variables, the inclusion of both observed and latent variables, and controls the effects of confounding and correlated variables, which is critically important and is lacking in earlier studies when estimating the effects of correlated air pollutants on respiratory function. We find evidence of associations between respiratory function and some types of VOCs, even when controlling for the effects of tobacco smoke exposure and additional covariates. Furthermore, we find that poverty has an indirect effect on respiratory function through its relationships with tobacco smoke exposure and some types of VOCs. This analysis demonstrates how SEM is a robust analytic tool for assessing associations between respiratory function and multiple exposures to pollutants.

167 High Throughput Metabolite Profiling Identifies Plasma Anandamide as a Biomarker of Functional Outcome After Aneurysmal Subarachnoid Hemorrhage

Abstract INTRODUCTION The quantification of metabolites in plasma samples in patients with aneurysmal subarachnoid hemorrhage (aSAH) can highlight important alterations in critical metabolic pathways. As metabolites reflect changes associated with disease conditions, metabolite profiling (metabolomics) can identify candidate biomarkers for disease and potentially uncover pathways for intervention. METHODS We performed high throughput metabolite profiling across a broad spectrum of chemical classes (173 metabolites) on plasma samples taken from 119 patients with aSAH. Samples were drawn at 3 time points following ictus: 2–4, 7–10, and 12–14 days. Univariate and logistic regression analyses were performed to examine the relation of each metabolite with multiple outcome variables, including short- and long-term functional outcome (modified Rankin Scale, mRS). RESULTS &gt;A good functional outcome (mRS 0–2) was found in 63.1% and 66.7% of patients at 30 and 90 days, respectively, following aSAH. Plasma concentrations of the endogenous cannabinoid anandamide during days 2–4 after aneurysmal SAH were decreased by 48.1% (P &lt; 0.0001) and 57.6% (P &lt;0.0001) in patients with mRS 0–2 at 30 and 90 days, respectively. A similar statistical result was noted with plasma anandamide concentrations averaged across all time periods. Logistic regression further demonstrated that anandamide remained an independent predictor of functional outcome (30 days: P = 0.04; 90 days: P = 0.03), even after adjusting for other factors that influence outcome, including age, World Federation of Neurological Surgeons grade (WFNS), Fisher grade, and symptomatic vasospasm. CONCLUSION Decreased plasma anandamide following aSAH predicts a good functional outcome at 30 and 90 days. While a role for anandamide in aneurysmal SAH has not been previously reported, elevated anandamide levels have been implicated in neuronal apoptosis and cerebral edema in the acutely injured brain. These data highlight the increasing capability of metabolomics techniques in profiling large-sized cohorts to illuminate novel markers of disease and potential metabolic regulators.

Log-linear models to evaluate direction of effect in binary variables

In this article, we present a new approach to determining direction of effects in binary variables. A variable is considered explanatory if it explains the probability distribution of another variable. In cross-classifications of binary variables, the univariate probability distribution of variables can be considered explained if omitting the univariate effects of this variable does not lead to an ill-fitting model. Directional (non-hierarchical) log-linear models are introduced that allow statements concerning the direction of association in binary data. Cases in which variables of latent linear regression processes are partially observed as binary variables reveal a close conceptual link between the proposed log-linear approach and existing direction of effect methodology for metric variables. A Monte Carlo study is presented that shows that the proposed approach has good power and enables researchers to distinguish the correct model from the incorrect, reverse model. The approach can be extended to multiple explanatory and multiple outcome variables. Empirical data examples from research on aggression development in adolescence illustrate the proposed direction dependence approach.