Supervised dimensionality reduction for multiple imputation by chained equations

Multivariate imputation by chained equations (MICE) is one of the most popular approaches to address missing values in a data set. This approach requires specifying a univariate imputation model for every variable under imputation. The specification of which predictors should be included in these univariate imputation models can be a daunting task. Principal component analysis (PCA) can simplify this process by replacing all of the potential imputation model predictors with a few components summarizing their variance. In this article, we extend the use of PCA with MICE to include a supervised aspect whereby information from the variables under imputation is incorporated into the principal component estimation. We conducted an extensive simulation study to assess the statistical properties of MICE with different versions of supervised dimensionality reduction and we compared them with the use of classical unsupervised PCA as a simpler dimensionality reduction technique

A Novel Item-Allocation Procedure for the Three-Form Planned Missing Data Design

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale\u27s items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use with scales containing fewer than 12 items. We also discuss the limitations of our procedure and several nuances for researchers to consider when implementing three-form PMDDs using our method. The RIA procedure allows researchers to implement statistically sound three-form planned missing data designs without the need for expert knowledge or results from prior statistical modeling. The RIA procedure can be used to construct both “paper-and-pencil” questionnaires and questionnaires administered through online survey software. The RIA procedure is a simple framework to aid in designing three-form PMDDs; implementing the RIA method does not require any specialized software or technical expertise

Solving the "many variables" problem in MICE with principal component regression

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the "many variables" problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy by means of a Monte Carlo simulation study and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures

High-dimensional Imputation for the Social Sciences: a Comparison of State-of-the-art Methods

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidencevalid estimates of the parameters of complete data analysis models. We found that using lasso penalty or forward selection to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results

On the Joys of Missing Data

We provide conceptual introductions to missingness mechanisms—missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR)—and state-of-the-art methods of handling missing data—full-information maximum likelihood (FIML) and multiple imputation (MI)—followed by a discussion of planned missing designs: multiform questionnaire protocols, two-method measurement models, and wave-missing longitudinal designs. We reviewed 80 articles of empirical studies published in the 2012 issues of the Journal of Pediatric Psychology to present a picture of how adequately missing data are currently handled in this field. To illustrate the benefits of utilizing MI or FIML and incorporating planned missingness into study designs, we provide example analyses of empirical data gathered using a three-form planned missing design

Middle school effects of the Dating Matters (R) comprehensive teen dating violence prevention model on physical violence, bullying, and cyberbullying:A cluster-randomized controlled trial

Few comprehensive primary prevention approaches for youth have been evaluated for effects on multiple types of violence. Dating Matters®: Strategies to Promote Healthy Teen Relationships (Dating Matters) is a comprehensive teen dating violence (TDV) prevention model designed by the Centers for Disease Control and Prevention and evaluated using a longitudinal stratified cluster-randomized controlled trial to determine effectiveness for preventing TDV and promoting healthy relationship behaviors among middle school students. In this study, we examine the prevention effects on secondary outcomes, including victimization and perpetration of physical violence, bullying, and cyberbullying. This study examined the effectiveness of Dating Matters compared to a standard-of-care TDV prevention program in 46 middle schools in four high-risk urban communities across the USA. The analytic sample (N = 3301; 53% female; 50% Black, non-Hispanic; and 31% Hispanic) consisted of 6th–8th grade students who had an opportunity for exposure to Dating Matters in all three grades or the standard-of-care in 8th grade only. Results demonstrated that both male and female students attending schools implementing Dating Matters reported 11% less bullying perpetration and 11% less physical violence perpetration than students in comparison schools. Female Dating Matters students reported 9% less cyberbullying victimization and 10% less cyberbullying perpetration relative to the standard-of-care. When compared to an existing evidence-based intervention for TDV, Dating Matters demonstrated protective effects on physical violence, bullying, and cyberbullying for most groups of students. The Dating Matters comprehensive prevention model holds promise for reducing multiple forms of violence among middle school-aged youth

PAK1 modulates a PPARγ/NF-κB cascade in intestinal inflammation

P21-activated kinases (PAKs) are multifunctional effectors of Rho GTPases with both kinase and scaffolding activity. Here, we investigated the effects of inflammation on PAK1 signaling and its role in colitis-driven carcinogenesis. PAK1 and p-PAK1 (Thr423) were assessed by immunohistochemistry, immunofluorescence, and Western blot. C57BL6/J wildtype mice were treated with a single intraperitoneal TNFα injection. Small intestinal organoids from these mice and from PAK1-KO mice were cultured with TNFα. NF-κB and PPARγ were analyzed upon PAK1 overexpression and silencing for transcriptional/translational regulation. PAK1 expression and activation was increased on the luminal intestinal epithelial surface in inflammatory bowel disease and colitis-associated cancer. PAK1 was phosphorylated upon treatment with IFNγ, IL-1β, and TNFα. In vivo, mice administered with TNFα showed increased p-PAK1 in intestinal villi, which was associated with nuclear p65 and NF-κB activation. p65 nuclear translocation downstream of TNFα was strongly inhibited in PAK1-KO small intestinal organoids. PAK1 overexpression induced a PAK1–p65 interaction as visualized by co-immunoprecipitation, nuclear translocation, and increased NF-κB transactivation, all of which were impeded by kinase-dead PAK1. Moreover, PAK1 overexpression downregulated PPARγ and mesalamine recovered PPARγ through PAK1 inhibition. On the other hand PAK1 silencing inhibited NF-κB, which was recovered using BADGE, a PPARγ antagonist. Altogether these data demonstrate that PAK1 overexpression and activation in inflammation and colitis-associated cancer promote NF-κB activity via suppression of PPARγ in intestinal epithelial cells

Effects of the Dating Matters® comprehensive prevention model on health- and delinquency-related risk behaviors in middle school youth:A cluster-randomized controlled trial

Teen dating violence (TDV) is associated with a variety of delinquent behaviors, such as theft, and health- and delinquency-related risk behaviors, including alcohol use, substance abuse, and weapon carrying. These behaviors may co-occur due to shared risk factors. Thus, comprehensive TDV-focused prevention programs may also impact these other risk behaviors. This study examined the effectiveness of CDC's Dating Matters (R): Strategies to Promote Healthy Teen Relationships (Dating Matters) comprehensive TDV prevention model compared to a standard-of-care condition on health- and delinquency-related risk behaviors among middle school students. Students (N = 3301; 53% female; 50% black, non-Hispanic; and 31% Hispanic) in 46 middle schools in four sites across the USA were surveyed twice yearly in 6th, 7th, and 8th grades. A structural equation modeling framework with multiple imputation to account for missing data was utilized. On average over time, students receiving Dating Matters scored 9% lower on a measure of weapon carrying, 9% lower on a measure of alcohol and substance abuse, and 8% lower on a measure of delinquency by the end of middle school than students receiving an evidence-based standard-of-care TDV prevention program. Dating Matters demonstrated protective effects for most groups of students through the end of middle school. These results suggest that this comprehensive model is successful at preventing risk behaviors associated with TDV. clinicaltrials.gov Identifier: NCT01672541

Intraventricular dyssynchrony in light chain amyloidosis: a new mechanism of systolic dysfunction assessed by 3-dimensional echocardiography

<p>Abstract</p> <p>Background</p> <p>Light chain amyloidosis (AL) is a rare but often fatal disease due to intractable heart failure. Amyloid deposition leads to diastolic dysfunction and often preserved ejection fraction. We hypothesize that AL is associated with regional systolic dyssynchrony. The aim is to compare left ventricular (LV) regional synchrony in AL subjects versus healthy controls using 16-segment dyssynchrony index measured from 3-dimension-al (3D) echocardiography.</p> <p>Methods</p> <p>Cardiac 3D echocardiography full volumes were acquired in 10 biopsy-proven AL subjects (60 ± 3 years, 5 females) and 10 healthy controls (52 ± 1 years, 5 females). The LV was subdivided into 16 segments and the time from end-diastole to the minimal systolic volume for each of the 16 segments was expressed as a percent of the cycle length. The standard deviations of these times provided a 16-segment dyssynchrony index (16-SD%). 16-SD% was compared between healthy and AL subjects.</p> <p>Results</p> <p>Left ventricular ejection fraction was comparable (control vs. AL: 62.4 ± 0.6 vs. 58.6 ± 2.8%, p = NS). 16-SD% was significantly higher in AL versus healthy subjects (5.93 ± 4.4 vs. 1.67 ± 0.87%, p = 0.003). 16-SD% correlated with left ventricular mass index (R 0.45, p = 0.04) but not to left ventricular ejection fraction.</p> <p>Conclusion</p> <p>Light chain amyloidosis is associated with left ventricular regional systolic dyssynchrony. Regional dyssynchrony may be an unrecognized mechanism of heart failure in AL subjects.</p

An RCT of dating matters:Effects on teen dating violence and relationship behaviors

Introduction Teen dating violence is a serious public health problem with few effective prevention strategies. This study examines whether the Dating Matters comprehensive prevention model, compared with a standard of care intervention, prevented negative relationship behaviors and promoted positive relationship behaviors. Study design This longitudinal, cluster-RCT compared the effectiveness of Dating Matters with standard of care across middle school. Standard of care was an evidence-based teen dating violence prevention curriculum (Safe Dates) implemented in eighth grade. Setting/participants Forty-six middle schools in high-risk urban neighborhoods in four U.S. cities were randomized. Schools lost to follow-up were replaced with new schools, which were independently randomized (71% school retention). Students were surveyed in fall and spring of sixth, seventh, and eighth grades (2012–2016). The analysis sample includes students from schools implementing Dating Matters or standard of care for >2 years who started sixth grade in the fall of 2012 or 2013 and had dated (N=2,349 students, mean age 12 years, 49% female, and 55% black, non-Hispanic, 28% Hispanic, 17% other). Intervention Dating Matters is a comprehensive, multicomponent prevention model including classroom-delivered programs for sixth to eighth graders, training for parents of sixth to eighth graders, educator training, a youth communications program, and local health department activities to assess capacity and track teen dating violence–related policy and data. Main outcome measures Self-reported teen dating violence perpetration and victimization, use of negative conflict resolution strategies, and positive relationship skills were examined as outcomes. Imputation and analyses were conducted in 2017. Results Latent panel models demonstrated significant program effects for three of four outcomes; Dating Matters students reported 8.43% lower teen dating violence perpetration, 9.78% lower teen dating violence victimization, and 5.52% lower use of negative conflict resolution strategies, on average across time points and cohorts, than standard of care students. There were no significant effects on positive relationship behaviors. Conclusions Dating Matters demonstrates comparative effectiveness, through middle school, for reducing unhealthy relationship behaviors, such as teen dating violence and use of negative conflict resolution strategies, relative to the standard of care intervention