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This article is from BMC Proceedings , volume 8 . Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10-12).

Childhood adversity exposure is common and is associated with unfavorable physical and mental health outcomes (Cicchetti & Toth, 1995; McLaughlin et al., 2012). While cumulative risk models of adversity have been instrumental in demonstrating that children with more adversity exposure are at greatest risk for unfavorable outcomes (Felitti et al., 1998), they do not elucidate the mechanisms by which adversity confers this risk. The Dimensional Model of Adversity and Psychopathology (DMAP) proposes two dimensions of adversity exposure – deprivation and threat – which differentially impact developmental outcomes through separate neurodevelopmental pathways (McLaughlin, Sheridan, & Lambert, 2014; Sheridan & McLaughlin, 2014). The model proposes that deprivation is associated with neural structure in areas of the brain that support complex cognitive functions (e.g., executive function, language, associative learning), like the frontoparietal control and dorsal attention networks. Another recent conceptual model proposes that early deprivation exposure may impact early-developing visual regions in the ventral visual stream that may scaffold the development of higher-order cognitive skills (Rosen et al., 2019). The DMAP further proposes that threat exposures are associated with structure in areas of the brain that support fear learning, emotion regulation, and threat perception like the limbic network, salience network, default mode network, amygdala, and hippocampus. Previous hypothesis-driven work has supported that deprivation and threat exposures differentially predict neural structure separately in childhood and adolescence (Busso et al., 2017; McLaughlin et al., 2016; 2019; McLaughlin, Sheridan, Winter, et al., 2014; Rosen et al., 2018; Sheridan, Copeland, et al., 2019). There is, however, a lack of consistent longitudinal evidence supporting the association between adversity exposures and neurodevelopmental trajectories. Furthermore, data-driven tests of the DMAP could support its relevance in understanding developmental outcomes following adversity exposures relative to cumulative risk models (e.g., Sheridan, Shi, et al., 2019). In the present study, we will take a network analytical approach to examine clusters of adversity exposures and structural neural development in a longitudinal neuroimaging sample of children and adolescents. The study involved two study visits starting with youth aged 8-16 years in the Seattle area. Participants were recruited for increased likelihood of maltreatment exposure. At the first visit, participants reported on lifetime deprivation and threat exposures and underwent structural magnetic resonance imaging (MRI). Approximately two years later, subjects completed a follow-up neuroimaging assessment using the same scanner.

This project will examine the latent structure of state mindfulness and test specific predictions about within-person associations between facets of state mindfulness, repetitive thought onset and severity, and negative affect in an intensive longitudinal dataset.

Several statistical techniques that can be used to ameliorate the difficulties inherent in the data analysis of longitudinal studies are presented. The first step in longitudinal data analysis is graphing. This permits visual inspection of the data, and with educated viewing can yield insights into the nature of the underlying mechanisms. The next level of sophistication is to apply regression analysis and change point analysis to the curves obtained from the graphical analysis. It is usually the case in longitudinal studies that the exact form of the curve is not known prior to the experimentation. The graphing of the data is useful in suggesting different mathematical models to apply to the curves. The results of the regression analysis will help determine the uniformity of the process across subjects. The next step is to use the form of the fitted equation to determine significant points on the curve. The shape of the curve will suggest change points in the subjects' behavior with respect to the dependent variable. In certain cases where problems arise, the use of principal components is called for. Practical advantages are that they explain the original curve best and will likely point to any existing major differences, and they occur mathematically and do not depend on the experimenter's ability to form a regression curve or pick important change points. When used in conjunction with each other, these techniques form a powerful package for analyzing longitudinal data. (RC)

Entering students at an unidentified community college had lower self-esteem and identified with lower-status occupations than their university counterparts. Two years later the differences in self-esteem had been eliminated, liberal arts students at the community college identified with higher-status occupations, and business and technical students at the community college identified more closely with occupations that corresponded to their chosen curricula. These relationships were especially clear when the 19 dependent variables were reduced to a 2-dimensional discriminant space. This report offers a complete and technical discussion of the data. (Author/CA)

This report explains why the Management Implications of Team Teaching (MITT) project chose multiple linear regression and path analysis to analyze through-time relationships among variables, and why it rejected repeated-measures analysis of variance (ANOVA) and difference scores over time. Project MITT examined governance and work structures for five time periods from 1974 to 1976 in 29 elementary schools, 16 of which had introduced team-teaching (or unitized) methods in 1974. To analyze longitudinal changes among variables and schools, the project's statistical techniques had to take account of small sample size and multiple time periods; they also had to control for pre-1974 differences among the schools, changes in variables because of unitization, and differences in variable means and ranges. All of these factors interfered with comparisons of unitized and nonunitized schools and distorted relationships among the variables. Hierarchical multiple linear regression solved these problems by relating variables to one another both over time and in order of explanatory power. Path analysis using lagged multiple linear regression helped to test postulated relationships through time and explore for further relationships. Four appendices discuss ANOVA, difference scores, path analysis, and corrections used for data cyclicity. (Author/RW)

A series of flight trials was performed on an F-111C aircraft, tail number A8-132, at the RAAF's Aircraft Research and Development Unit. Data obtained from the tests has been analysed in Air Operations Division to determine the aircraft aerodynamic and control derivatives. In this report the longitudinal and lateral derivatives for the take-off and landing configurations are presented and compared with wind tunnel results.

 Forage breeding is more complex than that practiced in other plant species. In addition to assessing the effect of superior genotypes through animal performance, there is a need to evaluate genetic materials over time, which requires the use of appropriate tools and statistical methods so that all effects of the environment are captured and analyzed due to the perennial character of these species. Another significant factor with regard to forage legumes is the analysis in pure or mixed stands, which may facilitate the conduction of trials in the first case and improve the adaptive ability evaluation of species with associated plant and the environment effects in the second case. In this context, the interaction of the genotype with the environment, which differs for the temperate and tropical legume species, is discussed from the point of view of the longitudinal data analysis by application of the mixed model methodology, also considering the employment of mixed pastures in the tropical region of the globe.

Adolescence is marked by significant biological and social changes which can impact adolescents’ mental and physical health (Sawyer et al., 2018). Experiences in adolescence also have the potential to shape health trajectories into adulthood (Sawyer et al., 2012). Identifying opportunities to support adolescent health is therefore crucial for promoting better lifelong health. Adolescents typically spend a substantial amount of time in school and school environments therefore represent an important outlet for fostering feelings of belonging in adolescence. Previous research has shown that high levels of school belonging in adolescence has been associated with improved psychosocial health (Steiner et al., 2019) and better well-being (Arslan et al., 2020). Schools also provide opportunities for adolescents to identify interests, form connections and become members of multiple groups (Allen et al., 2018). The Social Identity Approach to Health states that social processes (e.g. connection and group membership) can also affect physical health outcomes (Haslam et al., 2018). Multiple group membership (MGM) can afford people with psychological resources such as feelings of safety and belonging (Iyer et al., 2009) which can buffer against stress. Being part of multiple groups during adolescence may therefore have lasting effects on physical health outcomes into adulthood. The benefits of school belonging and MGM on the emotional health of adolescents are well established, however, less is known about the potential for school belonging and MGM to support long-term physical health outcomes. This research aims to address this gap in the literature by examining relationships between school belonging and MGM in adolescence, and physiological health in adulthood.

This research project uses data from a national cohort survey named "Growing up in Scotland. A longitudinal Cohort study of children and their families nutrition and mental health.". It will investigate the relationship between these variables using multiple regression. This is important research due to the growing global concerns about the rise in ultra-processed foods and the increasing mental health crisis. This study aims to understand this link better to improve pubic well-being. The findings could also benefit clinical psychologists by informing them about external factors that could influence the course of therapeutic interventions.

This research project uses data from a national cohort survey named "Growing up in Scotland. A longitudinal Cohort study of children and their families nutrition and mental health.". It will investigate the relationship between these variables using multiple regression. This is important research due to the growing global concerns about the rise in ultra-processed foods and the increasing mental health crisis. This study aims to understand this link better to improve pubic well-being. The findings could also benefit clinical psychologists by informing them about external factors that could influence the course of therapeutic interventions.

This research project uses data from a national cohort survey named "Growing up in Scotland. A longitudinal Cohort study of children and their families nutrition and mental health.". It will investigate the relationship between these variables using multiple regression. This is important research due to the growing global concerns about the rise in ultra-processed foods and the increasing mental health crisis. This study aims to understand this link better to improve pubic well-being. The findings could also benefit clinical psychologists by informing them about external factors that could influence the course of therapeutic interventions.

Stage 1 IPA at PCI RR

The project is a longitudinal study with at least two waves of data collection each 3 months apart. The purpose of this project is to examine the longitudinal relationships between interpretation bias, anxiety and depression, and the related constructs of worry, rumination, intolerance of uncertainty, and general distress, as well as social anxiety. We will also explore the content specificity of interpretation bias relating to different content matter, via worry and rumination-related scenarios that can either be social or non-social in nature.

This is a coordinated data analysis preregistration for a project testing complementary longitudinal models of the association between sense of purpose and cognitive functioning in a multi-study framework.

This is a coordinated data analysis preregistration for a project testing complementary longitudinal models of the association between sense of purpose and cognitive functioning in a multi-study framework.

This is a coordinated data analysis preregistration for a project testing complementary longitudinal models of the association between sense of purpose and cognitive functioning in a multi-study framework.

This study describes the changes in demographic characteristics of beginning teachers who entered the profession between 1956-57 and 1976. Existing literature on beginning teachers is examined to construct an image of the changes that have occurred among those choosing teaching careers. National and statewide samples are reviewed; however, demographic factors such as age, sex, race, and marital status are described only in national terms. A review of the literature on changes in characteristics includes information on: (1) age; (2) sex; (3) race; (4) marital status; (5) occupational inheritance; (6) commitment to teaching as a career; (7) idealism and locus of control; (8) academic ability and recruitment into the teaching occupation; (9) college selectivity, grade point average, and career commitment; (10) attrition among teachers selected into the occupation; and (11) academic ability and attrition of teachers from educational occupations. A list of sub-samples and tables, of the National Longitudinal Study, that will be examined in a final report is included. (JD)

Two analysis methods are applied to a family of flexible aircraft in order to investigate how and when structural (especially dynamic aeroelastic) effects affect the dynamic characteristics of aircraft. The first type of analysis is an open loop modal analysis technique. This method considers the effect of modal residue magnitudes on determining vehicle handling qualities. The second method is a pilot in the loop analysis procedure that considers several closed loop system characteristics. Both analyses indicated that dynamic aeroelastic effects caused a degradation in vehicle tracking performance, based on the evaluation of some simulation results. Volume 2 consists of the presentation of the state variable models of the flexible aircraft configurations used in the analysis applications mode shape plots for the structural modes, numerical results from the modal analysis frequency response plots from the pilot in the loop analysis and a listing of the modal analysis computer program.

Two analysis methods are applied to a family of flexible aircraft in order to investigate how and when structural (especially dynamic aeroelastic) effects affect the dynamic characteristics of aircraft. The first type of analysis is an open loop modal analysis technique. This method considers the effect of modal residue magnitudes on determining vehicle handling qualities. The second method is a pilot in the loop analysis procedure that considers several closed loop system characteristics. Both analyses indicated that dynamic aeroelastic effects caused a degradation in vehicle tracking performance, based on the evaluation of some simulation results. Volume 2 consists of the presentation of the state variable models of the flexible aircraft configurations used in the analysis applications mode shape plots for the structural modes, numerical results from the modal analysis frequency response plots from the pilot in the loop analysis and a listing of the modal analysis computer program.

This article is from Trials , volume 14 . Abstract None

This study examined the effects of the Armed Forces Health Longitudinal Technology Application (AHLTA) implementation on Ambulatory Data Module (ADM) compliance, coding compliance, and provider productivity. The sample sized consisted of the 24 months of data The AHLTA system is an electronic medical record designed to improve patient care delivery in the military health system. A statistical analysis of the implementation of the AHLTA system on coding compliance and provider productivity showed positive statistical significant results (r .165, and p .000) and (r =.216, and p .022) respectively. There was no statistical correlation between AHLTA implementation and ADM compliance.

The National Education Longitudinal Study of 1988 (NELS:88) is the most recent in a series of longitudinal studies conducted by the National Center for Education Statistics at the United States Department of Education. The NELS:88 began with a baseline assessment of school experiences, with the purpose of relating these experiences to current academic achievement and to later achievement in school and in life. A total of 1,201 schools from 50 states participated in the base-year study. Out of 29,884 students selected, 92.9% responded. Out of 26,410 parents selected, 90.5% responded. The unique features of the NELS:88 and how researchers can use these new features to study educational policy and the effects of education on children are the subjects of this paper. One special feature of the NELS:88 is that it focuses on eighth graders, thus creating a longitudinal data base that will enable researchers to assess the effects of elementary, middle, and junior high school experiences on high school performance and school completion. Both self-reports and teacher and administrator reports are assessed as well as the student's family and home environment. Special sampling procedures and follow-up methods are discussed. Policymakers will be particularly interested in data concerning primary-to-secondary transition patterns, school effectiveness, dropouts, parental involvement, equity of education across minority groups and for at-risk students, cognitive growth, ability grouping and tracking, needs of language minorities, mathematics and science programs, humanities and history programs, and programs for gifted and talented students. Five figures and one data table are included. (TJH)

This article is from Extreme Physiology & Medicine , volume 2 . Abstract Background: The aims of the present study were, firstly, to investigate in a cross-sectional analysis the age of peak Ironman performance within one calendar year in all qualifiers for Ironman Hawaii and Ironman Hawaii; secondly, to determine in a longitudinal analysis on a qualifier for Ironman Hawaii whether the age of peak Ironman performance and Ironman performance itself change across years; and thirdly, to determine the gender difference in performance. Methods: In a cross-sectional analysis, the age of the top ten finishers for all qualifier races for Ironman Hawaii and Ironman Hawaii was determined in 2010. For a longitudinal analysis, the age and the performance of the annual top ten female and male finishers in a qualifier for Ironman Hawaii was determined in Ironman Switzerland between 1995 and 2010. Results: In 19 of the 20 analyzed triathlons held in 2010, there was no difference in the age of peak Ironman performance between women and men (p > 0.05). The only difference in the age of peak Ironman performance between genders was in ‘Ironman Canada’ where men were older than women (p = 0.023). For all 20 races, the age of peak Ironman performance was 32.2 ± 1.5 years for men and 33.0 ± 1.6 years for women (p > 0.05). In Ironman Switzerland, there was no difference in the age of peak Ironman performance between genders for top ten women and men from 1995 to 2010 (F = 0.06, p = 0.8). The mean age of top ten women and men was 31.4 ± 1.7 and 31.5 ± 1.7 years (Cohen's d = 0.06), respectively. The gender difference in performance in the three disciplines and for overall race time decreased significantly across years. Men and women improved overall race times by approximately 1.2 and 4.2 min/year, respectively. Conclusions: Women and men peak at a similar age of 32–33 years in an Ironman triathlon with no gender difference. In a qualifier for Ironman Hawaii, the age of peak Ironman performance remained unchanged across years. In contrast, gender differences in performance in Ironman Switzerland decreased during the studied period, suggesting that elite female Ironman triathletes might still narrow the gender gap in the future.

Several sets of data are analyzed, all of which demonstrate that estimates of achievement rates can differ dramatically when different time periods are used for an evaluation of a compensatory education program. Specifically, it is found that including the summer months in an evaluation often considerably reduces estimates of achievement and hence monthly achievement rates. In other words, achievement gains made during the school year are not sustained, even until the next fall. These findings are consistently supported by the data regardless of the standardized achievement test used, the grade level, the subject area, or the program. An evaluation of a one-year program should at least take account of fall-to-fall achievement. For multi-year programs, evaluations should be based on a time period that includes the summer following each year of the program. Including the following summer in an evaluation will allow the conclusion to reflect the extent to which student achievement is sustained and will, therefore, provide more useful information to those concerned with improving programs. (RC)

Classical multivariate principal component analysis has been extended to functional data and termed functional principal component analysis (FPCA). Most existing FPCA approaches do not accommodate covariate information, and it is the goal of this paper to develop two methods that do. In the first approach, both the mean and covariance functions depend on the covariate $Z$ and time scale $t$ while in the second approach only the mean function depends on the covariate $Z$. Both new approaches accommodate additional measurement errors and functional data sampled at regular time grids as well as sparse longitudinal data sampled at irregular time grids. The first approach to fully adjust both the mean and covariance functions adapts more to the data but is computationally more intensive than the approach to adjust the covariate effects on the mean function only. We develop general asymptotic theory for both approaches and compare their performance numerically through simulation studies and a data set.

An analysis examined changes in participation in vocational education and training and in schooling and higher education in Australia. It drew principally on data from the Youth in Transitions study. Participation estimates were based on information from four cohorts of young people. Members of each cohort initially completed multiple-choice tests in reading comprehension and mathematics and then completed an annual mail questionnaire focused on participation in education and the labor market. Participation was compared when members of the cohorts were aged 19 in 1980, 1984, 1989, and 1994. Types of educational participation identified were year 12 completion, technical and further education (TAFE), apprenticeship, higher education, and post-school education and training. Results indicated substantial growth in most forms of post-compulsory education; a year 12 completion rate that more than doubled in the 14 years spanned by the cohorts; substantial increase in participation in TAFE programs; declining participation in apprenticeships; and nearly doubled participation in higher education. In a reversal, in the most recent cohort, young people from blue-collar families were more likely to participate in TAFE than those from other families. The wealth profile of apprentices declined marginally over time. In the mid-1990s, young people from the wealthiest quartile were somewhat less likely to enter apprenticeships. (YBL)

This article is from BMC Musculoskeletal Disorders , volume 15 . Abstract Background: There are few longitudinal cohort studies examining associations between incident MRI findings and incident spine-related symptom outcomes. Prior studies do not discriminate between the two distinct outcomes of low back pain (LBP) and radicular symptoms. To address this gap in the literature, we conducted a secondary analysis of existing data from the Longitudinal Assessment of Imaging and Disability of the Back (LAIDBACK). The purpose of this study was to examine the association of incident lumbar MRI findings with two specific spine-related symptom outcomes: 1) incident chronic bothersome LBP, and 2) incident radicular symptoms such as pain, weakness, or sensation alterations in the lower extremity. Methods: The original LAIDBACK study followed 123 participants without current LBP or sciatica, administering standardized MRI assessments of the lumbar spine at baseline and at 3-year follow-up, and collecting information on participant-reported spine-related symptoms and signs every 4 months for 3 years. These analyses examined bivariable and multivariable associations between incident MRI findings and symptom outcomes (LBP and radicular symptoms) using logistic regression. Results: Three-year cumulative incidence of new MRI findings ranged between 2 and 8%, depending on the finding. Incident annular fissures were associated with incident chronic LBP, after adjustment for prior back pain and depression (adjusted odds ratio [OR] 6.6; 95% confidence interval [CI] 1.2-36.9). All participants with incident disc extrusions (OR 5.4) and nerve root impingement (OR 4.1) reported incident radicular symptoms, although associations were not statistically significant. No other incident MRI findings showed large magnitude associations with symptoms. Conclusions: Even when applying more specific definitions for spine-related symptom outcomes, few MRI findings showed large magnitude associations with symptom outcomes. Although incident annular fissures, disc extrusions, and nerve root impingement were associated with incident symptom outcomes, the 3-year incidence of these MRI findings was extremely low, and did not explain the vast majority of incident symptom cases.

This article is from BMC Proceedings , volume 8 . Abstract Using the whole genome sequencing data and the simulated longitudinal phenotypes for 849 pedigree-based individuals from Genetic Analysis Workshop 18, we investigated various approaches to detecting the association of rare and common variants with blood pressure traits. We compared three strategies for longitudinal data: (a) using the baseline measurement only, (b) using the average from multiple visits, and (c) using all individual measurements. We also compared the power of using all of the pedigree-based data and the unrelated subset. The analyses were performed without knowledge of the underlying simulating model.

Parenting interventions for caregivers of children aged 2 to 18 years have been successful in reducing child maltreatment and improving child behaviour. While parenting interventions are effective in reducing child maltreatment in low- and middle-income countries (LMICs) , little is known about the long-term sustainability of these effects. Additionally, less is known about the moderators of these interventions on child maltreatment or how these moderators operate over time. Research investigating the mediation models in parenting interventions has primarily focused on child conduct problems rather than child maltreatment. Additionally, there is limited understanding of how these mechanisms function for participants with varying characteristics, particularly concerning child maltreatment outcomes. Parenting interventions have been adapted to diverse contexts and previous studies have performed meta-analysis or integrative analyses on parenting interventions. No IPD meta-analysis has yet examined the effects of parenting interventions on child maltreatment LMICs. Conducting and IPDMA will enable researchers to ask questions regarding equity and equity over time of the effects of parenting intervention. This study will explore differential effectiveness of parenting interventions across time.

This study aims to produce a scientific temporal analysis of the psychological lived experience of Saxenda pharmacotherapy for weight loss, in relation to the psychical consequences of the drug, using a longitudinal IPA analysis of secondary public data sourced from YouTube.

First Watch is designed to determine the core reasons for attrition and retention for first-term Sailors in the U.S. Navy. Attrition rate is staggering both in terms of dollars expended and in disruption to the personnel readiness of the Navy. The First Watch project is designed as a longitudinal project in which cohorts of new recruits are tracked through their first term of enlistment in the Navy (normally a 4-year enlistment). Questionnaires were administered at five key points in the recruit's/Sailor's first term: at the beginning and end of recruit training, at the end of A/Apprentice School, when the recruit/Sailor exits the Navy during training, and after the Sailor get to his/her Fleet job. This report covers the first year of data collection. Results indicate that RTC attrites were more likely to join the Navy to escape home or family situations and for the benefits associated with a Navy job than are RTC graduates. Results also showed that the Navy did not live up to attrites' expectations and that their values were not as consonant with Navy culture as those who ultimately graduated from RTC. Further, results indicate that attrites used stress coping styles that were not consonant with the demands of recruit training (e.g., wishful thinking), and that their levels of stress were higher, their morale lower, and that they slept fewer hours per night while in recruit training than did RTC graduates.

Calm water resistance tests on an L/B = 3.78 SES model were conducted in the Langley Field Towing Basin. During these tests model generated wave height data were taken along a line parallel to the model path. Results of this data at one speed are analyzed by the truncation correction method and the finite integral method to determine wavemaking resistance. The results are compared with resistance measurements and with the wavemaking predicted by a theoretical method. (Author)

Calm water resistance tests on an L/B = 3.78 SES model were conducted in the Langley Field Towing Basin. During these tests model generated wave height data were taken along a line parallel to the model path. Results of this data at one speed are analyzed by the truncation correction method and the finite integral method to determine wavemaking resistance. The results are compared with resistance measurements and with the wavemaking predicted by a theoretical method. (Author)

Calm water resistance tests on an L/B = 3.78 SES model were conducted in the Langley Field Towing Basin. During these tests model generated wave height data were taken along a line parallel to the model path. Results of this data at one speed are analyzed by the truncation correction method and the finite integral method to determine wavemaking resistance. The results are compared with resistance measurements and with the wavemaking predicted by a theoretical method. (Author)

This article is from Frontiers in Genetics , volume 5 . Abstract Gene–environment interaction (GEI) analysis can potentially enhance gene discovery for common complex traits. However, genome-wide interaction analysis is computationally intensive. Moreover, analysis of longitudinal data in families is much more challenging due to the two sources of correlations arising from longitudinal measurements and family relationships. GWIS of longitudinal family data can be a computational bottleneck. Therefore, we compared two methods for analysis of longitudinal family data: a methodologically sound but computationally demanding method using the Kronecker model (KRC) and a computationally more forgiving method using the hierarchical linear model (HLM). The KRC model uses a Kronecker product of an unstructured matrix for correlations among repeated measures (longitudinal) and a compound symmetry matrix for correlations within families at a given visit. The HLM uses an autoregressive covariance matrix for correlations among repeated measures and a random intercept for familial correlations. We compared the two methods using the longitudinal Framingham heart study (FHS) SHARe data. Specifically, we evaluated SNP–alcohol (amount of alcohol consumption) interaction effects on high density lipoprotein cholesterol (HDLC). Keeping the prohibitive computational burden of KRC in mind, we limited the analysis to chromosome 16, where preliminary cross-sectional analysis yielded some interesting results. Our first important finding was that the HLM provided very comparable results but was remarkably faster than the KRC, making HLM the method of choice. Our second finding was that longitudinal analysis provided smaller P-values, thus leading to more significant results, than cross-sectional analysis. This was particularly pronounced in identifying GEIs. We conclude that longitudinal analysis of GEIs is more powerful and that the HLM method is an optimal method of choice as compared to the computationally (prohibitively) intensive KRC method.

This article is from BMJ : British Medical Journal , volume 349 . Abstract Objectives To evaluate the impact of Brazil’s recently implemented Family Health Program (FHP), the largest primary health care programme in the world, on heart and cerebrovascular disease mortality across Brazil from 2000 to 2009.Design Ecological longitudinal design, evaluating the impact of FHP using negative binomial regression models for panel data with fixed effects specifications.Setting Nationwide analysis of data from Brazilian municipalities covering the period from 2000 to 2009.Data sources 1622 Brazilian municipalities with vital statistics of adequate quality.Main outcome measures The annual FHP coverage and the average FHP coverage in previous years were used as main independent variables and classified as none (0%), incipient (

Recent methodological research (Berry & Willoughby, 2017; Curran et al., 2014; Hamaker et al., 2015; Mulder & Hamaker, 2020) has demonstrated that traditional ways to analyse longitudinal data such us the cross-lagged panel model (CLMP) do not adequately separate within-person change and between-person stability and might thus lead to biased results. The overall goal of our project is to explore the consequences of this methodological issue on the estimation of the longitudinal effects of intergroup contact on outgroup prejudice (e.g., Pettigrew & Tropp, 2006, 2011).

This study utilized an Internet "Class Page" to evaluate interaction strategies in both traditional classroom settings and distance education settings where instruction was delivered via television and the Internet. Participants were primarily elementary school teachers who were enrolled in one of 11 graduate classes in Elementary School Curriculum taught during a 4-year period from 1997-2000. The participants completed survey instruments to ascertain: their participation on the "Class Page," their interactions with other students, and their attitudes toward the use of technology in the university setting. Data analyses indicate interesting responses spanning the 4 years. For example, examination of the data point to increased classroom teacher familiarity with computer usage over a 4-year time span, increased familiarity with the Internet, and higher skill levels of instructional technology applications in the classroom setting. Also, a higher degree of personal satisfaction in using classroom technology was consistent among the students. However, participant perceptions in distance education settings were consistently less positive than on-site face-to-face class perceptions regarding the value of the interactive instructional technologies utilized during this time. (Contains 11 references.) (Author/MES)

Using the nationally representative National Education Longitudinal Study of 1988 (NELS88) database, the relationships of first language (L1) proficiency to subsequent use of that language (home language maintenance), English proficiency and achievement and English self-concept were examined. Confirmatory factor analysis results showed that L1 proficiency in the tenth grade did not correlate significantly with subsequent English grades in school or English proficiency in twelfth grade, but had a strong positive relationship to twelfth-grade use of L1, and a surprisingly positive correlation with twelfth-grade English standardized test scores. Use of L1 up to twelfth grade showed almost no correlation with twelfth-grade English outcomes. Whereas eighth-grade L1 proficiency showed almost no correlation with tenth-grade English outcomes, eighth-grade L1 use had a negative impact on tenth-grade English test and proficiency. However, these negative effects were found only in early high school and did not persist over time. The results did not support speculation that home language proficiency would have persistent negative effects on English outcomes, but suggest the need for support in L1 enhancement for home language maintenance. (Contains 44 references.) (Author/MSE)

A Navy T-2 jet triner aircraft was instrumented to measure and record all motion variables. Motion time histories were recorded for a variety of carefully selected pilot inputs. A unique problem with the data was the high noise level in the measurement of the control input. Longitudinal motion data were analyzed with three digital computer parameter identification techniques: modified Newton-Raphson, Kalman filtering/smoothing, and maximum likelihood. Reported in Volume I are data gathering and modified Newton-Raphson analysis.

The judgment of whether or not a city's Title I reading program is a success is dependent upon the length of time used in the evaluation. This study's findings that estimates of achievement rates and judgments of success were dramatically affected by the length of time used in the evaluation was consistent across all the compensatory education programs investigated. Specifically, the inclusion of the summer months in an evaluation often reduces considerably estimates of achievement and hence monthly achievement rates. In other words, achievement gains made during the school year were not sustained even until the next fall. These findings are based on analysis of matched, longitudinal samples containing over 8,700 students and are unaffected by grade level, the subject area, the program, the standardized achievement test or the metric used in the analysis. (RC)

As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance component score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.

This article is from BMC Medical Research Methodology , volume 12 . Abstract Background: A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods: We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). Results: The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. Conclusions: The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.

This article is from PLoS ONE , volume 9 . Abstract Objective: Coreceptor switch from CCR5 to CXCR4 is associated with HIV disease progression. The molecular and evolutionary mechanisms underlying the CCR5 to CXCR4 switch are the focus of intense recent research. We studied the HIV-1 tropism dynamics in relation to coreceptor usage, the nature of quasispecies from ultra deep sequencing (UDPS) data and their phylogenetic relationships. Methods: Here, we characterized C2-V3-C3 sequences of HIV obtained from 19 patients followed up for 54 to 114 months using UDPS, with further genotyping and phylogenetic analysis for coreceptor usage. HIV quasispecies diversity and variability as well as HIV plasma viral load were measured longitudinally and their relationship with the HIV coreceptor usage was analyzed. The longitudinal UDPS data were submitted to phylogenetic analysis and sampling times and coreceptor usage were mapped onto the trees obtained. Results: Although a temporal viral genetic structuring was evident, the persistence of several viral lineages evolving independently along the infection was statistically supported, indicating a complex scenario for the evolution of viral quasispecies. HIV X4-using variants were present in most of our patients, exhibiting a dissimilar inter- and intra-patient predominance as the component of quasispecies even on antiretroviral therapy. The viral populations from some of the patients studied displayed evidences of the evolution of X4 variants through fitness valleys, whereas for other patients the data favored a gradual mode of emergence. Conclusions: CXCR4 usage can emerge independently, in multiple lineages, along the course of HIV infection. The mode of emergence, i.e. gradual or through fitness valleys seems to depend on both virus and patient factors. Furthermore, our analyses suggest that, besides becoming dominant after population-level switches, minor proportions of X4 viruses might exist along the infection, perhaps even at early stages of it. The fate of these minor variants might depend on both viral and host factors.

This paper introduces latent growth modeling (LGM) as a statistical method for analyzing change over time in latent, or unobserved, variables, with particular emphasis of the application of this method in higher education research. While increasingly popular in other areas of education research and despite a wealth of publicly-available datasets relevant to postsecondary education research, LGM has not been utilized widely by higher education researchers. This paper begins by introducing LGM as a desirable mechanism for analyzing variability in individual growth trajectories over time and then presents an illustration of its application. An example of the application of LGM to data obtained from the Integrated Postsecondary Educational Data System (IPEDS) is presented to introduce specific components of LGM, including model specification and goodness-of-fit indices, and to demonstrate the research potential for higher education researchers. Finally, additional datasets offering longitudinal analysis potential for higher education researchers are presented to facilitate research. (Contains 2 tables, 2 figures, and 8 footnotes.)

Statewide Longitudinal Data Systems grant-funded projects represent one of the most developed and systemic K-12 education data projects in U.S. history. This report reviews what types of K-12 data elements are included in state systems, data linking with other systems, and how the data are used for reporting and decision making.

From 2012 onwards the UK Government announced a series of immigration policy reforms known as the “hostile environment” policy, which led to the so-called Windrush Scandal. Several hundred Commonwealth citizens who settled legally in the UK were falsely identified as undocumented - and in many cases deported. Aside from deportation, many people from minoritised ethnic groups in the UK lost livelihoods, jobs, incomes and benefits as a result of the hostile environment policy. We would expect the social and economic stressors introduced by the hostile environment policy to result in increases in mental health problems. Using data from the UK Household Longitudinal Study we plan to compare the change in psychological distress before and after the implementation of the 2014 Immigration Act, and before and after the 2017 Windrush Scandal media coverage, between people from minoritised ethnic groups and people of White ethnicity. We will do this using an interrupted time series design embedded in a Bayesian framework which accounts for fixed and time-varying confounders, as well as spatial and temporal uncertainty.