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Most of the available methods for longitudinal data analysis are designed and validated for the situation where the number of subjects is large and the number of observations per subject is relatively small. Motivated by the Naturalistic Teenage Driving Study (NTDS), which represents the exact opposite situation, we examine standard and propose new methodology for marginal analysis of longitudinal count data in a small number of very long sequences. We consider standard methods based on generalized estimating equations, under working independence or an appropriate correlation structure, and find them unsatisfactory for dealing with time-dependent covariates when the counts are low. For this situation, we explore a within-cluster resampling (WCR) approach that involves repeated analyses of random subsamples with a final analysis that synthesizes results across subsamples. This leads to a novel WCR method which operates on separated blocks within subjects and which performs better than all of the previously considered methods. The methods are applied to the NTDS data and evaluated in simulation experiments mimicking the NTDS.

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)

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)

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 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 report is an integral part of the publication series of the Youth in Transition study, a nationwide panel survey of adolescent boys, which attempts to discover and document how the contemporary social environments affect the development of young men during their high school years. Four waves of data were gathered from 2,213 boys comprising the sample, who were clustered into 87 different high schools throughout the country. Additionally, because of the special interest in the school environment, data were collected from the principals, counselors, and samples of teachers in the participating schools. The efforts reported in this study are based on the attempt to outline a practical procedure to be used in longitudinal analyses of Youth in Transition data, the major aim being to develop a strategy which can be applied to most, if not all, of the analyses to be performed. The report develops a "parallel prediction" model for longitudinal analysis, which makes separate use of each repetition of the criterion dimension; it is contended that the proposed strategy is widely applicable in studies employing panel designs. The proposed model was applied to a limited set of analyses of the Youth in Transition data. Early identification of subgroups was seen to have a facilitating effect in longitudinal analysis. (Author/RJ)

The Bilingual Reading Study employs several measures of oral language proficiency: (1) a standardized oral language proficiency test, the Language Assessment Scales (LAS); (2) teacher ratings on the Student Operational Language Assessment Scale (SOLA); (3) teacher ratings on the Southwest Educational Development Laboratory (SEDL) Oral Language Proficiency Scale (OLPS); and (4) an ethnographic verification of each child's linguistic capability based on audio tapes of the child in the classroom, on the playground, and at home. In this paper, the reliability and distribution of responses for each measure is presented, followed by a discussion of the relations among the various measures. (BW)

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 Metabolites , volume 3 . Abstract The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.

This article is from BMJ : British Medical Journal , volume 342 . Abstract Objective To investigate whether the incentive scheme for UK general practitioners led them to neglect activities not included in the scheme.Design Longitudinal analysis of achievement rates for 42 activities (23 included in incentive scheme, 19 not included) selected from 428 identified indicators of quality of care.Setting 148 general practices in England (653 500 patients).Main outcome measures Achievement rates projected from trends in the pre-incentive period (2000-1 to 2002-3) and actual rates in the first three years of the scheme (2004-5 to 2006-7).Results Achievement rates improved for most indicators in the pre-incentive period. There were significant increases in the rate of improvement in the first year of the incentive scheme (2004-5) for 22 of the 23 incentivised indicators. Achievement for these indicators reached a plateau after 2004-5, but quality of care in 2006-7 remained higher than that predicted by pre-incentive trends for 14 incentivised indicators. There was no overall effect on the rate of improvement for non-incentivised indicators in the first year of the scheme, but by 2006-7 achievement rates were significantly below those predicted by pre-incentive trends.Conclusions There were substantial improvements in quality for all indicators between 2001 and 2007. Improvements associated with financial incentives seem to have been achieved at the expense of small detrimental effects on aspects of care that were not incentivised.

Stage 1 IPA at PCI RR

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.

While many studies have reported the predictors of teacher attrition, we know little about what predicts the attrition of school leaders. Using the Colorado state data on elementary school principals' and assistant principals' career paths from 1999 to 2001 and school achievement-level data, we addressed two research questions: 1) How do the age-specific attrition rates differ by gender and race? and 2) What other conditional factors are associated with the attrition of school leaders? We found that female and minority groups generally had higher attrition rates at age 40 or younger and at age 56 or older than male and non-minority groups. Our data also indicated that school size and salary increase were associated with the attrition of both male and female leaders. Large schools were more likely to have higher rates of school leader attrition, and the leaders who expected relatively higher salary increases by transferring were more likely to leave their schools. Lower school achievement predicted higher attrition of female leaders only. These findings have important implications for policy-makers when they plan and implement strategies for preventing high attrition rates of school leaders. (Contains 2 figures, 1 table and 5 notes.)

Generalized Estimation Equations (GEE) are a well-known method for the analysis of non-Gaussian longitudinal data. This method has computational simplicity and marginal parameter interpretation. However, in the presence of missing data, it is only valid under the strong assumption of missing completely at random (MCAR). Some corrections can be done when the missing data mechanism is missing at random (MAR): inverse probability weighting (WGEE) and multiple imputation (MIGEE). In order to obtain consistent estimates, it is necessary the correct specification of the weight model for WGEE or the imputation model for the MIGEE. A recent method combining ideas of these two approaches has doubly robust property. For consistency, it requires only the weight or the imputation model to be correct. In this work it is assumed a proportional odds model and it is proposed a doubly robust estimator for the analysis of ordinal longitudinal data with intermittently missing response and covariate under the MAR mechanism. Simulation results revealed better performance of the proposed method compared to WGEE and MIGEE. The method is applied to a data set related to Analgesia Pain in Childbirth study.

 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.

This article is from Trials , volume 14 . Abstract None

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.

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.

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.

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.

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).

We propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data. Our proposal can be cast within the framework of linear mixed models with discrete individual random intercepts; however, differently from the standard formulation, the proposed Covariance Pattern Mixture Model (CPMM) does not require the usual local independence assumption. The model is thus able to simultaneously model the heterogeneity, the association among the responses and the temporal dependence structure. We focus on the investigation of temporal patterns related to the cognitive functioning in retired American respondents. In particular, we aim to understand whether it can be affected by some individual socio-economical characteristics and whether it is possible to identify some homogenous groups of respondents that share a similar cognitive profile. An accurate description of the detected groups allows government policy interventions to be opportunely addressed. Results identify three homogenous clusters of individuals with specific cognitive functioning, consistent with the class conditional distribution of the covariates. The flexibility of CPMM allows for a different contribution of each regressor on the responses according to group membership. In so doing, the identified groups receive a global and accurate phenomenological characterization.

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 (

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.

Pre-registration (before conducting the data analysis) of the data analysis protocol (V3.0 10.05.2021) for the planned manuscript entitled: Impact of COVID-19 pandemic on diet behaviour among UK adults: a longitudinal analysis of data from the HEBECO study.

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.

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.

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.

The extent to which individuals are socially integrated within their families, friendship groups and wider communities has implications for their health and wellbeing (Obisesan & Gillum, 2009). Social connections can provide us with resources which mitigate harmful consequences of stress and offer us a sense of support and belonging (Haslam et al., 2018). Conversely, low levels of social integration, marked by a lack of social connections, has been associated with poorer psychological health and higher rates of depression (Santini et al., 2015). The impact of social integration also extends to physical health, with research linking social isolation and loneliness with increased risk of cardiovascular disease (CVD; Gallagher et al., 2024; Hu et al., 2021). Social integration has also been linked to cognitive function, especially in older adults (Atti et al., 2010). Cognitive function refers to mental abilities such as learning, memory, attention and problem-solving, all of which we rely on daily. Although a degree of cognitive decline is expected with aging, clinically significant changes are not (Stephan et al., 2007). From a cognitive perspective, social engagement and activity is cognitively effortful and therefore may support healthy cognitive function (Krueger et al., 2009). However, a review and meta-analysis (Evans et al., 2019) has indicated that findings in this area are mixed, in part due to widespread variation in measurement. This study will strengthen our understanding of this relationship and contribute to more consistency in findings by applying a longitudinal design to examine if higher levels of social integration are associated with better cognitive function. Importantly, our study extends prior work by incorporating multiple waves of data over an extended period and using cross-lagged panel models to disentangle directionality—thereby addressing key limitations in existing research, such as reliance on cross-sectional designs and failure to account for reciprocal or time-dependent effects. For this analysis we will use The Irish Longitudinal Study on Ageing (TILDA) as it provides a robust dataset for examining these relationships. TILDA is a large-scale, nationally representative survey designed specifically to explore the ageing process among older adults in Ireland. As a multi-wave data, TILDA offers an opportunity to explore how social integration may affect both cognitive and overall health over time.

The extent to which individuals are socially integrated within their families, friendship groups and wider communities has implications for their health and wellbeing (Obisesan & Gillum, 2009). Social connections can provide us with resources which mitigate harmful consequences of stress and offer us a sense of support and belonging (Haslam et al., 2018). Conversely, low levels of social integration, marked by a lack of social connections, has been associated with poorer psychological health and higher rates of depression (Santini et al., 2015). The impact of social integration also extends to physical health, with research linking social isolation and loneliness with increased risk of cardiovascular disease (CVD; Gallagher et al., 2024; Hu et al., 2021). Social integration has also been linked to cognitive function, especially in older adults (Atti et al., 2010). Cognitive function refers to mental abilities such as learning, memory, attention and problem-solving, all of which we rely on daily. Although a degree of cognitive decline is expected with aging, clinically significant changes are not (Stephan et al., 2007). From a cognitive perspective, social engagement and activity is cognitively effortful and therefore may support healthy cognitive function (Krueger et al., 2009). However, a review and meta-analysis (Evans et al., 2019) has indicated that findings in this area are mixed, in part due to widespread variation in measurement. This study will strengthen our understanding of this relationship and contribute to more consistency in findings by applying a longitudinal design to examine if higher levels of social integration are associated with better cognitive function. Importantly, our study extends prior work by incorporating multiple waves of data over an extended period and using cross-lagged panel models to disentangle directionality—thereby addressing key limitations in existing research, such as reliance on cross-sectional designs and failure to account for reciprocal or time-dependent effects. For this analysis we will use The Irish Longitudinal Study on Ageing (TILDA) as it provides a robust dataset for examining these relationships. TILDA is a large-scale, nationally representative survey designed specifically to explore the ageing process among older adults in Ireland. As a multi-wave data, TILDA offers an opportunity to explore how social integration may affect both cognitive and overall health over time.

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)

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)

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)

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)

The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of ``functional data analysis,'' it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analysis a random function typically represents a patient, or subject, who is observed at only a small number of randomly distributed points, with nonnegligible measurement error. Nevertheless, essentially the same methods can be used in both these cases, as well as in the vast number of settings that lie between them. How is performance affected by the sampling plan? In this paper we answer that question. We show that if there is a sample of $n$ functions, or subjects, then estimation of eigenvalues is a semiparametric problem, with root-$n$ consistent estimators, even if only a few observations are made of each function, and if each observation is encumbered by noise. However, estimation of eigenfunctions becomes a nonparametric problem when observations are sparse. The optimal convergence rates in this case are those which pertain to more familiar function-estimation settings. We also describe the effects of sampling at regularly spaced points, as opposed to random points. In particular, it is shown that there are often advantages in sampling randomly. However, even in the case of noisy data there is a threshold sampling rate (depending on the number of functions treated) above which the rate of sampling (either randomly or regularly) has negligible impact on estimator performance, no matter whether eigenfunctions or eigenvectors are being estimated.

A review of the literature on the validity and reliability of survey data is presented prior to an analysis of the reliability of selected questions in the Second Followup Questionnaire of the National Longitudinal Study of the High School Class of 1972 (NLS). The reliability study includes an evaluation of test-retest reliability as a function of data collection procedures (whether mail-in or personal interview), item characteristics (response format, item content, and item length), respondent characteristics (sex, ethnicity, socioeconomic status, and academic ability), and the interaction of these diverse factors. At issue is not only the quality of NLS questionnaire data, but the consideration of guidelines for analyzing survey data and improving the quality of that data. In lieu of an empirical analysis of validity that might have encroached on the privacy of respondents, the validity check was based on a literature review, focusing on NLS types of items. The validity study also focused on data collection procedures, item characteristics, respondent characteristics, and their interactions. An overview of the purpose of the NLS and the methodologies employed are also included. The Short Form of the Second Follow-up Questionnaire is appended. (Author/EVH)

A review of the literature on the validity and reliability of survey data is presented prior to an analysis of the reliability of selected questions in the Second Followup Questionnaire of the National Longitudinal Study of the High School Class of 1972 (NLS). The reliability study includes an evaluation of test-retest reliability as a function of data collection procedures (whether mail-in or personal interview), item characteristics (response format, item content, and item length), respondent characteristics (sex, ethnicity, socioeconomic status, and academic ability), and the interaction of these diverse factors. At issue is not only the quality of NLS questionnaire data, but the consideration of guidelines for analyzing survey data and improving the quality of that data. In lieu of an empirical analysis of validity that might have encroached on the privacy of respondents, the validity check was based on a literature review, focusing on NLS types of items. The validity study also focused on data collection procedures, item characteristics, respondent characteristics, and their interactions. An overview of the purpose of the NLS and the methodologies employed are also included. The Short Form of the Second Follow-up Questionnaire is appended. (Author/EVH)

Static and oscillatory wind tunnel data are presented for a 10-percent-scale model of an F-16XL aircraft. Static data include the effect of angle of attack, sideslip angle, and control surface deflections on aerodynamic coefficients. Dynamic data from small-amplitude oscillatory tests are presented at nominal values of angle of attack between 20 and 60 degrees. Model oscillations were performed at five frequencies from 0.6 to 2.9 Hz and one amplitude of 5 degrees. A simple harmonic analysis of the oscillatory data provided Fourier coefficients associated with the in-phase and out-of-phase components of the aerodynamic coefficients. A strong dependence of the oscillatory data on frequency led to the development of models with unsteady terms in the form of indicial functions. Two models expressing the variation of the in-phase and out-of-phase components with angle of attack and frequency were proposed and their parameters estimated from measured data.

This article is from Frontiers in Genetics , volume 5 . Abstract Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis (Mtb) that affects millions of people worldwide. The majority of individuals who are exposed to Mtb develop latent infections, in which an immunological response to Mtb antigens is present but there is no clinical evidence of disease. Because currently available tests cannot differentiate latent individuals who are at low risk from those who are highly susceptible to developing active disease, there is considerable interest in the identification of diagnostic biomarkers that can predict reactivation of latent TB. We present results from our analysis of a controlled longitudinal experiment in which a group of rhesus macaques were exposed to a low dose of Mtb to study their progression to latent infection or active disease. Subsets of the animals were then euthanized at scheduled time points, and granulomas taken from their lungs were assayed for gene expression using microarrays. The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach. Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression. Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.

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)

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.

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 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 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.

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)

A longitudinal data analysis of the 1985 and 1992 Surveys of Officers and Enlisted Personnel and Their Spouses was used to assess the perceptions and attitudes of respondents about various aspects of military life over time. The seven-year time span between the surveys represented a unique period in military history with the occurrence of such events as Operations Desert Shield/Storm, the extensive military drawdown, and the changing role of the military (e.g., peace keeping). The general research approach was to investigate the outcome variables in relationship to a broad range of predictors. Major findings of this study were: (a) the attitudes and opinions of active-duty military members remained stable at the aggregate level from 1985 to 1992; (b) active-duty military members in 1992 had much lower expectations of their chosen careers than did the active-duty military members surveyed in 1985; (c) expected total years of service was the best predictor of officers' career intentions; and (d) family readiness for deployment increased from 1985 to 1992.

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.

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.