O projecto Interacção e Conhecimento (IC) durou 12 anos. O objectivo principal consistia em estudar e promover interacções sociais, nomeadamente entre pares, criando cenários de educação formal mais inclusivos, facilitando a apropriação de conhecimentos e a mobilização/ desenvolvimento de competências dos alunos. Assumimos uma abordagem interpretativa e realizámos um estudo de caso intrínseco, sobre o IC, para estudar as transições observadas no quadro de referência teórico, nas opções metodológicas e nas práticas. Centramo-nos nas transições metodológicas, quanto aos paradigmas e aos designs de investigação, discutindo as opções tomadas a este respeito. Também iluminamos aspectos metodológicos onde não se observam transições.
Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quantitative phenotypes were computed and analyzed. Genome screen results were then compared for these longitudinal phenotypes and the results obtained using two cross-sectional designs: data collected near a single age (45 years) and data collected at a single time point. Significant linkage was obtained for nine regions (LOD scores ranging from 5.5 to 34.6) for six of the phenotypes. Using cross-sectional data, LOD scores were slightly lower for the same chromosomal regions, with two regions becoming nonsignificant and one additional region being identified. The magnitude of the LOD score was highly correlated with the heritability of each phenotype as well as the proportion of phenotypic variance due to that locus. There were no false-positive linkage results using the longitudinal data and three false-positive findings using the cross-sectional data. The three false positive results appear to be due to the kurtosis in the trait distribution, even after removing extreme outliers. Our analyses demonstrated that the use of simple longitudinal phenotypes was a powerful means to detect genes of major to moderate effect on trait variability. In only one instance was the power and heritability of the trait increased by using data from one examination. Power to detect linkage can be improved by identifying the most heritable phenotype...
Longitudinal designs in psychiatric research have many benefits, including the ability to measure the course of a disease over time. However, measuring participants repeatedly over time also leads to repeated opportunities for missing data, either through failure to answer certain items, missed assessments, or permanent withdrawal from the study. To avoid bias and loss of information, one should take missing values into account in the analysis. Several popular ways that are now being used to handle missing data, such as the last observation carried forward (LOCF), often lead to incorrect analyses. We discuss a number of these popular but unprincipled methods and describe modern approaches to classifying and analyzing data with missing values. We illustrate these approaches using data from the WECare study, a longitudinal randomized treatment study of low income women with depression.
Replication of research findings across independent longitudinal studies is essential for a cumulative and innovative developmental science. Meta-analysis of longitudinal studies is often limited by the amount of published information on particular research questions, the complexity of longitudinal designs and sophistication of analyses, and practical limits on full reporting of results. In many cases, cross-study differences in sample composition and measurements impede or lessen the utility of pooled data analysis. A collaborative, coordinated analysis approach can provide a broad foundation for cumulating scientific knowledge by facilitating efficient analysis of multiple studies in ways that maximize comparability of results and permit evaluation of study differences. The goal of such an approach is to maximize opportunities for replication and extension of findings across longitudinal studies through open access to analysis scripts and output for published results, permitting modification, evaluation, and extension of alternative statistical models, and application to additional data sets. Drawing on the cognitive aging literature as an example, we articulate some of the challenges of meta-analytic and pooled-data approaches and introduce a coordinated analysis approach as an important avenue for maximizing the comparability...
Although longitudinal designs are the only way in which age changes can be directly observed, a recurrent criticism involves to what extent retest effects may downwardly bias estimates of true age-related cognitive change. Considerable attention has been given to the problem of retest effects within mixed effects models that include separate parameters for longitudinal change over time (usually specified as a function of age) and for the impact of retest (specified as a function of number of exposures). Because time (i.e., intervals between assessment) and number of exposures are highly correlated (and are perfectly correlated in equal interval designs) in most longitudinal designs, the separation of effects of within-person change from effects of retest gains is only possible given certain assumptions (e.g., age convergence). To the extent that cross-sectional and longitudinal effects of age differ, obtained estimates of aging and retest may not be informative. The current simulation study investigated the recovery of within-person change (i.e., aging) and retest effects from repeated cognitive testing as a function of number of waves, age range at baseline, and size and direction of age-cohort differences on the intercept and age slope in age-based models of change. Significant bias and Type I error rates in the estimated effects of retest were observed when these convergence assumptions were not met. These simulation results suggest that retest effects may not be distinguishable from effects of aging-related change and age-cohort differences in typical long-term traditional longitudinal designs.
Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE is applicable to making statistical inference on regression coefficients in both balanced and unbalanced longitudinal designs and even twin and familial studies, whereas standard software platforms have several major limitations in handling these complex studies. Specifically, conventional voxel-based analyses in these software platforms involve Gaussian smoothing imaging data and then independently fitting a statistical model at each voxel. However, the conventional smoothing methods suffer from the lack of spatial adaptivity to the shape and spatial extent of region of interest and the arbitrary choice of smoothing extent, while independently fitting statistical models across voxels does not account for the spatial properties of imaging observations and noise distribution. To address such drawbacks, we adapt a powerful propagation–separation (PS) procedure to sequentially incorporate the neighboring information of each voxel and develop a new novel strategy to solely update a set of parameters of interest...
We propose novel estimation approaches for generalized varying coefficient models that are tailored for unsynchronized, irregular and infrequent longitudinal designs/data. Unsynchronized longitudinal data refers to the time-dependent response and covariate measurements for each individual measured at distinct time points. The proposed methods are motivated by data from the Comprehensive Dialysis Study (CDS). We model the potential age-varying association between infection-related hospitalization status and the inflammatory marker, C-reactive protein (CRP), within the first two years from initiation of dialysis. Traditional longitudinal modeling cannot directly be applied to unsynchronized data and no method exists to estimate time- or age-varying effects for generalized outcomes (e.g., binary or count data) to date. In addition, through the analysis of the CDS data and simulation studies, we show that preprocessing steps, such as binning, needed to synchronize data to apply traditional modeling can lead to significant loss of information in this context. In contrast, the proposed approaches discard no observation; they exploit the fact that although there is little information in a single subject trajectory due to irregularity and infrequency...
Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models.
Developmental scientists have argued that the implementation of longitudinal methods is necessary for obtaining an accurate picture of the nature and sources of developmental change (Magnusson & Cairns, 1996; Morrison & Ornstein, 1996; Magnusson & Stattin, 2006). Developmentalists studying cognition have been relatively slow to embrace longitudinal research, and thus few exemplar studies have tracked individual children’s cognitive performance over time and even fewer have examined contexts that are associated with this growth. In this article we first outline some of the benefits of implementing longitudinal designs. Using illustrations from existing studies of children’s basic cognitive development and of their school-based academic performance, we discuss when it may be appropriate to employ longitudinal (versus other) methods. We then outline methods for integrating longitudinal data into one’s research portfolio, contrasting the leveraging of existing longitudinal data sets with the launching of new longitudinal studies in order to address specific questions concerning cognitive development. Finally, for those who are interested in conducting longitudinal investigations of their own, we provide practical on-the-ground guidelines for designing and carrying out such studies of cognitive development.
One of the most important steps in biomedical longitudinal studies is choosing a good experimental design that can provide high accuracy in the analysis of results with a minimum sample size. Several methods for constructing efficient longitudinal designs have been developed based on power analysis and the statistical model used for analyzing the final results. However, development of this technology is not available to practitioners through user-friendly software. In this paper we introduce LADES (Longitudinal Analysis and Design of Experiments Software) as an alternative and easy-to-use tool for conducting longitudinal analysis and constructing efficient longitudinal designs. LADES incorporates methods for creating cost-efficient longitudinal designs, unequal longitudinal designs, and simple longitudinal designs. In addition, LADES includes different methods for analyzing longitudinal data such as linear mixed models, generalized estimating equations, among others. A study of European eels is reanalyzed in order to show LADES capabilities. Three treatments contained in three aquariums with five eels each were analyzed. Data were collected from 0 up to the 12th week post treatment for all the eels (complete design). The response under evaluation is sperm volume. A linear mixed model was fitted to the results using LADES. The complete design had a power of 88.7% using 15 eels. With LADES we propose the use of an unequal design with only 14 eels and 89.5% efficiency. LADES was developed as a powerful and simple tool to promote the use of statistical methods for analyzing and creating longitudinal experiments in biomedical research.
Genetic pleiotropy refers to the situation in which a single gene influences multiple traits and so it is considered as a major factor that underlies genetic correlation among traits. To identify pleiotropy, an important focus in genome-wide association studies (GWAS) is on finding genetic variants that are simultaneously associated with multiple traits. On the other hand, longitudinal designs are often employed in many complex disease studies, such that, traits are measured repeatedly over time within the same subject. Performing genetic association analysis simultaneously on multiple longitudinal traits for detecting pleiotropic effects is interesting but challenging. In this paper, we propose a 2-step method for simultaneously testing the genetic association with multiple longitudinal traits. In the first step, a mixed effects model is used to analyze each longitudinal trait. We focus on estimation of the random effect that accounts for the subject-specific genetic contribution to the trait; fixed effects of other confounding covariates are also estimated. This first step enables separation of the genetic effect from other confounding effects for each subject and for each longitudinal trait. Then in the second step, we perform a simultaneous association test on multiple estimated random effects arising from multiple longitudinal traits. The proposed method can efficiently detect pleiotropic effects on multiple longitudinal traits and can flexibly handle traits of different data types such as quantitative...
Researchers planning a longitudinal study typically search, more or less informally, a multivariate space of possible study designs that include dimensions such as the hypothesized true variance in change, indicator reliability, the number and spacing of measurement occasions, total study time, and sample size. The main search goal is to select a research design that best addresses the guiding questions and hypotheses of the planned study while heeding applicable external conditions and constraints, including time, money, feasibility, and ethical considerations. Because longitudinal study selection ultimately requires optimization under constraints, it is amenable to the general operating principles of optimization in computer-aided design. Based on power equivalence theory (MacCallum et al., 2010; von Oertzen, 2010), we propose a computational framework to promote more systematic searches within the study design space. Starting with an initial design, the proposed framework generates a set of alternative models with equal statistical power to detect hypothesized effects, and delineates trade-off relations among relevant parameters, such as total study time and the number of measurement occasions. We present LIFESPAN (Longitudinal Interactive Front End Study Planner)...
Cumulative risk (CR) models provide some of the most robust findings in the developmental literature, predicting numerous and varied outcomes. Typically, however, these outcomes are predicted one at a time, across different samples, using concurrent designs, longitudinal designs of short duration, or retrospective designs. We predicted that a single CR index, applied within a single sample, would prospectively predict diverse outcomes, i.e., depression, intelligence, school dropout, arrest, smoking, and physical disease from childhood to adulthood. Further, we predicted that number of risk factors would predict number of adverse outcomes (cumulative outcome; CO). We also predicted that early CR (assessed at age 5/6) explains variance in CO above and beyond that explained by subsequent risk (assessed at ages 12/13 and 19/20). The sample consisted of 284 individuals, 48% of whom were diagnosed with a speech/language disorder. Cumulative risk, assessed at 5/6-, 12/13-, and 19/20-years-old, predicted aforementioned outcomes at age 25/26 in every instance. Furthermore, number of risk factors was positively associated with number of negative outcomes. Finally, early risk accounted for variance beyond that explained by later risk in the prediction of CO. We discuss these findings in terms of five criteria posed by these data...
By improving the precision and accuracy of public health surveillance tools, we can improve cost-efficacy and obtain meaningful information to act upon. In this dissertation, we propose statistical methods for improving public health surveillance research. In Chapter 1, we introduce a pooled testing option for HIV prevalence estimation surveys to increase testing consent rates and subsequently decrease non-response bias. Pooled testing is less certain than individual testing, but, if more people to submit to testing, then it should reduce the potential for non-response bias. In Chapter 2, we illustrate technical issues in the design of neonatal tetanus elimination surveys. We address identifying the target population; using binary classification via lot quality assurance sampling (LQAS); and adjusting the design for the sensitivity of the survey instrument. In Chapter 3, we extend LQAS survey designs for monitoring malnutrition for longitudinal surveillance programs. By combining historical information with data from previous surveys, we detect spikes in malnutrition rates. Using this framework, we detect rises in malnutrition prevalence in longitudinal programs in Kenya and the Sudan. In Chapter 4, we develop a computationally efficient geostatistical disease mapping model that naturally handles model fitting issues due to temporal boundary misalignment by assuming that an underlying continuous risk surface induces spatial correlation between areas. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between 1990 and 2000. In Chapter 5...
Although a number of previous studies have speculated about the relationship between adolescent and adult gambling, there is very little prospective longitudinal data available to examine whether under-aged gambling makes a person more likely to gamble as an adult. To investigate this issue, the gambling habits of 578 young people were tracked for four years from mid-adolescence (age 15 years) into adulthood (18– 19 years) with standardised participation data collected every year. The results showed that gambling patterns in young people are subject to considerable individual variability. Only 1 in 4 young people who gambled at the age of 15 continued gambling every year and it was rare to find young people whose participation in specific activities was consistent from one year to the next. Participation patterns observed when young people were closer to leaving school were more predictive of adult gambling patterns than those obtained at a young age. The findings emphasise the potential divergence in results that arise from basing conclusions on individual-level and longitudinal analyses as opposed to cross-sectional designs and/or group level analyses.; Paul H. Delfabbro, Anthony H. Winefield and Sarah Anderson
We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly-specified model-based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type 2 diabetes mellitus.
Linear quantile regression models aim at providing a detailed and robust
picture of the (conditional) response distribution as function of a set of
observed covariates. Longitudinal data represent an interesting field of
application of such models; due to their peculiar features, they represent a
substantial challenge, in that the standard, cross-sectional, model
representation needs to be extended for dealing with such kind of data. In
fact, repeated observations from the same statistical unit poses a problem of
dependence; in a conditional perspective, this dependence could be ascribed to
sources of unobserved, individual-specific, heterogeneity. Along these lines,
quantile regression models have recently been extended to the analysis of
longitudinal, continuous, responses, by modelling dependence via time-constant
or time-varying random effects. In this manuscript, we introduce a general
quantile regression model for longitudinal, continuous, responses where
time-varying and time-constant random parameters are jointly taken into
account. A further feature of longitudinal designs is the presence of partially
incomplete sequences, due to some individuals leaving the study before its
designed end. The missing data process may produce a selection of units which
can be informative with respect to the parameters of the longitudinal data
model. To deal with the case of irretrievable drop-out...
Objective: To outline the strengths and limitations of longitudinal research designs in psychiatry, and to describe different types of longitudinal designs and methods for analyzing longitudinal data. Method: Key references on longitudinal methods were reviewed and examples drawn from literature in psychiatry and psychology. Results: Longitudinal studies provide important information regarding the incidence and developmental trajectories of mental disorders. They allow for identification of risk factors and developmental concomitants. Recent developments in statistical methods for analyzing longitudinal data provide efficient estimates of change and predictors of change over time, identification and characteristics of distinct subgroups defined by change pattern, and improved methods for obtaining unbiased population estimates when data are incomplete. Conclusion: Longitudinal designs, methods and analysis can contribute to psychiatric studies on risk factors for common mental disorders, studies of early intervention and prevention and treatment outcomes.
Silva, Simonete Pereira da; Universidade Regional do Cariri. Departamento de Educação Física. Crato, CE. Brasil; Beunen, Gaston Prudence; Katholieke Universiteit Leuven. Leuven. Bélgica; Freitas, Duarte Luiz de; Universidade da Madeira. Departamento d
Fonte: Universidade Federal de Santa Catarina. Florianópolis, SC. BrasilPublicador: Universidade Federal de Santa Catarina. Florianópolis, SC. Brasil
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; "Avaliado por Pares","Artigo Solicitado"; Literature review; "Avaliado por Pares", "Artigo Solicitado"; Revisão de literaturaFormato: application/pdf
DOI: http://dx.doi.org/10.5007/1980-0037.2013v15n1p130 O objetivo principal desta revisão é fornecer uma visão geral dos principais estudos longitudinais e longitudinais-mistos que se centraram sobre o crescimento somático, maturação biológica e, mais recentemente, também no desempenho físico. Somente foram considerados os estudos realizados na América do Norte, Europa e países de língua portuguesa. Em primeiro lugar, são apresentadas as principais considerações teóricas, características gerais, o delineamento do estudo e análise estatística multivariada dos dados. Na segunda parte, é edificado o panorama geral sobre os estudos emblemáticos de natureza longitudinal e longitudinal-mista. Finalmente, foram considerados alguns dos principais desafios que se colocam à pesquisa longitudinal.; DOI: http://dx.doi.org/10.5007/1980-0037.2013v15n1p130 The principle purpose of this review is to provide an overview of the major longitudinal and mixed longitudinal studies that focused on somatic growth, biological maturation and more recently also on physical performance. Only selected studies that were conducted in USA, Europe and Portuguese speaking countries will be considered. First, the main theoretical considerations...
The study of change in repeated measures studies or longitudinal studies (cross-sectional and/or cross-sequential) is of considerable interest in the field of developmental psychology. Qualitative and quantitative measures of interindividual and intraindividual variability can be used to capture changes in cognitive development. In the present study, through an empirical analysis of infant cognitive development, we investigate whether or not longitudinal (cross-sectional/cross-sequential) research designs can be used interchangeably with univariate or multivariate data analysis techniques. Methodologically, longitudinal data can be processed by univariate or multivariate analysis. However, the results and their interpretation may be different, even when the necessary statistical requirements are performed. Current statistical programs incorporate techniques to test for the presence of significant differences in data, regardless of whether these are evaluated by univariate or multivariate analysis. The results of this study, conducted in infants studied at three time points (18, 21 and 24 months), show that both intraindividual and interindividual variability can be detected by repeated measures analyses.