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Journal articles on the topic 'Longitudinal data sets'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Valenstein, Marcia. "The Promise of Large, Longitudinal Data Sets." Psychiatric Services 64, no. 6 (2013): 503. http://dx.doi.org/10.1176/appi.ps.201300134.

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2

Schneiderman, Emet D., Charles J. Kowalski, and Stephen M. Willis. "Regression imputation of missing values in longitudinal data sets." International Journal of Bio-Medical Computing 32, no. 2 (1993): 121–33. http://dx.doi.org/10.1016/0020-7101(93)90051-7.

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Bône, Alexandre, Olivier Colliot, and Stanley Durrleman. "Learning the spatiotemporal variability in longitudinal shape data sets." International Journal of Computer Vision 128, no. 12 (2020): 2873–96. http://dx.doi.org/10.1007/s11263-020-01343-w.

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Prahl-Andersen, B., and C. Kowalski. "Analysis of Cohort Effects in Mixed Longitudinal Data Sets." International Journal of Sports Medicine 18, S 3 (1997): S186—S190. http://dx.doi.org/10.1055/s-2007-972712.

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Evans, William N., Helen Levy, and Kosali I. Simon. "Data Watch: Research Data in Health Economics." Journal of Economic Perspectives 14, no. 4 (2000): 203–16. http://dx.doi.org/10.1257/jep.14.4.203.

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In this paper, we discuss some important data sets that can be used by economists interested in conducting research in health economics. We describe six types of data sets: health components of data sets traditionally used by economists; longitudinal surveys of health and economic behavior; data on employer-provided insurance; cross-sectional surveys of households that focus on health; data on health care providers; and vital statistics. We summarize some of the leading surveys, discuss the availability of the data, identify how researchers have utilized these data and when possible, include a

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6

Carstensen, Laura L. "Peril in the Prediction of Psychopathology From Longitudinal Data Sets." Contemporary Psychology: A Journal of Reviews 34, no. 4 (1989): 344–45. http://dx.doi.org/10.1037/027884.

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Dawson, Deborah V., and Ilene C. Siegler. "Approaches to the nonparametric analysis of limited longitudinal data sets." Experimental Aging Research 22, no. 1 (1996): 33–57. http://dx.doi.org/10.1080/03610739608253996.

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Bryan, Julia A., Norma L. Day-Vines, Cheryl Holcomb-McCoy, and Cheryl Moore-Thomas. "Using National Education Longitudinal Data Sets in School Counseling Research." Counselor Education and Supervision 49, no. 4 (2010): 266–79. http://dx.doi.org/10.1002/j.1556-6978.2010.tb00102.x.

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9

Reith, Nicholas E., Pamela Paxton, and Melanie M. Hughes. "Building Cross-National, Longitudinal Data Sets: Issues and Strategies for Implementation." International Journal of Sociology 46, no. 1 (2016): 21–41. http://dx.doi.org/10.1080/00207659.2016.1130416.

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10

Barham, Catherine, and Nasima Begum. "Time series analysis of the Labour Force Survey longitudinal data sets." Economic & Labour Market Review 1, no. 1 (2007): 48–53. http://dx.doi.org/10.1057/palgrave.elmr.1410012.

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11

Adhynugraha, Muhammad Ilham, Fadli Cahya Megawanto, Siti Vivi Octaviany, et al. "MALE UAV LONGITUDINAL STABILITY DETERMINATION USING WIND TUNNEL DATA." Jurnal Teknosains 14, no. 1 (2024): 29. https://doi.org/10.22146/teknosains.89420.

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Unmanned aerial systems have been increasing in demand for a wide range of operations, including the rapid growth of advanced navigation and communication. One of the most important things in designing an Unmanned Aerial System (UAS) is to ensure the system's stability, such as the UAS itself. This study was conducted on an in-house medium altitude long endurance (MALE) UAS aircraft. It is focused on analyzing the longitudinal stability of MALE UAS. A mathematical approach was used to analyze the longitudinal stability. A series of wind tunnel tests using a scaled model of the MALE UAS is done

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Tsai, Miao-Yu, Chia-Ni Sun, and Chao-Chun Lin. "Concordance correlation coefficients estimated by modified variance components and generalized estimating equations for longitudinal overdispersed Poisson data." Statistical Methods in Medical Research 31, no. 2 (2021): 267–86. http://dx.doi.org/10.1177/09622802211065156.

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For longitudinal overdispersed Poisson data sets, estimators of the intra-, inter-, and total concordance correlation coefficient through variance components have been proposed. However, biased estimators of quadratic forms are used in concordance correlation coefficient estimation. In addition, the generalized estimating equations approach has been used in estimating agreement for longitudinal normal data and not for longitudinal overdispersed Poisson data. Therefore, this paper proposes a modified variance component approach to develop the unbiased estimators of the concordance correlation c

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Xia, Qing, Jeffrey A. Thompson, and Devin C. Koestler. "Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)." Statistical Applications in Genetics and Molecular Biology 20, no. 4-6 (2021): 101–19. http://dx.doi.org/10.1515/sagmb-2021-0020.

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Abstract Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach tha

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Vendittelli, R. "STRESS, PHYSICAL ACTIVITY, AND AGING: COORDINATED ANALYSES OF TWO LONGITUDINAL DATA SETS." Innovation in Aging 1, suppl_1 (2017): 418. http://dx.doi.org/10.1093/geroni/igx004.1505.

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15

Lai, Tze L., Kevin H. Sun, and Samuel P. Wong. "Information Sets and Excess Zeros in Random Effects Modeling of Longitudinal Data." Statistics in Biosciences 2, no. 1 (2010): 81–94. http://dx.doi.org/10.1007/s12561-010-9022-1.

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16

Adepeju, Monsuru, Samuel Langton, and Jon Bannister. "Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets." Journal of Open Source Software 5, no. 56 (2020): 2379. http://dx.doi.org/10.21105/joss.02379.

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17

Golding, Jean. "The difficult problems of authorship in regard to large longitudinal data sets." Paediatric and Perinatal Epidemiology 15, no. 3 (2001): 207. http://dx.doi.org/10.1046/j.1365-3016.2001.015003207.x.

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18

Wang, Qiang, Dong Yu, Jinyu Zhou, and Chaowu Jin. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm." Sustainability 15, no. 9 (2023): 7388. http://dx.doi.org/10.3390/su15097388.

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Since there is a longitudinal and horizontal penetration problem between multi-level data centers in the smart grid information transmission network. Based on the improved Simulated Annealing algorithm, this paper proposes a data storage optimization model for smart grids based on Hadoop architecture. Combining the characteristics of distributed storage in cloud computing, the smart grid data are equivalent to a task-oriented data set. The smart grid information platform is flattened, equal to a collection of multiple distributed data centers. The smart grid data over time were counted to deri

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Bishop, Crystal D., Walter L. Leite, and Patricia A. Snyder. "Using Propensity Score Weighting to Reduce Selection Bias in Large-Scale Data Sets." Journal of Early Intervention 40, no. 4 (2018): 347–62. http://dx.doi.org/10.1177/1053815118793430.

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Data sets from large-scale longitudinal surveys involving young children and families have become available for secondary analysis by researchers in a variety of fields. Researchers in early intervention have conducted secondary analyses of such data sets to explore relationships between nonmalleable and malleable factors and child outcomes, and to address issues of measurement. Survey data have been used to a lesser extent to examine plausible causal relationships between variables, perhaps due to the increased likelihood of selection bias that results with nonexperimental data. In this artic

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Gubashi, Karim Rashid, and Mohammed M. Kadhem. "Estimating Longitudinal and Transverse Dispersion Coefficients in Open Channel." Tikrit Journal of Engineering Sciences 13, no. 4 (2006): 96–115. http://dx.doi.org/10.25130/tjes.13.4.05.

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Results are presented from a series of laboratory experiments conducted on an open channel. Twenty five data sets (L1-L25) have been measured to obtain the magnitude of the longitudinal dispersion coefficient and eight data sets (T1-T8) have been performed to estimate transverse mixing coefficient in channel. The method involves derivation of a new expression for the longitudinal and transverse dispersion coefficients which used routing concentration of pollutant in the advection- dispersion equation solution in water quality mathematical model. Values of longitudinal and transverse dispersion

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Raab, Gillian M., Beata Nowok, and Chris Dibben. "Practical Data Synthesis for Large Samples." Journal of Privacy and Confidentiality 7, no. 3 (2018): 67–97. http://dx.doi.org/10.29012/jpc.v7i3.407.

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We describe results on the creation and use of synthetic data that were derived in the context of a project to make synthetic extracts available for users of the UK Longitudinal Studies. A critical review of existing methods of inference from large synthetic data sets is presented. We introduce new variance estimates for use with large samples of completely synthesised data that do not require them to be generated from the posterior predictive distribution derived from the observed data and can be used with a single synthetic data set. We make recommendations on how to synthesise data based on

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22

Csapo, Istvan, Christopher M. Holland, and Charles R. G. Guttmann. "Image registration framework for large-scale longitudinal MRI data sets: strategy and validation." Magnetic Resonance Imaging 25, no. 6 (2007): 889–93. http://dx.doi.org/10.1016/j.mri.2007.03.004.

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23

Verboon, Peter, and Ron Pat-El. "Clustering longitudinal data using R: A Monte Carlo Study." Methodology 18, no. 2 (2022): 144–63. http://dx.doi.org/10.5964/meth.7143.

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The analysis of change within subjects over time is an ever more important research topic. Besides modelling the individual trajectories, a related aim is to identify clusters of subjects within these trajectories. Various methods for analyzing these longitudinal trajectories have been proposed. In this paper we investigate the performance of three different methods under various conditions in a Monte Carlo study. The first method is based on the non-parametric k-means algorithm. The second is a latent class mixture model, and the third a method based on the analysis of change indices. All met

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24

Brower, Amy, Kee Chan, Michael Hartnett, and Jennifer Taylor. "The Longitudinal Pediatric Data Resource: Facilitating Longitudinal Collection of Health Information to Inform Clinical Care and Guide Newborn Screening Efforts." International Journal of Neonatal Screening 7, no. 3 (2021): 37. http://dx.doi.org/10.3390/ijns7030037.

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The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with a condition through NBS. The Newborn Screening Translational Research Network (NBSTRN) developed the Longitudinal Pediatric Data Resource (LPDR) to capture, store, analyze, visualize, and share genomic and phenotypic data over the lifespan of NBS identified newborns to facilitate understanding of genetic disease and to assess the impact of early id

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25

Li, Bo, Huijun Le, Wenbo Li, Yiding Chen, and Libo Liu. "Longitudinal Evolution of Storm-Enhanced Densities: A Case Study." Remote Sensing 14, no. 24 (2022): 6340. http://dx.doi.org/10.3390/rs14246340.

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Due to the limitations on observational data, most storm-enhanced density (SED) studies have focused on the North American sector. The complete picture of the longitudinal evolution of SEDs is still not clear. In this study, we investigated the dynamic evolution of SEDs from the European sector to the North American sector during a geomagnetic storm that occurred on the 15 July 2012, the main phase of which lasted nearly 30 h, maintaining the stable interplanetary magnetic field (IMF) and solar wind input conditions. Multiple data sets were analyzed, including convection data from the Super Du

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26

Espuny Pujol, Ferran, Christina Pagel, Katherine L. Brown, et al. "Linkage of National Congenital Heart Disease Audit data to hospital, critical care and mortality national data sets to enable research focused on quality improvement." BMJ Open 12, no. 5 (2022): e057343. http://dx.doi.org/10.1136/bmjopen-2021-057343.

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ObjectivesTo link five national data sets (three registries, two administrative) and create longitudinal healthcare trajectories for patients with congenital heart disease (CHD), describing the quality and the summary statistics of the linked data set.DesignBespoke linkage of record-level patient identifiers across five national data sets. Generation of spells of care defined as periods of time-overlapping events across the data sets.SettingNational Congenital Heart Disease Audit (NCHDA) procedures in public (National Health Service; NHS) hospitals in England and Wales, paediatric and adult in

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27

Doering, Torsten, Nallan C. Suresh, and Dennis Krumwiede. "Measuring the effects of time: repeated cross-sectional research in operations and supply chain management." Supply Chain Management: An International Journal 25, no. 1 (2019): 122–38. http://dx.doi.org/10.1108/scm-04-2019-0142.

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Purpose Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels. Design/methodology/approach A comparison of RCS, pa

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28

Tamer KAYA, G., and Fatma CEVYK . "Estimation of Missing Values in Longitudinal Data Sets Using Regression Methods in Biological Research." Journal of Biological Sciences 1, no. 7 (2001): 678–79. http://dx.doi.org/10.3923/jbs.2001.678.679.

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29

Schramm, Catherine, Céline Vial, Anne-Catherine Bachoud-Lévi, and Sandrine Katsahian. "Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data." Statistical Methods in Medical Research 27, no. 1 (2015): 97–113. http://dx.doi.org/10.1177/0962280215621591.

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Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers sev

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Ding, Jun, Farida Ahangari, Celia R. Espinoza, et al. "Integrating multiomics longitudinal data to reconstruct networks underlying lung development." American Journal of Physiology-Lung Cellular and Molecular Physiology 317, no. 5 (2019): L556—L568. http://dx.doi.org/10.1152/ajplung.00554.2018.

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A comprehensive understanding of the dynamic regulatory networks that govern postnatal alveolar lung development is still lacking. To construct such a model, we profiled mRNA, microRNA, DNA methylation, and proteomics of developing murine alveoli isolated by laser capture microdissection at 14 predetermined time points. We developed a detailed comprehensive and interactive model that provides information about the major expression trajectories, the regulators of specific key events, and the impact of epigenetic changes. Intersecting the model with single-cell RNA-Seq data led to the identifica

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Wu, Pei-Chen. "Longitudinal Measurement Invariance of Beck Depression Inventory–II in Early Adolescents." Assessment 24, no. 3 (2016): 337–45. http://dx.doi.org/10.1177/1073191115608941.

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This study explored the longitudinal measurement invariance in the Beck Depression Inventory–II (BDI-II) in early adolescents (junior high school students). The participants were 730 early adolescents (330 boys and 400 girls), who were followed up over 3 years (in six waves). To reduce the size of longitudinal model and verify the stability of the findings, the Fall and Spring series data sets were analyzed separately. Each series includes three waves of data with about 1-year apart. It was found that the three-factor model (Negative Attitude, Performance Difficulty, and Somatic Elements) best

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Bing, Simon, Andrea Dittadi, Stefan Bauer, and Patrick Schwab. "Conditional generation of medical time series for extrapolation to underrepresented populations." PLOS Digital Health 1, no. 7 (2022): e0000074. http://dx.doi.org/10.1371/journal.pdig.0000074.

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The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact on the development of new diagnostics and therapeutic treatment options. However, access to EHRs is often restricted due to their perceived sensitive nature and associated legal concerns, and the cohorts therein typically are those seen at a specific hospital or network of hospitals and therefore not representative of the wider population of patients. Here, we

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Li, Lu, Huub Hoefsloot, Barbara M. Bakker, et al. "Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models." Metabolites 15, no. 1 (2024): 2. https://doi.org/10.3390/metabo15010002.

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Background: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. This paper introduces a novel data analysis approach to incorporate mechanistic models in metabolomics data analysis. Methods: We arranged time-resolved metabolomics measurements of plasma samples collected during a meal challenge te

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Zhong, Ping-Shou, Runze Li, and Shawn Santo. "Homogeneity tests of covariance matrices with high-dimensional longitudinal data." Biometrika 106, no. 3 (2019): 619–34. http://dx.doi.org/10.1093/biomet/asz011.

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Summary This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, a

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Mattos, Thalita B., Larissa Avila Matos, and Victor H. Lachos. "A semiparametric mixed-effects model for censored longitudinal data." Statistical Methods in Medical Research 30, no. 12 (2021): 2582–603. http://dx.doi.org/10.1177/09622802211046387.

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In longitudinal studies involving laboratory-based outcomes, repeated measurements can be censored due to assay detection limits. Linear mixed-effects (LMEs) models are a powerful tool to model the relationship between a response variable and covariates in longitudinal studies. However, the linear parametric form of linear mixed-effect models is often too restrictive to characterize the complex relationship between a response variable and covariates. More general and robust modeling tools, such as nonparametric and semiparametric regression models, have become increasingly popular in the last

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Tayfur, Gokmen. "Fuzzy, ANN, and regression models to predict longitudinal dispersion coefficient in natural streams." Hydrology Research 37, no. 2 (2006): 143–64. http://dx.doi.org/10.2166/nh.2006.0012.

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This study developed fuzzy, ANN, and regression-based models to predict longitudinal dispersion coefficient in natural streams from flow discharge data. 92 sets of field data were employed to calibrate and validate the models. 63 sets of data were used for the calibration while the remaining data were used for the validation of the models. The model-prediction results revealed the superiority of the developed models over the existing equations. The developed models predicted the measured data satisfactorily with minimum errors and maximum accuracy rates. The three models had comparable perform

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Cheng, Terence C., Nattavudh Powdthavee, and Andrew J. Oswald. "Longitudinal Evidence for a Midlife Nadir in Human Well‐Being: Results from Four Data Sets." Economic Journal 127, no. 599 (2015): 126–42. http://dx.doi.org/10.1111/ecoj.12256.

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Tian, Ye D., Harry Menegay, Kristin A. Waite, Paola G. Saroufim, Mark F. Beno, and Jill S. Barnholtz-Sloan. "Facilitating Cancer Epidemiologic Efforts in Cleveland via Creation of Longitudinal De-Duplicated Patient Data Sets." Cancer Epidemiology Biomarkers & Prevention 29, no. 4 (2020): 787–95. http://dx.doi.org/10.1158/1055-9965.epi-19-0815.

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Partridge, Stephanie, Eloise Howse, Gwynnyth Llewellyn, and Margaret Allman-Farinelli. "Adequacy of Data Sources for Investigation of Tertiary Education Student’s Wellbeing in Australia: A Scoping Review." Healthcare 6, no. 4 (2018): 136. http://dx.doi.org/10.3390/healthcare6040136.

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Young adulthood is a period of transition, which for many includes higher education. Higher education is associated with specific risks to wellbeing. Understanding the available data on wellbeing in this group may help inform the future collection of data to inform policy and practice in the sector. This scoping review aimed to identify the availability of data sources on the wellbeing of the Australian young adult population who are attending tertiary education. Using the methods of Arksey and O’Malley, data from three primary sources, i.e., Australian Bureau of Statistics, Australian Institu

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Sherrod, Stacy D., and John A. McLean. "Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies." Clinical Chemistry 62, no. 1 (2016): 77–83. http://dx.doi.org/10.1373/clinchem.2015.238261.

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Abstract BACKGROUND Untargeted multiomics data sets are obtained for samples in systems, synthetic, and chemical biology by integrating chromatographic separations with ion mobility–mass spectrometry (IM-MS) analysis. The data sets are interrogated using bioinformatics strategies to organize the data for identification prioritization. CONTENT The use of big data approaches for data mining of massive data sets in systems-wide analyses is presented. Untargeted biological data across multiomics dimensions are obtained using a variety of chromatography strategies with structural MS. Separation tim

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Suchomlinov, Andrej, Gediminas Čerškus, Andrej Kolosov, et al. "Increasing prevalence of overweight and obesity among children in Vilnius, Lithuania: comparison of two longitudinal data sets of children born in 1990 and 1996." Anthropologischer Anzeiger 73, no. 3 (2016): 177–85. http://dx.doi.org/10.1127/anthranz/2016/0634.

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42

Thomson, Rachel. "The Qualitative Longitudinal Case History: Practical, Methodological and Ethical Reflections." Social Policy and Society 6, no. 4 (2007): 571–82. http://dx.doi.org/10.1017/s1474746407003909.

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This paper describes the development of ‘case histories’ from a qualitative longitudinal data set that followed 100 young people's transitions to adulthood over a ten year period. The paper describes two stages in the analytic process: first, the forging of a case history from a longitudinal archive and second, bringing case histories into conversation with each other. The paper emphasises two aspects of a qualitative longitudinal data set: the longitudinal dimension that privileges the individual case, and the cross sectional dimension that privileges the social and the spatial context. It is

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Festag, Sven, and Cord Spreckelsen. "Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation." JMIR Formative Research 4, no. 5 (2020): e14064. http://dx.doi.org/10.2196/14064.

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Background Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. Objective In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way. Methods The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets). Five networks were trained o

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Schimleck, L. R., R. Evans, J. Ilic, and A. C. Matheson. "Estimation of wood stiffness of increment cores by near-infrared spectroscopy." Canadian Journal of Forest Research 32, no. 1 (2002): 129–35. http://dx.doi.org/10.1139/x01-176.

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The use of calibrated near-infrared (NIR) spectroscopy for predicting the radial variation of the longitudinal modulus of elasticity (EL) of increment cores is described. Sets of Eucalyptus delegatensis R.T. Baker (alpine ash) and Pinus radiata D. Don (radiata pine) samples were characterized in terms of EL(SS) (estimated stiffness based on a combination of SilviScan-2 diffractometric data and measured density (R. Evans and J. Ilic. 2001. For. Prod. J. 51(3): 5357)). NIR spectra, obtained from the radiallongitudinal face of each sample, were used to develop EL(SS) calibrations for the E. del

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Dorn, Sherman. "No more aggregate NAEP studies? [editorial]." education policy analysis archives 14 (November 20, 2006): 31. http://dx.doi.org/10.14507/epaa.v14n31.2006.

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This editorial reviews recent studies of accountability policies using National Assessment of Educational Progress (NAEP) data and compares the use of aggregate NAEP data to the availability of individual-level data from NAEP. While the individual-level NAEP data sets are restricted-access and do not give accurate point-estimates of achievement, they nonetheless provide greater opportunity to conduct more appropriate multi-level analyses with state policies as one set of variables. Policy analysts using NAEP data should still look at exclusion rates and the non-longitudinal nature of the NAEP

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McCarville, Daniel. "A data transformation process for using Benford’s Law with bounded data." Emerald Open Research 3 (November 25, 2021): 29. http://dx.doi.org/10.35241/emeraldopenres.14374.1.

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Benford’s Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford’s Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford’s Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford’s Law. This paper implements a transformation procedure which allows for assessment data to be compared to Ben

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Voll, Stacey, Graciela Muniz Terrera, and Scott Hofer. "A PSYCHOMETRICALLY ROBUST LONGITUDINAL RESEARCH MEASURE OF FRAILTY: FIVE DIMENSIONS ACROSS AGE AND TIME." Innovation in Aging 6, Supplement_1 (2022): 752. http://dx.doi.org/10.1093/geroni/igac059.2732.

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Abstract The research construct of frailty in aging requires a measure with sound psychometric properties, that is stable across longitudinal points of observation. Using Exploratory Factor Analysis and Longitudinal Mixed Methods, we developed a five-factor research measures of frailty that is robust across time. Standardized regression scores for each factor allow us to estimate the change in severity of dysfunction as individuals age. We propose a system for developing research tools for the concept of frailty in large longitudinal data sets, and present our findings of five factors of frail

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Debavelaere, Vianney, Stanley Durrleman, and Stéphanie Allassonnière. "Learning the Clustering of Longitudinal Shape Data Sets into a Mixture of Independent or Branching Trajectories." International Journal of Computer Vision 128, no. 12 (2020): 2794–809. http://dx.doi.org/10.1007/s11263-020-01337-8.

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Wood, Jeffrey J., Sarah D. Lynne-Landsman, David A. Langer, et al. "School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Data Sets." Child Development 83, no. 1 (2011): 351–66. http://dx.doi.org/10.1111/j.1467-8624.2011.01677.x.

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Fergizas, Julius, and Andrej Suchomlinov. "Changes in the prevalence of thinness among children in Vilnius, Lithuania: a comparison of two longitudinal data sets of children born in 1990 and 1996." Anthropologischer Anzeiger 77, no. 4 (2020): 281–88. http://dx.doi.org/10.1127/anthranz/2020/1203.

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