Journal articles: 'Longitudinal data sets'

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

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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 (March 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 (July 2, 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 (July 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 (November 1, 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 web address that contains more detailed information about each survey.

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Carstensen, Laura L. "Peril in the Prediction of Psychopathology From Longitudinal Data Sets." Contemporary Psychology: A Journal of Reviews 34, no. 4 (April 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 (January 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 (June 2010): 266–79. http://dx.doi.org/10.1002/j.1556-6978.2010.tb00102.x.

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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 (January 2, 2016): 21–41. http://dx.doi.org/10.1080/00207659.2016.1130416.

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

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Vendittelli, R. "STRESS, PHYSICAL ACTIVITY, AND AGING: COORDINATED ANALYSES OF TWO LONGITUDINAL DATA SETS." Innovation in Aging 1, suppl_1 (June 30, 2017): 418. http://dx.doi.org/10.1093/geroni/igx004.1505.

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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 (June 12, 2010): 81–94. http://dx.doi.org/10.1007/s12561-010-9022-1.

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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 (December 4, 2020): 2379. http://dx.doi.org/10.21105/joss.02379.

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

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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 (December 20, 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 coefficient for longitudinal overdispersed Poisson data. Further, the indices of intra-, inter-, and total agreement through generalized estimating equations are also developed considering the correlation structure of longitudinal count repeated measurements. Simulation studies are conducted to compare the performance of the modified variance component and generalized estimating equation approaches for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is used for illustration. In conclusion, the modified variance component approach performs outstandingly well with small mean square errors and nominal 95% coverage rates. The generalized estimating equation approach provides in model assumption flexibility of correlation structures for repeated measurements to produce satisfactory concordance correlation coefficient estimation results.

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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 (December 1, 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 that leverages technical replicate samples profiled at multiple timepoints/batches, so-called “bridge samples”, to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinal ComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

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Wang, Qiang, Dong Yu, Jinyu Zhou, and Chaowu Jin. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm." Sustainability 15, no. 9 (April 28, 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 derive the dependencies between task sets and data sets. According to the dependency between task sets and data sets, the mathematical model was established in combination with the actual data transmission of the power grid. The optimal transmission correspondence between each data set and the data center was calculated. An improved Simulated Annealing algorithm solves the longitudinal and horizontal penetration problem between multi-level data centers. When generating a new solution, the Grey Wolf algorithm provides direction for finding the optimal solution. This paper integrated the existing business data and computational storage resources in the smart grid to establish a mathematical model of the affiliation between data centers and data sets. The optimal distribution of the data set was calculated, and the optimally distributed data set was stored in a distributed physical disk. Arithmetic examples were used to analyze the efficiency and stability of several algorithms to verify the improved algorithm’s advantages, and the improved algorithms’ effectiveness was confirmed by simulation.

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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 (August 19, 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 article, we use National Early Intervention Longitudinal Study data to demonstrate the use of inverse probability of treatment weighting, a quasi-experimental methodology based on propensity scores that can be used to reduce selection bias and examine plausible causal relationships. We discuss the advantages and disadvantages of this approach, and implications for its use in early intervention research.

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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 (February 2, 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 these results. The practical consequences of these results are illustrated with an example from the Scottish Longitudinal Study.

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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 (July 2007): 889–93. http://dx.doi.org/10.1016/j.mri.2007.03.004.

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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 (December 31, 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 coefficients are compared with measured data and previous similar studies. These comparisons have been showed that tracer technique by using second moment is the most accurate prediction of the longitudinal and transverse dispersions coefficients than the other techniques.

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Verboon, Peter, and Ron Pat-El. "Clustering longitudinal data using R: A Monte Carlo Study." Methodology 18, no. 2 (June 30, 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 methods are available in R. Results show that the k-means method performs consistently well in recovering the known clustering structure. The mixture model method performs reasonably well, but the change indices method has problems with smaller data sets.

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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 (June 30, 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 identification and treatment. NBSTRN developed a consensus-based process using clinical care experts to create, maintain, and evolve question and answer sets organized into common data elements (CDEs). The LPDR contains 24,172 core and disease specific CDEs for 118 rare genetic diseases, and the CDEs are being made available through the NIH CDE Repository. The number of CDEs for each condition average of 2200 with a range from 69 to 7944. The LPDR is used by state NBS programs, clinical researchers, and community-based organizations. Case level, de-identified data sets are available for secondary research and data mining. The development of the LPDR for longitudinal data gathering, sharing, and analysis supports research and facilitates the translation of discoveries into clinical practice.

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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 (June 15, 2001): 678–79. http://dx.doi.org/10.3923/jbs.2001.678.679.

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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 (December 14, 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 Dual Auroral Radar Network (SuperDARN), total electron contents (TECs) from the Madrigal database, plasma data from the Millstone Hill incoherent scatter radar (MHISR), solar wind and geomagnetic indices from OMNIWeb, and regional auroral electrojet indices from SuperMAG. The observations showed that the positions of SEDs shifted from local noon over the European sector towards dusk over the American sector and simultaneously moved to lower latitudes. The peak values of SED TECs were found to be greater in the European sector and to decrease with universal time. A double SED phenomenon appeared in the North American sector, which is the first of its kind to be reported. Further analysis showed that the temporal and spatial changes in the SEDs were associated with the eastward auroral electrojet.

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Espuny Pujol, Ferran, Christina Pagel, Katherine L. Brown, James C. Doidge, Richard G. Feltbower, Rodney C. Franklin, Arturo Gonzalez-Izquierdo, 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 (May 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 intensive care data sets (Paediatric Intensive Care Audit Network; PICANet and the Case Mix Programme from the Intensive Care National Audit & Research Centre; ICNARC-CMP), administrative hospital episodes (hospital episode statistics; HES inpatient, outpatient, accident and emergency; A&E) and mortality registry data.ParticipantsPatients with any CHD procedure recorded in NCHDA between April 2000 and March 2017 from public hospitals.Primary and secondary outcome measuresPrimary: number of linked records, number of unique patients and number of generated spells of care. Secondary: quality and completeness of linkage.ResultsThere were 143 862 records in NCHDA relating to 96 041 unique patients. We identified 65 797 linked PICANet patient admissions, 4664 linked ICNARC-CMP admissions and over 6 million linked HES episodes of care (1.1M inpatient, 4.7M outpatient). The linked data set had 4 908 153 spells of care after quality checks, with a median (IQR) of 3.4 (1.8–6.3) spells per patient-year. Where linkage was feasible (in terms of year and centre), 95.6% surgical procedure records were linked to a corresponding HES record, 93.9% paediatric (cardiac) surgery procedure records to a corresponding PICANet admission and 76.8% adult surgery procedure records to a corresponding ICNARC-CMP record.ConclusionsWe successfully linked four national data sets to the core data set of all CHD procedures performed between 2000 and 2017. This will enable a much richer analysis of longitudinal patient journeys and outcomes. We hope that our detailed description of the linkage process will be useful to others looking to link national data sets to address important research priorities.

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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 (December 31, 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 several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

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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 (November 17, 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, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling. Findings RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided. Practical implications The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations. Originality/value RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

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Ding, Jun, Farida Ahangari, Celia R. Espinoza, Divya Chhabra, Teodora Nicola, Xiting Yan, Charitharth V. Lal, et al. "Integrating multiomics longitudinal data to reconstruct networks underlying lung development." American Journal of Physiology-Lung Cellular and Molecular Physiology 317, no. 5 (November 1, 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 identification of active pathways in multiple or individual cell types. We then constructed a similar model for human lung development by profiling time-series human omics data sets. Several key pathways and regulators are shared between the reconstructed models. We experimentally validated the activity of a number of predicted regulators, leading to new insights about the regulation of innate immunity during lung development.

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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 (May 24, 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, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator. An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time.

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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 (October 18, 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 decade. In this article, we use semiparametric mixed models to analyze censored longitudinal data with irregularly observed repeated measures. The proposed model extends the censored linear mixed-effect model and provides more flexible modeling schemes by allowing the time effect to vary nonparametrically over time. We develop an Expectation-Maximization (EM) algorithm for maximum penalized likelihood estimation of model parameters and the nonparametric component. Further, as a byproduct of the EM algorithm, the smoothing parameter is estimated using a modified linear mixed-effects model, which is faster than alternative methods such as the restricted maximum likelihood approach. Finally, the performance of the proposed approaches is evaluated through extensive simulation studies as well as applications to data sets from acquired immune deficiency syndrome studies.

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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 (October 15, 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 (January 27, 2020): 787–95. http://dx.doi.org/10.1158/1055-9965.epi-19-0815.

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Suchomlinov, Andrej, Gediminas Čerškus, Andrej Kolosov, Ignas Rakita, Christian Aßmann, Eglė Jakimavičien, and Janina Tutkuvienė. "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 (September 1, 2016): 177–85. http://dx.doi.org/10.1127/anthranz/2016/0634.

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Wu, Pei-Chen. "Longitudinal Measurement Invariance of Beck Depression Inventory–II in Early Adolescents." Assessment 24, no. 3 (July 28, 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 fitted the data. Results of both data sets provided support for the longitudinal measurement invariance (threshold invariance) of the three-factor model, suggesting that the BDI-II measured the same construct over 3 years. The study also examined the category function of the BDI-II on the basis of the pattern of threshold estimates. Finally, the implications of the findings on the continuing use of the BDI-II are discussed.

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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 (July 19, 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 present HealthGen, a new approach for the conditional generation of synthetic EHRs that maintains an accurate representation of real patient characteristics, temporal information and missingness patterns. We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations. Synthetic conditionally generated EHRs could help increase the accessibility of longitudinal healthcare data sets and improve the generalisability of inferences made from these data sets to underrepresented populations.

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Tayfur, Gokmen. "Fuzzy, ANN, and regression models to predict longitudinal dispersion coefficient in natural streams." Hydrology Research 37, no. 2 (April 1, 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 performances although the fuzzy model had the highest accuracy rate (79%) and lowest mean relative error (0.85).

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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 (November 26, 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 Institute of Health and Welfare and relevant longitudinal studies, were identified. Data sources were screened and coded, and relevant information was extracted. Key data for eight areas related to wellbeing, namely, family and community, health, education and training, work, economic wellbeing, housing, crime and justice, and culture and leisure sources were identified. Forty individual data sets from 16 surveys and six active longitudinal studies were identified. Two data sets contained seven of the areas of wellbeing, of which one was specific to young adults in tertiary education, while the other survey was not limited to young adults. Both data sets lacked information concerning crime and justice variables, which have recently been identified as being of major concern among Australian university students. We recommend that government policy address the collection of a comprehensive data set encompassing each of the eight areas of wellbeing to inform future policy and practice.

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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 (January 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 timescales for different techniques and the resulting data deluge when combined with IM-MS are presented. Data mining self-organizing map strategies are used to rapidly filter the data, highlighting those features describing uniqueness to the query. Examples are provided in longitudinal analyses in synthetic biology and human liver exposure to acetaminophen, and in chemical biology for natural product discovery from bacterial biomes. CONCLUSIONS Matching the separation timescales of different forms of chromatography with IM-MS provides sufficient multiomics selectivity to perform untargeted systems-wide analyses. New data mining strategies provide a means for rapidly interrogating these data sets for feature prioritization and discovery in a range of applications in systems, synthetic, and chemical biology.

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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 (November 30, 2020): 281–88. http://dx.doi.org/10.1127/anthranz/2020/1203.

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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 (May 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 on separated real-world clinical data sets by using the privacy-protecting protocol. In total, the data sets contain 1304 real longitudinal patient records for 296 patients. Results These networks reached a mean F1 value of 0.955. The gold standard centralized training that is based on the union of all sets and does not take data security into consideration reaches a final value of 0.962. Conclusions Using real-world clinical data, our study shows that detection of protected health information can be secured by collaborative privacy-preserving training. In general, the approach shows the feasibility of deep learning on distributed and confidential clinical data while ensuring data protection.

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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 Benford’s Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.

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Thomson, Rachel. "The Qualitative Longitudinal Case History: Practical, Methodological and Ethical Reflections." Social Policy and Society 6, no. 4 (October 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 argued that both aspects should always be kept in play in analysis. The paper concludes by reflecting on the ethical and practical challenges associated with the case history approach, heightened by the growing demand to archive and share qualitative longitudinal data sets.

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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 (June 3, 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, Patricia A. Wood, Shaunna L. Clark, J. Mark Eddy, and Nick Ialongo. "School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Data Sets." Child Development 83, no. 1 (December 21, 2011): 351–66. http://dx.doi.org/10.1111/j.1467-8624.2011.01677.x.

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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 (January 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. delegatensis and P. radiata sample sets and the two sets combined. The relationships between laboratory-determined EL(SS) and NIR-fitted EL(SS) were good in all cases. EL(SS) was estimated in separate test sets and found to correlate well with measured EL. NIR spectra were obtained in 15-mm sections from the radiallongitudinal face of two intact P. radiata increment cores. EL(SS) of each section was estimated using the P. radiata and the combined P. radiata and E. delegatensis calibrations. NIR estimates of EL(SS) were in good agreement with SilviScan-2 determined stiffness indicating that NIR spectroscopy can be successfully used to estimate radial variation in wood stiffness of increment cores.

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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 data sets.

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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 (November 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 frailty for females and males from the English Longitudinal Study on Ageing.

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Liang, Junjie, Yanting Wu, Dongkuan Xu, and Vasant G. Honavar. "Longitudinal Deep Kernel Gaussian Process Regression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8556–64. http://dx.doi.org/10.1609/aaai.v35i10.17038.

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Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, \ie irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods. L-DKGPR effectively learns the multilevel correlation with a novel additive kernel that simultaneously accommodates both time-varying and the time-invariant effects. We derive an efficient algorithm to train L-DKGPR using latent space inducing points and variational inference. Results of extensive experiments on several benchmark data sets demonstrate that L-DKGPR significantly outperforms the state-of-the-art longitudinal data analysis (LDA) methods.

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Holman, C. D'Arcy J., John A. Bass, Diana L. Rosman, Merran B. Smith, James B. Semmens, Emma J. Glasson, Emma L. Brook, et al. "A decade of data linkage in Western Australia: strategic design, applications and benefits of the WA data linkage system." Australian Health Review 32, no. 4 (2008): 766. http://dx.doi.org/10.1071/ah080766.

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Objectives: The report describes the strategic design, steps to full implementation and outcomes achieved by the Western Australian Data Linkage System (WADLS), instigated in 1995 to link up to 40 years of data from over 30 collections for an historical population of 3.7 million. Staged development has seen its expansion, initially from a linkage key to local health data sets, to encompass links to national and local health and welfare data sets, genealogical links and spatial references for mapping applications. Applications: The WADLS has supported over 400 studies with over 250 journal publications and 35 graduate research degrees. Applications have occurred in health services utilisation and outcomes, aetiologic research, disease surveillance and needs analysis, and in methodologic research. Benefits: Longitudinal studies have become cheaper and more complete; deletion of duplicate records and correction of data artifacts have enhanced the quality of information assets; data linkage has conserved patient privacy; community machinery necessary for organised responses to health and social problems has been exercised; and the commercial return on research infrastructure investment has exceeded 1000%. Most importantly, there have been unbiased contributions to medical knowledge and identifiable advances in population health arising from the research.

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Journal articles on the topic 'Longitudinal data sets'

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