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Big Data in Computational Health Informatics
Published in Ayman El-Baz, Jasjit S. Suri, Big Data in Multimodal Medical Imaging, 2019
Ruogu Fang, Yao Xiao, Jianqiao Tian, Samira Pouyanfar, Yimin Yang, Shu-Ching Chen, S. S. Iyengar
Longitudinal data refers to the collection of repeated measurements of participant outcomes and possibly treatments or exposures [185], i.e., “the outcome variable is repeatedly measured on the same individual on multiple occasions” [186]. In recent decades, longitudinal data analysis, especially the statistical analysis of longitudinal data, has attracted more and more attention. Longitudinal studies involve the characterization of normal growth and aging, the effectiveness assessment of risk factors and treatments. It plays a key role in epidemiology, clinical research, and therapeutic evaluation. With big data analytic tools, it becomes promising to apply the longitudinal analysis of care across patients and diagnoses for identifying best care approaches.
Individual differences
Published in Sara J. Czaja, Walter R. Boot, Neil Charness, Wendy A. Rogers, Designing for Older Adults, 2019
Sara J. Czaja, Walter R. Boot, Neil Charness, Wendy A. Rogers
Intra-individual variability occurs within an individual. This type of variability is often examined in longitudinal studies where changes in individuals are measured across two or more measurement occasions, which are typically spread over years. For example, the performance of the same group of people on variables such as cognitive abilities might be measured every 5 or 10 years. Intra-individual differences among older adults may reflect differences in performance due to the aging process such as changes in visual acuity or processing speed that occur from young adulthood to old age; disease (e.g., dementia); or learning and experience (e.g., world knowledge or language skills).
Permutation Tests and Nonparametric Combination Methodology
Published in Corain Livio, Arboretti Rosa, Bonnini Stefano, Ranking of Multivariate Populations, 2017
Corain Livio, Arboretti Rosa, Bonnini Stefano
Until recently, functional data analysis (FDA) and longitudinal data analysis (LDA) have been viewed as distinct enterprises. As of a 2004 special issue of Statistica Sinica, it is seen that endeavours have been made to reconcile the two lines of research. It is worth noting that although functional data analysis and longitudinal data analysis are both devoted to analysing curves/trajectories on the same subjects, FDA and LDA are also intrinsically different (Davidian et al., 2004). Longitudinal data are involved in follow-up studies (common in biomedical sciences) that usually require several (few) measurements of the variables of interest for each individual during the period of study. They are often treated by multivariate parametric techniques that study the variation among the means during the time controlled by a number of covariates. In contrast, functional data are frequently recorded by mechanical instruments (more common in engineering and physical sciences, although also in an increasing number of biomedical problems) that collect many repeated measurements per subject. Its basic units of study are complex objects such as curves (commonly), images or shapes (information along the time of the same individual is jointly considered). Conceptually, functional data can be considered sample paths of a continuous stochastic process (Valderrama, 2007) where the usual focus is studying the covariance structure. In addition, the infinite dimensional structure of the functional data makes the links with standard nonparametric statistics (in particular with smoothing techniques) particularly strong (González-Manteiga and Vieu, 2007). Despite these differences, which involve mainly the viewpoints and ways of thinking about the data of both fields, Zhao et al. (2004) connected them, illustrating the ideas in the context of a gene expression study example, introducing LDA to the FDA viewpoint.
Social media use, loneliness and psychological distress in emerging adults
Published in Behaviour & Information Technology, 2023
Zoe Taylor, Ala Yankouskaya, Constantina Panourgia
Although we modelled effects in line with theory and previous research evidence, we collected data at one specific point. Longitudinal data would enable researchers to make definitive claims about a causal path of associations. Besides, data collection exclusively relied on self-reported measures and thus, the possibility of social desirability must be acknowledged. Furthermore, our study did not explore the precise effect of different social media platforms; rather, assessed the overall and simultaneous use of different social media platforms. Even though this reflects the virtual reality of emerging adults who normally use a diverse array of social media platforms, it also neglects particular aspects and functions of different social media platforms. For example, it is reported that passive use of image-based social media platforms is linked to negative beliefs about self and intensified feelings of dissatisfaction (Trifiro 2018). Moreover, our study did not counterbalance for the effects of environmental context in the relation between SMU and psychological distress. For instance, future research should consider that social media can be used as an escape from pressure of offline life or as a coping strategy to reduce stress (Hou et al. 2017). Likewise, for future studies it will be a noteworthy endeavour to test the impact of users’ socio-economic background, which has shown a strong link to problematic social media use (He et al. 2020).
Which factors influence the frequency of participation in longitudinal cohort studies? - An analysis of demographics, social factors, and medical preconditions in participants of the health effects in high level exposure to polychlorinated biphenyls (HELPcB) cohort
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Theresa Knaup, Andre Esser, Thomas Schettgen, Thomas Kraus, Andrea Kaifie
Drop-outs and low participation rates in longitudinal studies might lead to attrition bias. This results in underpowered trials which impairs the necessary significance and thus validity (Matthews et al. 2004; Moerbeek 2020). Therefore, it is important to understand which factors or characteristics of participants are associated with a high participation frequency. Evidence indicates that higher participation frequencies are associated with higher graduation and professional qualification. In addition, significantly more drop-outs can be observed in patients with a low socioeconomic status and adverse lifestyle habits, such as smoking (Carter et al. 2012; Goldberg et al. 2006; Siddiqui, Flay, and Hu 1996). Though many investigations on characteristics to attrition in studies exist, the results are often not homogenous. Cohort profiles, study methods and characteristics of study participants vary among those investigations and are therefore not easy to compare with this study. For example, many studies use (online-) surveys in order to assess demographics and medical data (Goldberg et al. 2006). Such surveys cause low costs and are associated with high participation numbers since the effort to participate is comparably low. In contrast, our analysis was based upon detailed examinations at the respective study visits and therefore more elaborative for both, participants and organizers. In addition, a cohort study design is more expensive and often has lower participation numbers than trials which solely used surveys. Thus, declining participation rates during the follow-ups markedly endanger the validity of cohort studies.
Longitudinal MRI data analysis in presence of measurement error but absence of replicates
Published in IISE Transactions on Healthcare Systems Engineering, 2018
Chitta Ranjan, Kamran Paynabar, Martin Reuter, Kourosh Jafari-Khouzani, the ADNI
Longitudinal data, commonly found in healthcare and medical applications, contains a series of measurements/data collected for a subject at different points in time. Several important drug efficacy analyses, such as assessment of effectiveness of a drug delivery system to cancer cells and progress of patients’ condition on administering a drug, are done with the help of such longitudinal data. Another application of longitudinal data is found in the area of patient monitoring and diagnosis using magnetic resonance imaging (MRI). It is a medical imaging technique that captures the anatomy and the physiological processes of a subject.