Longitudinal Data Analysis
By Atanu Bhattacharjee. Bayesian Approaches in Oncology Using R and OpenBUGS, 2020
1,570 citations recorded by the source corpus.
Longitudinal studies are quite common in modern clinical trials and cohort studies. Unlike cross-sectional designs, where observations from study subjects are available only at a single time point, individuals in longitudinal or cohort studies are assessed repeatedly over time. By taking advantages of multiple snapshots of a group over time, data from longitudinal studies captures both between-individual differences and within-individual dynamics, affording the opportunity to study more complicated biological, psychological, and behavioral hypotheses than their crosssectional counterparts. For example, if we want to test whether exposure to some chemical agent can cause some type of cancer, the between-subject difference observed in crosssectional data can only provide evidence of an association or correlation between the exposure and disease. The within-individual dynamics in longitudinal data allows for inference of a causal nature for such a relationship. Longitudinal data presents multiple methodological challenges in study designs and data analyses. The primary problem is the correlation among the repeated responses of the same subject. Classic models for cross-sectional data analysis such as multiple linear and logistic regressions are based on the independence of observations and thus in general do not apply to longitudinal data. For example, in
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/be42b7409f640cf4db393a8892920b4bd199a9ff
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In Management Science and Operations Research
Citations in the corpus
- A review on longitudinal data analysis with random forest (2022, neutral): “Another approach is given by Yu and Lambert (1999) [55] which can be considered as an extension from the traditional non-parametric and semi-parametric regression models for longitudinal data (see Chapter 8-12 of [14] for detailed review and discussions on traditional methods).”
- The effect of occupational self-efficacy on work performance through intrinsic work motivation (2018, background): “The longitudinal data provide information about individual change to better understand the determinants of individual development or heterogeneity in the population (Hedeker and Gibbons, 2006).”