This webpage provides information about the research project “Methods for the Analysis of Longitudinal Dyadic Data, with Applications to Intergenerational Exchanges of Family Support”. The three-year ...
Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...
Inappropriate choice of working correlation structure in generalized estimating equations (GEE) could lead to inefficient parameter estimation while impractical normality assumption in likelihood ...
Purpose This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) ...
This course is available on the MPA in Data Science for Public Policy, MRes in Management (Marketing), MSc in Health Data Science, MSc in Social Research Methods, MSc in Statistics, MSc in Statistics ...
Chongzhi Di develops statistical learning methods for analyzing complex, high-dimensional, and real-time data in biomedical and behavioral research. His work focuses on wearable devices, accelerometry ...
Field‐traditional decayed, missing, filled surfaces (dmfs) increments require complete follow‐up, only using initial and final visits. Repeated dmfs scores complicate sophisticated statistical models, ...
In longitudinal clinical trials, missing data is a threat to scientific integrity. Whether due to patient dropouts, missed ...
Longitudinal data analysis encompasses a range of statistical methodologies that examine data collected over extended periods, enabling researchers to disentangle temporal effects and dynamic ...
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