Poststratification fusion learning in longitudinal data analysis
| العنوان: | Poststratification fusion learning in longitudinal data analysis |
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| المؤلفون: | Peter X.-K. Song, Lu Tang |
| المصدر: | Biometrics. 77:914-928 |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Data Analysis, Statistics and Probability, Computer science, Feature selection, Machine learning, computer.software_genre, 01 natural sciences, Regularization (mathematics), General Biochemistry, Genetics and Molecular Biology, Statistical power, 010104 statistics & probability, 03 medical and health sciences, Statistical inference, medicine, Humans, Computer Simulation, Attrition, Longitudinal Studies, 0101 mathematics, Generalized estimating equation, 030304 developmental biology, 0303 health sciences, Models, Statistical, General Immunology and Microbiology, business.industry, Applied Mathematics, General Medicine, medicine.disease, Sample size determination, Parametric model, Artificial intelligence, General Agricultural and Biological Sciences, business, computer |
| الوصف: | Stratification is a very commonly used approach in biomedical studies to handle sample heterogeneity arising from, for examples, clinical units, patient subgroups, or missing-data. A key rationale behind such approach is to overcome potential sampling biases in statistical inference. Two issues of such stratification-based strategy are (i) whether individual strata are sufficiently distinctive to warrant stratification, and (ii) sample size attrition resulted from the stratification may potentially lead to loss of statistical power. To address these issues, we propose a penalized generalized estimating equations approach to reducing the complexity of parametric model structures due to excessive stratification. Specifically, we develop a data-driven fusion learning approach for longitudinal data that improves estimation efficiency by integrating information across similar strata, yet still allows necessary separation for stratum-specific conclusions. The proposed method is evaluated by simulation studies and applied to a motivating example of psychiatric study to demonstrate its usefulness in real world settings. |
| تدمد: | 1541-0420 0006-341X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::365cc9e438358d2ce5ab7f4acb97ad16Test https://doi.org/10.1111/biom.13333Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....365cc9e438358d2ce5ab7f4acb97ad16 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15410420 0006341X |
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