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This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health. ¿
| Autorius: | Jeffrey R. Wilson, (Din) Ding-Geng Chen, Elsa Vazquez-Arreola, |
| Serija: | Emerging Topics in Statistics and Biostatistics |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 192 |
| ISBN-10: | 3030489035 |
| ISBN-13: | 9783030489038 |
| Formatas: | 241 x 160 x 17 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates“
