Generalized single index modeling of longitudinal data with multiple binary responses
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Wiley Open Access
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| Format: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867011558772572160 |
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| author | Zibo Tian Peihua Qiu |
| author_facet | Zibo Tian Peihua Qiu Zibo Tian Peihua Qiu |
| collection | Wiley Open Access |
| contents | Generalized single index modeling of longitudinal data with multiple binary responses Zibo Tian Peihua Qiu Statistics in Medicine In health and clinical research, medical indices (eg, BMI) are commonly used for monitoring and/or predicting health outcomes of interest. While single‐index modeling can be used to construct such indices, methods to use single‐index models for analyzing longitudinal data with multiple correlated binary responses are underdeveloped, although there are abundant applications with such data (eg, prediction of multiple medical conditions based on longitudinally observed disease risk factors). This article aims to fill the gap by proposing a generalized single‐index model that can incorporate multiple single indices and mixed effects for describing observed longitudinal data of multiple binary responses. Compared to the existing methods focusing on constructing marginal models for each response, the proposed method can make use of the correlation information in the observed data about different responses when estimating different single indices for predicting response variables. Estimation of the proposed model is achieved by using a local linear kernel smoothing procedure, together with methods designed specifically for estimating single‐index models and traditional methods for estimating generalized linear mixed models. Numerical studies show that the proposed method is effective in various cases considered. It is also demonstrated using a dataset from the English Longitudinal Study of Aging project. 10.1002/sim.10139 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.10139 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10139 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Generalized single index modeling of longitudinal data with multiple binary responses Zibo Tian Peihua Qiu Statistics in Medicine Generalized single index modeling of longitudinal data with multiple binary responses Zibo Tian Peihua Qiu Statistics in Medicine In health and clinical research, medical indices (eg, BMI) are commonly used for monitoring and/or predicting health outcomes of interest. While single‐index modeling can be used to construct such indices, methods to use single‐index models for analyzing longitudinal data with multiple correlated binary responses are underdeveloped, although there are abundant applications with such data (eg, prediction of multiple medical conditions based on longitudinally observed disease risk factors). This article aims to fill the gap by proposing a generalized single‐index model that can incorporate multiple single indices and mixed effects for describing observed longitudinal data of multiple binary responses. Compared to the existing methods focusing on constructing marginal models for each response, the proposed method can make use of the correlation information in the observed data about different responses when estimating different single indices for predicting response variables. Estimation of the proposed model is achieved by using a local linear kernel smoothing procedure, together with methods designed specifically for estimating single‐index models and traditional methods for estimating generalized linear mixed models. Numerical studies show that the proposed method is effective in various cases considered. It is also demonstrated using a dataset from the English Longitudinal Study of Aging project. 10.1002/sim.10139 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Generalized single index modeling of longitudinal data with multiple binary responses |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10139 |