Single-Index Measurement Error Jump Regression Model in Alzheimer's Disease Studies.
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| Titel: | Single-Index Measurement Error Jump Regression Model in Alzheimer's Disease Studies. |
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| Autoren: | Zhao YY; School of Statistics and Data Science, Nanjing Audit University, Nanjing, China., Lei K; Department of Statistics, Florida State University, Tallahassee, Florida, USA., Liu Y; School of Statistics and Data Science, Nanjing Audit University, Nanjing, China.; Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia., Tan Y; Department of Statistics, Florida State University, Tallahassee, Florida, USA., Ismail N; Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia., Ridzuan Mohd Tajuddin R; Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia., Liu R; Department of Statistics, University of Georgia, Athens, GA, USA., Huang C; Department of Epidemiology & Biostatistics, University of Georgia, Athens, GA, USA. |
| Körperschaften: | Alzheimer's Disease Neuroimaging Initiative |
| Quelle: | Statistics in medicine [Stat Med] 2025 Mar 30; Vol. 44 (7), pp. e70081. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| MeSH-Schlagworte: | Alzheimer Disease*/diagnostic imaging , Models, Statistical*, Humans ; Regression Analysis ; Computer Simulation ; Neuroimaging/statistics & numerical data ; Aged ; Female ; Male |
| Abstract: | Alzheimer's disease (AD) is the major cause of dementia in the elderly, and investigations on the impact of risk factors on neurocognitive performance are crucial in preventative treatment. While existing statistical regression models, such as single-index models, have proven effective tools for uncovering the relationship between the neurocognitive scores and covariates of interest such as demographic information, clinical variables, and neuroimaging features, limited research has explored scenarios where jump discontinuities exist in the regression patterns and the covariates are unobservable but measured with errors, which are common in real applications. To address these challenges, we propose a single-index measurement error jump regression model (SMEJRM) that can handle both jump discontinuities and measurement errors in image covariates introduced by different image processing software. This development is motivated by data from 168 patients in the Alzheimer's Disease Neuroimaging Initiative. We establish both the estimation procedure and the corresponding asymptotic results. Simulation studies are conducted to evaluate the finite sample performance of our SMEJRM and the estimation procedure. The real application reveals that jump discontinuities do exist in the relationship between neurocognitive scores and some covariates of interest in this study. (© 2025 John Wiley & Sons Ltd.) |
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| Grant Information: | DMS-1953087 National Science Foundation; 12071220 National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: Alzheimer's disease; clustering‐based estimation; jump discontinuities; measurement error; single‐index model |
| Entry Date(s): | Date Created: 20250414 Date Completed: 20250414 Latest Revision: 20250414 |
| Update Code: | 20250414 |
| DOI: | 10.1002/sim.70081 |
| PMID: | 40226882 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Alzheimer's disease (AD) is the major cause of dementia in the elderly, and investigations on the impact of risk factors on neurocognitive performance are crucial in preventative treatment. While existing statistical regression models, such as single-index models, have proven effective tools for uncovering the relationship between the neurocognitive scores and covariates of interest such as demographic information, clinical variables, and neuroimaging features, limited research has explored scenarios where jump discontinuities exist in the regression patterns and the covariates are unobservable but measured with errors, which are common in real applications. To address these challenges, we propose a single-index measurement error jump regression model (SMEJRM) that can handle both jump discontinuities and measurement errors in image covariates introduced by different image processing software. This development is motivated by data from 168 patients in the Alzheimer's Disease Neuroimaging Initiative. We establish both the estimation procedure and the corresponding asymptotic results. Simulation studies are conducted to evaluate the finite sample performance of our SMEJRM and the estimation procedure. The real application reveals that jump discontinuities do exist in the relationship between neurocognitive scores and some covariates of interest in this study.<br /> (© 2025 John Wiley & Sons Ltd.) |
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| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.70081 |