Developing and evaluating risk prediction models with panel current status data
© 2020 The International Biometric Society..
Panel current status data arise frequently in biomedical studies when the occurrence of a particular clinical condition is only examined at several prescheduled visit times. Existing methods for analyzing current status data have largely focused on regression modeling based on commonly used survival models such as the proportional hazards model and the accelerated failure time model. However, these procedures have the limitations of being difficult to implement and performing sub-optimally in relatively small sample sizes. The performance of these procedures is also unclear under model misspecification. In addition, no methods currently exist to evaluate the prediction performance of estimated risk models with panel current status data. In this paper, we propose a simple estimator under a general class of nonparametric transformation (NPT) models by fitting a logistic regression working model and demonstrate that our proposed estimator is consistent for the NPT model parameter up to a scale multiplier. Furthermore, we propose nonparametric estimators for evaluating the prediction performance of the risk score derived from model fitting, which is valid regardless of the adequacy of the fitted model. Extensive simulation results suggest that our proposed estimators perform well in finite samples and the regression parameter estimators outperform existing estimators under various scenarios. We illustrate the proposed procedures using data from the Framingham Offspring Study.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:77 |
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| Enthalten in: |
Biometrics - 77(2021), 2 vom: 01. Juni, Seite 599-609 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chan, Stephanie [VerfasserIn] |
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| Links: |
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| Themen: |
Current status data |
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| Anmerkungen: |
Date Completed 25.10.2021 Date Revised 16.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/biom.13317 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Date Revised 16.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
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| 520 | |a © 2020 The International Biometric Society. | ||
| 520 | |a Panel current status data arise frequently in biomedical studies when the occurrence of a particular clinical condition is only examined at several prescheduled visit times. Existing methods for analyzing current status data have largely focused on regression modeling based on commonly used survival models such as the proportional hazards model and the accelerated failure time model. However, these procedures have the limitations of being difficult to implement and performing sub-optimally in relatively small sample sizes. The performance of these procedures is also unclear under model misspecification. In addition, no methods currently exist to evaluate the prediction performance of estimated risk models with panel current status data. In this paper, we propose a simple estimator under a general class of nonparametric transformation (NPT) models by fitting a logistic regression working model and demonstrate that our proposed estimator is consistent for the NPT model parameter up to a scale multiplier. Furthermore, we propose nonparametric estimators for evaluating the prediction performance of the risk score derived from model fitting, which is valid regardless of the adequacy of the fitted model. Extensive simulation results suggest that our proposed estimators perform well in finite samples and the regression parameter estimators outperform existing estimators under various scenarios. We illustrate the proposed procedures using data from the Framingham Offspring Study | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
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| 650 | 4 | |a risk prediction | |
| 650 | 4 | |a robustness | |
| 650 | 4 | |a single-index model | |
| 700 | 1 | |a Wang, Xuan |e verfasserin |4 aut | |
| 700 | 1 | |a Jazić, Ina |e verfasserin |4 aut | |
| 700 | 1 | |a Peskoe, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Yingye |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Tianxi |e verfasserin |4 aut | |
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