A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data
© 2021 John Wiley & Sons Ltd..
A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:20 |
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| Enthalten in: |
Pharmaceutical statistics - 20(2021), 6 vom: 15. Nov., Seite 1249-1264 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shi, Luoxi [VerfasserIn] |
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| Links: |
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| Themen: |
Area under the curve |
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| Anmerkungen: |
Date Completed 24.01.2022 Date Revised 02.11.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/pst.2146 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM326989625 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021 John Wiley & Sons Ltd. | ||
| 520 | |a A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a area under the curve | |
| 650 | 4 | |a biomarker | |
| 650 | 4 | |a longitudinal | |
| 650 | 4 | |a missing data | |
| 650 | 4 | |a mixed effects model | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 700 | 1 | |a Hatsukami, Dorothy K |e verfasserin |4 aut | |
| 700 | 1 | |a Koopmeiners, Joseph S |e verfasserin |4 aut | |
| 700 | 1 | |a Le, Chap T |e verfasserin |4 aut | |
| 700 | 1 | |a Benowitz, Neal L |e verfasserin |4 aut | |
| 700 | 1 | |a Donny, Eric C |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Xianghua |e verfasserin |4 aut | |
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