Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.
To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables and confounders; however, for many complex phenot...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0266752 |
| _version_ | 1811155394459860992 |
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| author | Kirsten Voorhies Ruofan Bie John E Hokanson Scott T Weiss Ann Chen Wu Julian Hecker Georg Hahn Dawn L Demeo Edwin Silverman Michael H Cho Christoph Lange Sharon M Lutz |
| author_facet | Kirsten Voorhies Ruofan Bie John E Hokanson Scott T Weiss Ann Chen Wu Julian Hecker Georg Hahn Dawn L Demeo Edwin Silverman Michael H Cho Christoph Lange Sharon M Lutz |
| author_sort | Kirsten Voorhies |
| collection | DOAJ |
| description | To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables and confounders; however, for many complex phenotypes, the assumptions of the linear regression models are not always met. As an alternative, we used neural networks for the modeling of complex phenotypes and covariate adjustments. We compared the prediction accuracy of the neural network models to that of classical approaches based on linear regression. Using data from the UK Biobank, COPDGene study, and Childhood Asthma Management Program (CAMP), we examined the features of neural networks in this context and compared them with traditional regression approaches for prediction of three outcomes: forced expiratory volume in one second (FEV1), age at smoking cessation, and log transformation of age at smoking cessation (due to age at smoking cessation being right-skewed). We used mean squared error to compare neural network and regression models, and found the models performed similarly unless the observed distribution of the phenotype was skewed, in which case the neural network had smaller mean squared error. Our results suggest neural network models have an advantage over standard regression approaches when the phenotypic distribution is skewed. However, when the distribution is not skewed, the approaches performed similarly. Our findings are relevant to studies that analyze phenotypes that are skewed by nature or where the phenotype of interest is skewed as a result of the ascertainment condition. |
| first_indexed | 2024-04-10T04:33:30Z |
| format | Article |
| id | doaj.art-02d45b6ef4484a998648d7ed86368c05 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T04:33:30Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-02d45b6ef4484a998648d7ed86368c052023-03-10T05:32:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026675210.1371/journal.pone.0266752Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.Kirsten VoorhiesRuofan BieJohn E HokansonScott T WeissAnn Chen WuJulian HeckerGeorg HahnDawn L DemeoEdwin SilvermanMichael H ChoChristoph LangeSharon M LutzTo increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables and confounders; however, for many complex phenotypes, the assumptions of the linear regression models are not always met. As an alternative, we used neural networks for the modeling of complex phenotypes and covariate adjustments. We compared the prediction accuracy of the neural network models to that of classical approaches based on linear regression. Using data from the UK Biobank, COPDGene study, and Childhood Asthma Management Program (CAMP), we examined the features of neural networks in this context and compared them with traditional regression approaches for prediction of three outcomes: forced expiratory volume in one second (FEV1), age at smoking cessation, and log transformation of age at smoking cessation (due to age at smoking cessation being right-skewed). We used mean squared error to compare neural network and regression models, and found the models performed similarly unless the observed distribution of the phenotype was skewed, in which case the neural network had smaller mean squared error. Our results suggest neural network models have an advantage over standard regression approaches when the phenotypic distribution is skewed. However, when the distribution is not skewed, the approaches performed similarly. Our findings are relevant to studies that analyze phenotypes that are skewed by nature or where the phenotype of interest is skewed as a result of the ascertainment condition.https://doi.org/10.1371/journal.pone.0266752 |
| spellingShingle | Kirsten Voorhies Ruofan Bie John E Hokanson Scott T Weiss Ann Chen Wu Julian Hecker Georg Hahn Dawn L Demeo Edwin Silverman Michael H Cho Christoph Lange Sharon M Lutz Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. PLoS ONE |
| title | Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. |
| title_full | Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. |
| title_fullStr | Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. |
| title_full_unstemmed | Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. |
| title_short | Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. |
| title_sort | covariate adjustment of spirometric and smoking phenotypes the potential of neural network models |
| url | https://doi.org/10.1371/journal.pone.0266752 |
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