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Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency

OBJECTIVES: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case‐control study of vitamin D deficiency predicting subsequent suici...

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Autores principales: Zubizarreta, Jose R., Umhau, John C., Deuster, Patricia A., Brenner, Lisa A., King, Andrew J., Petukhova, Maria V., Sampson, Nancy A., Tizenberg, Boris, Upadhyaya, Sanjaya K., RachBeisel, Jill A., Streeten, Elizabeth A., Kessler, Ronald C., Postolache, Teodor T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886287/
https://www.ncbi.nlm.nih.gov/pubmed/34739164
http://dx.doi.org/10.1002/mpr.1897
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author Zubizarreta, Jose R.
Umhau, John C.
Deuster, Patricia A.
Brenner, Lisa A.
King, Andrew J.
Petukhova, Maria V.
Sampson, Nancy A.
Tizenberg, Boris
Upadhyaya, Sanjaya K.
RachBeisel, Jill A.
Streeten, Elizabeth A.
Kessler, Ronald C.
Postolache, Teodor T.
author_facet Zubizarreta, Jose R.
Umhau, John C.
Deuster, Patricia A.
Brenner, Lisa A.
King, Andrew J.
Petukhova, Maria V.
Sampson, Nancy A.
Tizenberg, Boris
Upadhyaya, Sanjaya K.
RachBeisel, Jill A.
Streeten, Elizabeth A.
Kessler, Ronald C.
Postolache, Teodor T.
author_sort Zubizarreta, Jose R.
collection PubMed
description OBJECTIVES: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case‐control study of vitamin D deficiency predicting subsequent suicide. METHODS: We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm. RESULTS: The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic. CONCLUSIONS: In addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor.
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spelling pubmed-88862872022-03-04 Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency Zubizarreta, Jose R. Umhau, John C. Deuster, Patricia A. Brenner, Lisa A. King, Andrew J. Petukhova, Maria V. Sampson, Nancy A. Tizenberg, Boris Upadhyaya, Sanjaya K. RachBeisel, Jill A. Streeten, Elizabeth A. Kessler, Ronald C. Postolache, Teodor T. Int J Methods Psychiatr Res Original Articles OBJECTIVES: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case‐control study of vitamin D deficiency predicting subsequent suicide. METHODS: We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm. RESULTS: The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic. CONCLUSIONS: In addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor. John Wiley and Sons Inc. 2021-11-05 /pmc/articles/PMC8886287/ /pubmed/34739164 http://dx.doi.org/10.1002/mpr.1897 Text en © 2021 The Authors. International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Zubizarreta, Jose R.
Umhau, John C.
Deuster, Patricia A.
Brenner, Lisa A.
King, Andrew J.
Petukhova, Maria V.
Sampson, Nancy A.
Tizenberg, Boris
Upadhyaya, Sanjaya K.
RachBeisel, Jill A.
Streeten, Elizabeth A.
Kessler, Ronald C.
Postolache, Teodor T.
Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency
title Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency
title_full Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency
title_fullStr Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency
title_full_unstemmed Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency
title_short Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency
title_sort evaluating the heterogeneous effect of a modifiable risk factor on suicide: the case of vitamin d deficiency
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886287/
https://www.ncbi.nlm.nih.gov/pubmed/34739164
http://dx.doi.org/10.1002/mpr.1897
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