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Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.

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Title: Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.
Authors: Brantner CL; Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA.; Duke Clinical Research Institute, North Carolina, USA., Nguyen TQ; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Maryland, USA., Parikh H; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Maryland, USA.; Department of Biostatistics, Yale University, Connecticut, USA., Zhao C; Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA., Hong H; Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA.; Duke Clinical Research Institute, North Carolina, USA., Stuart EA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Maryland, USA.
Source: Journal of the Royal Statistical Society. Series C, Applied statistics [J R Stat Soc Ser C Appl Stat] 2025 Dec 12. Date of Electronic Publication: 2025 Dec 12.
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 101086541 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0035-9254 (Print) Linking ISSN: 00359254 NLM ISO Abbreviation: J R Stat Soc Ser C Appl Stat
Database: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AU" term="%22Brantner+CL%22">Brantner CL</searchLink>; Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA.; Duke Clinical Research Institute, North Carolina, USA.<br /><searchLink fieldCode="AU" term="%22Nguyen+TQ%22">Nguyen TQ</searchLink>; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Maryland, USA.<br /><searchLink fieldCode="AU" term="%22Parikh+H%22">Parikh H</searchLink>; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Maryland, USA.; Department of Biostatistics, Yale University, Connecticut, USA.<br /><searchLink fieldCode="AU" term="%22Zhao+C%22">Zhao C</searchLink>; Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA.<br /><searchLink fieldCode="AU" term="%22Hong+H%22">Hong H</searchLink>; Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA.; Duke Clinical Research Institute, North Carolina, USA.<br /><searchLink fieldCode="AU" term="%22Stuart+EA%22">Stuart EA</searchLink>; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Maryland, USA.
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  Data: <searchLink fieldCode="JN" term="%22101086541%22">Journal of the Royal Statistical Society. Series C, Applied statistics</searchLink> [J R Stat Soc Ser C Appl Stat] 2025 Dec 12. <i>Date of Electronic Publication: </i>2025 Dec 12.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Oxford+University+Press%22">Oxford University Press </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101086541 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Print <i>ISSN: </i>0035-9254 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200359254%22">00359254 </searchLink><i>NLM ISO Abbreviation: </i>J R Stat Soc Ser C Appl Stat
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        Value: 10.1093/jrsssc/qlaf068
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        Text: English
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              Text: 2025 Dec 12
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