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Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.

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Title: Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.
Authors: Brantner CL; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Nguyen TQ; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Tang T; Department of Statistical Science, Duke University, Durham, North Carolina, USA., Zhao C; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA., Hong H; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA., Stuart EA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Source: Statistics in medicine [Stat Med] 2024 Mar 30; Vol. 43 (7), pp. 1291-1314. Date of Electronic Publication: 2024 Jan 25.
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Brantner+CL%22">Brantner CL</searchLink>; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.<br /><searchLink fieldCode="AU" term="%22Nguyen+TQ%22">Nguyen TQ</searchLink>; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.<br /><searchLink fieldCode="AU" term="%22Tang+T%22">Tang T</searchLink>; Department of Statistical Science, Duke University, Durham, North Carolina, USA.<br /><searchLink fieldCode="AU" term="%22Zhao+C%22">Zhao C</searchLink>; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.<br /><searchLink fieldCode="AU" term="%22Hong+H%22">Hong H</searchLink>; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.<br /><searchLink fieldCode="AU" term="%22Stuart+EA%22">Stuart EA</searchLink>; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
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  Data: <searchLink fieldCode="JN" term="%228215016%22">Statistics in medicine</searchLink> [Stat Med] 2024 Mar 30; Vol. 43 (7), pp. 1291-1314. <i>Date of Electronic Publication: </i>2024 Jan 25.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley%22">Wiley </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>8215016 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1097-0258 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2202776715%22">02776715 </searchLink><i>NLM ISO Abbreviation: </i>Stat Med <i>Subsets: </i>MEDLINE
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