Recalibrating single-study effect sizes using hierarchical Bayesian models
IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect s...
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| Autor*in: |
Zhipeng Cao [verfasserIn] Matthew McCabe [verfasserIn] Peter Callas [verfasserIn] Renata B. Cupertino [verfasserIn] Jonatan Ottino-González [verfasserIn] Alistair Murphy [verfasserIn] Devarshi Pancholi [verfasserIn] Nathan Schwab [verfasserIn] Orr Catherine [verfasserIn] Kent Hutchison [verfasserIn] Janna Cousijn [verfasserIn] Alain Dagher [verfasserIn] John J. Foxe [verfasserIn] Anna E. Goudriaan [verfasserIn] Robert Hester [verfasserIn] Chiang-Shan R. Li [verfasserIn] Wesley K. Thompson [verfasserIn] Angelica M. Morales [verfasserIn] Edythe D. London [verfasserIn] Valentina Lorenzetti [verfasserIn] Maartje Luijten [verfasserIn] Rocio Martin-Santos [verfasserIn] Reza Momenan [verfasserIn] Martin P. Paulus [verfasserIn] Lianne Schmaal [verfasserIn] Rajita Sinha [verfasserIn] Nadia Solowij [verfasserIn] Dan J. Stein [verfasserIn] Elliot A. Stein [verfasserIn] Anne Uhlmann [verfasserIn] Ruth J. van Holst [verfasserIn] Dick J. Veltman [verfasserIn] Reinout W. Wiers [verfasserIn] Murat Yücel [verfasserIn] Sheng Zhang [verfasserIn] Patricia Conrod [verfasserIn] Scott Mackey [verfasserIn] Hugh Garavan [verfasserIn] The ENIGMA Addiction Working Group [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Frontiers in Neuroimaging - Frontiers Media S.A., 2022, 2(2023) |
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| Übergeordnetes Werk: |
volume:2 ; year:2023 |
| Links: |
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| DOI / URN: |
10.3389/fnimg.2023.1138193 |
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| Katalog-ID: |
DOAJ098994395 |
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| 520 | |a IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. | ||
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| 700 | 0 | |a Matthew McCabe |e verfasserin |4 aut | |
| 700 | 0 | |a Peter Callas |e verfasserin |4 aut | |
| 700 | 0 | |a Renata B. Cupertino |e verfasserin |4 aut | |
| 700 | 0 | |a Jonatan Ottino-González |e verfasserin |4 aut | |
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| 700 | 0 | |a Devarshi Pancholi |e verfasserin |4 aut | |
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| 700 | 0 | |a Kent Hutchison |e verfasserin |4 aut | |
| 700 | 0 | |a Janna Cousijn |e verfasserin |4 aut | |
| 700 | 0 | |a Alain Dagher |e verfasserin |4 aut | |
| 700 | 0 | |a John J. Foxe |e verfasserin |4 aut | |
| 700 | 0 | |a Anna E. Goudriaan |e verfasserin |4 aut | |
| 700 | 0 | |a Robert Hester |e verfasserin |4 aut | |
| 700 | 0 | |a Chiang-Shan R. Li |e verfasserin |4 aut | |
| 700 | 0 | |a Wesley K. Thompson |e verfasserin |4 aut | |
| 700 | 0 | |a Angelica M. Morales |e verfasserin |4 aut | |
| 700 | 0 | |a Edythe D. London |e verfasserin |4 aut | |
| 700 | 0 | |a Valentina Lorenzetti |e verfasserin |4 aut | |
| 700 | 0 | |a Maartje Luijten |e verfasserin |4 aut | |
| 700 | 0 | |a Rocio Martin-Santos |e verfasserin |4 aut | |
| 700 | 0 | |a Reza Momenan |e verfasserin |4 aut | |
| 700 | 0 | |a Martin P. Paulus |e verfasserin |4 aut | |
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| 700 | 0 | |a Dan J. Stein |e verfasserin |4 aut | |
| 700 | 0 | |a Elliot A. Stein |e verfasserin |4 aut | |
| 700 | 0 | |a Anne Uhlmann |e verfasserin |4 aut | |
| 700 | 0 | |a Ruth J. van Holst |e verfasserin |4 aut | |
| 700 | 0 | |a Dick J. Veltman |e verfasserin |4 aut | |
| 700 | 0 | |a Reinout W. Wiers |e verfasserin |4 aut | |
| 700 | 0 | |a Murat Yücel |e verfasserin |4 aut | |
| 700 | 0 | |a Sheng Zhang |e verfasserin |4 aut | |
| 700 | 0 | |a Patricia Conrod |e verfasserin |4 aut | |
| 700 | 0 | |a Scott Mackey |e verfasserin |4 aut | |
| 700 | 0 | |a Hugh Garavan |e verfasserin |4 aut | |
| 700 | 0 | |a The ENIGMA Addiction Working Group |e verfasserin |4 aut | |
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10.3389/fnimg.2023.1138193 doi (DE-627)DOAJ098994395 (DE-599)DOAJa9e7bfb5c4094b3094c15ed84edecb02 DE-627 ger DE-627 rakwb eng RC346-429 Zhipeng Cao verfasserin aut Recalibrating single-study effect sizes using hierarchical Bayesian models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. effect size recalibration hierarchical Bayesian model case-control differences substance dependence small sample size inflated effect size Neurology. Diseases of the nervous system Zhipeng Cao verfasserin aut Matthew McCabe verfasserin aut Peter Callas verfasserin aut Renata B. Cupertino verfasserin aut Jonatan Ottino-González verfasserin aut Alistair Murphy verfasserin aut Devarshi Pancholi verfasserin aut Nathan Schwab verfasserin aut Orr Catherine verfasserin aut Kent Hutchison verfasserin aut Janna Cousijn verfasserin aut Alain Dagher verfasserin aut John J. Foxe verfasserin aut Anna E. Goudriaan verfasserin aut Robert Hester verfasserin aut Chiang-Shan R. Li verfasserin aut Wesley K. Thompson verfasserin aut Angelica M. Morales verfasserin aut Edythe D. London verfasserin aut Valentina Lorenzetti verfasserin aut Maartje Luijten verfasserin aut Rocio Martin-Santos verfasserin aut Reza Momenan verfasserin aut Martin P. Paulus verfasserin aut Martin P. Paulus verfasserin aut Lianne Schmaal verfasserin aut Lianne Schmaal verfasserin aut Rajita Sinha verfasserin aut Nadia Solowij verfasserin aut Dan J. Stein verfasserin aut Elliot A. Stein verfasserin aut Anne Uhlmann verfasserin aut Ruth J. van Holst verfasserin aut Dick J. Veltman verfasserin aut Reinout W. Wiers verfasserin aut Murat Yücel verfasserin aut Sheng Zhang verfasserin aut Patricia Conrod verfasserin aut Scott Mackey verfasserin aut Hugh Garavan verfasserin aut The ENIGMA Addiction Working Group verfasserin aut In Frontiers in Neuroimaging Frontiers Media S.A., 2022 2(2023) (DE-627)1809400724 (DE-600)3123824-5 28131193 nnns volume:2 year:2023 https://doi.org/10.3389/fnimg.2023.1138193 kostenfrei https://doaj.org/article/a9e7bfb5c4094b3094c15ed84edecb02 kostenfrei https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full kostenfrei https://doaj.org/toc/2813-1193 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2023 |
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10.3389/fnimg.2023.1138193 doi (DE-627)DOAJ098994395 (DE-599)DOAJa9e7bfb5c4094b3094c15ed84edecb02 DE-627 ger DE-627 rakwb eng RC346-429 Zhipeng Cao verfasserin aut Recalibrating single-study effect sizes using hierarchical Bayesian models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. effect size recalibration hierarchical Bayesian model case-control differences substance dependence small sample size inflated effect size Neurology. Diseases of the nervous system Zhipeng Cao verfasserin aut Matthew McCabe verfasserin aut Peter Callas verfasserin aut Renata B. Cupertino verfasserin aut Jonatan Ottino-González verfasserin aut Alistair Murphy verfasserin aut Devarshi Pancholi verfasserin aut Nathan Schwab verfasserin aut Orr Catherine verfasserin aut Kent Hutchison verfasserin aut Janna Cousijn verfasserin aut Alain Dagher verfasserin aut John J. Foxe verfasserin aut Anna E. Goudriaan verfasserin aut Robert Hester verfasserin aut Chiang-Shan R. Li verfasserin aut Wesley K. Thompson verfasserin aut Angelica M. Morales verfasserin aut Edythe D. London verfasserin aut Valentina Lorenzetti verfasserin aut Maartje Luijten verfasserin aut Rocio Martin-Santos verfasserin aut Reza Momenan verfasserin aut Martin P. Paulus verfasserin aut Martin P. Paulus verfasserin aut Lianne Schmaal verfasserin aut Lianne Schmaal verfasserin aut Rajita Sinha verfasserin aut Nadia Solowij verfasserin aut Dan J. Stein verfasserin aut Elliot A. Stein verfasserin aut Anne Uhlmann verfasserin aut Ruth J. van Holst verfasserin aut Dick J. Veltman verfasserin aut Reinout W. Wiers verfasserin aut Murat Yücel verfasserin aut Sheng Zhang verfasserin aut Patricia Conrod verfasserin aut Scott Mackey verfasserin aut Hugh Garavan verfasserin aut The ENIGMA Addiction Working Group verfasserin aut In Frontiers in Neuroimaging Frontiers Media S.A., 2022 2(2023) (DE-627)1809400724 (DE-600)3123824-5 28131193 nnns volume:2 year:2023 https://doi.org/10.3389/fnimg.2023.1138193 kostenfrei https://doaj.org/article/a9e7bfb5c4094b3094c15ed84edecb02 kostenfrei https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full kostenfrei https://doaj.org/toc/2813-1193 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2023 |
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10.3389/fnimg.2023.1138193 doi (DE-627)DOAJ098994395 (DE-599)DOAJa9e7bfb5c4094b3094c15ed84edecb02 DE-627 ger DE-627 rakwb eng RC346-429 Zhipeng Cao verfasserin aut Recalibrating single-study effect sizes using hierarchical Bayesian models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. effect size recalibration hierarchical Bayesian model case-control differences substance dependence small sample size inflated effect size Neurology. Diseases of the nervous system Zhipeng Cao verfasserin aut Matthew McCabe verfasserin aut Peter Callas verfasserin aut Renata B. Cupertino verfasserin aut Jonatan Ottino-González verfasserin aut Alistair Murphy verfasserin aut Devarshi Pancholi verfasserin aut Nathan Schwab verfasserin aut Orr Catherine verfasserin aut Kent Hutchison verfasserin aut Janna Cousijn verfasserin aut Alain Dagher verfasserin aut John J. Foxe verfasserin aut Anna E. Goudriaan verfasserin aut Robert Hester verfasserin aut Chiang-Shan R. Li verfasserin aut Wesley K. Thompson verfasserin aut Angelica M. Morales verfasserin aut Edythe D. London verfasserin aut Valentina Lorenzetti verfasserin aut Maartje Luijten verfasserin aut Rocio Martin-Santos verfasserin aut Reza Momenan verfasserin aut Martin P. Paulus verfasserin aut Martin P. Paulus verfasserin aut Lianne Schmaal verfasserin aut Lianne Schmaal verfasserin aut Rajita Sinha verfasserin aut Nadia Solowij verfasserin aut Dan J. Stein verfasserin aut Elliot A. Stein verfasserin aut Anne Uhlmann verfasserin aut Ruth J. van Holst verfasserin aut Dick J. Veltman verfasserin aut Reinout W. Wiers verfasserin aut Murat Yücel verfasserin aut Sheng Zhang verfasserin aut Patricia Conrod verfasserin aut Scott Mackey verfasserin aut Hugh Garavan verfasserin aut The ENIGMA Addiction Working Group verfasserin aut In Frontiers in Neuroimaging Frontiers Media S.A., 2022 2(2023) (DE-627)1809400724 (DE-600)3123824-5 28131193 nnns volume:2 year:2023 https://doi.org/10.3389/fnimg.2023.1138193 kostenfrei https://doaj.org/article/a9e7bfb5c4094b3094c15ed84edecb02 kostenfrei https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full kostenfrei https://doaj.org/toc/2813-1193 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2023 |
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10.3389/fnimg.2023.1138193 doi (DE-627)DOAJ098994395 (DE-599)DOAJa9e7bfb5c4094b3094c15ed84edecb02 DE-627 ger DE-627 rakwb eng RC346-429 Zhipeng Cao verfasserin aut Recalibrating single-study effect sizes using hierarchical Bayesian models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. effect size recalibration hierarchical Bayesian model case-control differences substance dependence small sample size inflated effect size Neurology. Diseases of the nervous system Zhipeng Cao verfasserin aut Matthew McCabe verfasserin aut Peter Callas verfasserin aut Renata B. Cupertino verfasserin aut Jonatan Ottino-González verfasserin aut Alistair Murphy verfasserin aut Devarshi Pancholi verfasserin aut Nathan Schwab verfasserin aut Orr Catherine verfasserin aut Kent Hutchison verfasserin aut Janna Cousijn verfasserin aut Alain Dagher verfasserin aut John J. Foxe verfasserin aut Anna E. Goudriaan verfasserin aut Robert Hester verfasserin aut Chiang-Shan R. Li verfasserin aut Wesley K. Thompson verfasserin aut Angelica M. Morales verfasserin aut Edythe D. London verfasserin aut Valentina Lorenzetti verfasserin aut Maartje Luijten verfasserin aut Rocio Martin-Santos verfasserin aut Reza Momenan verfasserin aut Martin P. Paulus verfasserin aut Martin P. Paulus verfasserin aut Lianne Schmaal verfasserin aut Lianne Schmaal verfasserin aut Rajita Sinha verfasserin aut Nadia Solowij verfasserin aut Dan J. Stein verfasserin aut Elliot A. Stein verfasserin aut Anne Uhlmann verfasserin aut Ruth J. van Holst verfasserin aut Dick J. Veltman verfasserin aut Reinout W. Wiers verfasserin aut Murat Yücel verfasserin aut Sheng Zhang verfasserin aut Patricia Conrod verfasserin aut Scott Mackey verfasserin aut Hugh Garavan verfasserin aut The ENIGMA Addiction Working Group verfasserin aut In Frontiers in Neuroimaging Frontiers Media S.A., 2022 2(2023) (DE-627)1809400724 (DE-600)3123824-5 28131193 nnns volume:2 year:2023 https://doi.org/10.3389/fnimg.2023.1138193 kostenfrei https://doaj.org/article/a9e7bfb5c4094b3094c15ed84edecb02 kostenfrei https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full kostenfrei https://doaj.org/toc/2813-1193 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2023 |
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10.3389/fnimg.2023.1138193 doi (DE-627)DOAJ098994395 (DE-599)DOAJa9e7bfb5c4094b3094c15ed84edecb02 DE-627 ger DE-627 rakwb eng RC346-429 Zhipeng Cao verfasserin aut Recalibrating single-study effect sizes using hierarchical Bayesian models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. effect size recalibration hierarchical Bayesian model case-control differences substance dependence small sample size inflated effect size Neurology. Diseases of the nervous system Zhipeng Cao verfasserin aut Matthew McCabe verfasserin aut Peter Callas verfasserin aut Renata B. Cupertino verfasserin aut Jonatan Ottino-González verfasserin aut Alistair Murphy verfasserin aut Devarshi Pancholi verfasserin aut Nathan Schwab verfasserin aut Orr Catherine verfasserin aut Kent Hutchison verfasserin aut Janna Cousijn verfasserin aut Alain Dagher verfasserin aut John J. Foxe verfasserin aut Anna E. Goudriaan verfasserin aut Robert Hester verfasserin aut Chiang-Shan R. Li verfasserin aut Wesley K. Thompson verfasserin aut Angelica M. Morales verfasserin aut Edythe D. London verfasserin aut Valentina Lorenzetti verfasserin aut Maartje Luijten verfasserin aut Rocio Martin-Santos verfasserin aut Reza Momenan verfasserin aut Martin P. Paulus verfasserin aut Martin P. Paulus verfasserin aut Lianne Schmaal verfasserin aut Lianne Schmaal verfasserin aut Rajita Sinha verfasserin aut Nadia Solowij verfasserin aut Dan J. Stein verfasserin aut Elliot A. Stein verfasserin aut Anne Uhlmann verfasserin aut Ruth J. van Holst verfasserin aut Dick J. Veltman verfasserin aut Reinout W. Wiers verfasserin aut Murat Yücel verfasserin aut Sheng Zhang verfasserin aut Patricia Conrod verfasserin aut Scott Mackey verfasserin aut Hugh Garavan verfasserin aut The ENIGMA Addiction Working Group verfasserin aut In Frontiers in Neuroimaging Frontiers Media S.A., 2022 2(2023) (DE-627)1809400724 (DE-600)3123824-5 28131193 nnns volume:2 year:2023 https://doi.org/10.3389/fnimg.2023.1138193 kostenfrei https://doaj.org/article/a9e7bfb5c4094b3094c15ed84edecb02 kostenfrei https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full kostenfrei https://doaj.org/toc/2813-1193 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2023 |
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Zhipeng Cao Matthew McCabe Peter Callas Renata B. Cupertino Jonatan Ottino-González Alistair Murphy Devarshi Pancholi Nathan Schwab Orr Catherine Kent Hutchison Janna Cousijn Alain Dagher John J. Foxe Anna E. Goudriaan Robert Hester Chiang-Shan R. Li Wesley K. Thompson Angelica M. Morales Edythe D. London Valentina Lorenzetti Maartje Luijten Rocio Martin-Santos Reza Momenan Martin P. Paulus Lianne Schmaal Rajita Sinha Nadia Solowij Dan J. Stein Elliot A. Stein Anne Uhlmann Ruth J. van Holst Dick J. Veltman Reinout W. Wiers Murat Yücel Sheng Zhang Patricia Conrod Scott Mackey Hugh Garavan The ENIGMA Addiction Working Group |
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recalibrating single-study effect sizes using hierarchical bayesian models |
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Recalibrating single-study effect sizes using hierarchical Bayesian models |
| abstract |
IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. |
| abstractGer |
IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. |
| abstract_unstemmed |
IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples. |
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Recalibrating single-study effect sizes using hierarchical Bayesian models |
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Zhipeng Cao Matthew McCabe Peter Callas Renata B. Cupertino Jonatan Ottino-González Alistair Murphy Devarshi Pancholi Nathan Schwab Orr Catherine Kent Hutchison Janna Cousijn Alain Dagher John J. Foxe Anna E. Goudriaan Robert Hester Chiang-Shan R. Li Wesley K. Thompson Angelica M. Morales Edythe D. London Valentina Lorenzetti Maartje Luijten Rocio Martin-Santos Reza Momenan Martin P. Paulus Lianne Schmaal Rajita Sinha Nadia Solowij Dan J. Stein Elliot A. Stein Anne Uhlmann Ruth J. van Holst Dick J. Veltman Reinout W. Wiers Murat Yücel Sheng Zhang Patricia Conrod Scott Mackey Hugh Garavan The ENIGMA Addiction Working Group |
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| callnumber-a |
RC346-429 |
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2024-07-03T20:25:55.926Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ098994395</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414010438.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240414s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fnimg.2023.1138193</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ098994395</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa9e7bfb5c4094b3094c15ed84edecb02</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zhipeng Cao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Recalibrating single-study effect sizes using hierarchical Bayesian models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p &lt; 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p &lt; 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">effect size recalibration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hierarchical Bayesian model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">case-control differences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">substance dependence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">small sample size</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">inflated effect size</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. 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