A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials

Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage p...
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Autor*in:	da Costa, Bruno R. [verfasserIn] Sutton, Alex J.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Individual patient data Meta-analysis Random-effects Evidence synthesis Interaction effects Subgroup analysis

Anmerkung:	© The Author(s). 2019

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 19(2019), 1 vom: 26. Okt.
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:1 ; day:26 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s12874-019-0831-8

Katalog-ID:	SPR027377725

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520			\|a Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis.
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allfields	10.1186/s12874-019-0831-8 doi (DE-627)SPR027377725 (SPR)s12874-019-0831-8-e DE-627 ger DE-627 rakwb eng da Costa, Bruno R. verfasserin (orcid)0000-0002-1786-6332 aut A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. Individual patient data (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Random-effects (dpeaa)DE-He213 Evidence synthesis (dpeaa)DE-He213 Interaction effects (dpeaa)DE-He213 Subgroup analysis (dpeaa)DE-He213 Sutton, Alex J. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 26. Okt. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:26 month:10 https://dx.doi.org/10.1186/s12874-019-0831-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 26 10
spelling	10.1186/s12874-019-0831-8 doi (DE-627)SPR027377725 (SPR)s12874-019-0831-8-e DE-627 ger DE-627 rakwb eng da Costa, Bruno R. verfasserin (orcid)0000-0002-1786-6332 aut A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. Individual patient data (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Random-effects (dpeaa)DE-He213 Evidence synthesis (dpeaa)DE-He213 Interaction effects (dpeaa)DE-He213 Subgroup analysis (dpeaa)DE-He213 Sutton, Alex J. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 26. Okt. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:26 month:10 https://dx.doi.org/10.1186/s12874-019-0831-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 26 10
allfields_unstemmed	10.1186/s12874-019-0831-8 doi (DE-627)SPR027377725 (SPR)s12874-019-0831-8-e DE-627 ger DE-627 rakwb eng da Costa, Bruno R. verfasserin (orcid)0000-0002-1786-6332 aut A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. Individual patient data (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Random-effects (dpeaa)DE-He213 Evidence synthesis (dpeaa)DE-He213 Interaction effects (dpeaa)DE-He213 Subgroup analysis (dpeaa)DE-He213 Sutton, Alex J. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 26. Okt. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:26 month:10 https://dx.doi.org/10.1186/s12874-019-0831-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 26 10
allfieldsGer	10.1186/s12874-019-0831-8 doi (DE-627)SPR027377725 (SPR)s12874-019-0831-8-e DE-627 ger DE-627 rakwb eng da Costa, Bruno R. verfasserin (orcid)0000-0002-1786-6332 aut A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. Individual patient data (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Random-effects (dpeaa)DE-He213 Evidence synthesis (dpeaa)DE-He213 Interaction effects (dpeaa)DE-He213 Subgroup analysis (dpeaa)DE-He213 Sutton, Alex J. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 26. Okt. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:26 month:10 https://dx.doi.org/10.1186/s12874-019-0831-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 26 10
allfieldsSound	10.1186/s12874-019-0831-8 doi (DE-627)SPR027377725 (SPR)s12874-019-0831-8-e DE-627 ger DE-627 rakwb eng da Costa, Bruno R. verfasserin (orcid)0000-0002-1786-6332 aut A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. Individual patient data (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Random-effects (dpeaa)DE-He213 Evidence synthesis (dpeaa)DE-He213 Interaction effects (dpeaa)DE-He213 Subgroup analysis (dpeaa)DE-He213 Sutton, Alex J. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 26. Okt. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:26 month:10 https://dx.doi.org/10.1186/s12874-019-0831-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 26 10
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Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. 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author	da Costa, Bruno R.
spellingShingle	da Costa, Bruno R. misc Individual patient data misc Meta-analysis misc Random-effects misc Evidence synthesis misc Interaction effects misc Subgroup analysis A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials
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topic_title	A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials Individual patient data (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Random-effects (dpeaa)DE-He213 Evidence synthesis (dpeaa)DE-He213 Interaction effects (dpeaa)DE-He213 Subgroup analysis (dpeaa)DE-He213
topic	misc Individual patient data misc Meta-analysis misc Random-effects misc Evidence synthesis misc Interaction effects misc Subgroup analysis
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title	A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials
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title_full	A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials
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author_browse	da Costa, Bruno R. Sutton, Alex J.
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title_sort	comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials
title_auth	A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials
abstract	Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. © The Author(s). 2019
abstractGer	Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. © The Author(s). 2019
abstract_unstemmed	Background When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. Conclusions The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis. © The Author(s). 2019
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title_short	A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials
url	https://dx.doi.org/10.1186/s12874-019-0831-8
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up_date	2024-07-04T01:36:10.427Z
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Methods Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. Results Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. 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A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials

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