Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic

Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust stati...
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Autor*in:	Leyrat, Clémence [verfasserIn] Caille, Agnès Foucher, Yohann Giraudeau, Bruno

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Cluster randomized trial Confounding bias Propensity score Baseline imbalance

Anmerkung:	© Leyrat et al. 2015

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 16(2016), 1 vom: 22. Jan.
Übergeordnetes Werk:	volume:16 ; year:2016 ; number:1 ; day:22 ; month:01

Links:	Volltext

DOI / URN:	10.1186/s12874-015-0100-4

Katalog-ID:	SPR027370399

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520			\|a Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.
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allfields	10.1186/s12874-015-0100-4 doi (DE-627)SPR027370399 (SPR)s12874-015-0100-4-e DE-627 ger DE-627 rakwb eng Leyrat, Clémence verfasserin aut Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leyrat et al. 2015 Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. Cluster randomized trial (dpeaa)DE-He213 Confounding bias (dpeaa)DE-He213 Propensity score (dpeaa)DE-He213 Baseline imbalance (dpeaa)DE-He213 Caille, Agnès aut Foucher, Yohann aut Giraudeau, Bruno aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 22. Jan. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:22 month:01 https://dx.doi.org/10.1186/s12874-015-0100-4 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 16 2016 1 22 01
spelling	10.1186/s12874-015-0100-4 doi (DE-627)SPR027370399 (SPR)s12874-015-0100-4-e DE-627 ger DE-627 rakwb eng Leyrat, Clémence verfasserin aut Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leyrat et al. 2015 Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. Cluster randomized trial (dpeaa)DE-He213 Confounding bias (dpeaa)DE-He213 Propensity score (dpeaa)DE-He213 Baseline imbalance (dpeaa)DE-He213 Caille, Agnès aut Foucher, Yohann aut Giraudeau, Bruno aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 22. Jan. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:22 month:01 https://dx.doi.org/10.1186/s12874-015-0100-4 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 16 2016 1 22 01
allfields_unstemmed	10.1186/s12874-015-0100-4 doi (DE-627)SPR027370399 (SPR)s12874-015-0100-4-e DE-627 ger DE-627 rakwb eng Leyrat, Clémence verfasserin aut Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leyrat et al. 2015 Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. Cluster randomized trial (dpeaa)DE-He213 Confounding bias (dpeaa)DE-He213 Propensity score (dpeaa)DE-He213 Baseline imbalance (dpeaa)DE-He213 Caille, Agnès aut Foucher, Yohann aut Giraudeau, Bruno aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 22. Jan. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:22 month:01 https://dx.doi.org/10.1186/s12874-015-0100-4 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 16 2016 1 22 01
allfieldsGer	10.1186/s12874-015-0100-4 doi (DE-627)SPR027370399 (SPR)s12874-015-0100-4-e DE-627 ger DE-627 rakwb eng Leyrat, Clémence verfasserin aut Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leyrat et al. 2015 Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. Cluster randomized trial (dpeaa)DE-He213 Confounding bias (dpeaa)DE-He213 Propensity score (dpeaa)DE-He213 Baseline imbalance (dpeaa)DE-He213 Caille, Agnès aut Foucher, Yohann aut Giraudeau, Bruno aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 22. Jan. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:22 month:01 https://dx.doi.org/10.1186/s12874-015-0100-4 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 16 2016 1 22 01
allfieldsSound	10.1186/s12874-015-0100-4 doi (DE-627)SPR027370399 (SPR)s12874-015-0100-4-e DE-627 ger DE-627 rakwb eng Leyrat, Clémence verfasserin aut Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leyrat et al. 2015 Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. Cluster randomized trial (dpeaa)DE-He213 Confounding bias (dpeaa)DE-He213 Propensity score (dpeaa)DE-He213 Baseline imbalance (dpeaa)DE-He213 Caille, Agnès aut Foucher, Yohann aut Giraudeau, Bruno aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 22. Jan. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:22 month:01 https://dx.doi.org/10.1186/s12874-015-0100-4 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 16 2016 1 22 01
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source	Enthalten in BMC medical research methodology 16(2016), 1 vom: 22. Jan. volume:16 year:2016 number:1 day:22 month:01
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author	Leyrat, Clémence
spellingShingle	Leyrat, Clémence misc Cluster randomized trial misc Confounding bias misc Propensity score misc Baseline imbalance Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic
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topic_title	Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic Cluster randomized trial (dpeaa)DE-He213 Confounding bias (dpeaa)DE-He213 Propensity score (dpeaa)DE-He213 Baseline imbalance (dpeaa)DE-He213
topic	misc Cluster randomized trial misc Confounding bias misc Propensity score misc Baseline imbalance
topic_unstemmed	misc Cluster randomized trial misc Confounding bias misc Propensity score misc Baseline imbalance
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title	Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic
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title_full	Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic
author_sort	Leyrat, Clémence
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author_browse	Leyrat, Clémence Caille, Agnès Foucher, Yohann Giraudeau, Bruno
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author-letter	Leyrat, Clémence
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title_sort	propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic
title_auth	Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic
abstract	Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. © Leyrat et al. 2015
abstractGer	Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. © Leyrat et al. 2015
abstract_unstemmed	Background Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs. © Leyrat et al. 2015
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title_short	Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic
url	https://dx.doi.org/10.1186/s12874-015-0100-4
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author2	Caille, Agnès Foucher, Yohann Giraudeau, Bruno
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up_date	2024-07-04T01:34:10.201Z
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Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. Methods We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. Results The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40 % of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. 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Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic

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