The search for stable prognostic models in multiple imputed data sets

Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed u...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Vergouw, David [verfasserIn] Heymans, Martijn W Peat, George M Kuijpers, Ton Croft, Peter R de Vet, Henrica CW van der Horst, Henriëtte E van der Windt, Daniëlle AWM

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Multiple Imputation Shoulder Pain Model Stability Complete Case Analysis Model Composition

Anmerkung:	© Vergouw et al; licensee BioMed Central Ltd. 2010

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 10(2010), 1 vom: 17. Sept.
Übergeordnetes Werk:	volume:10 ; year:2010 ; number:1 ; day:17 ; month:09

Links:	Volltext

DOI / URN:	10.1186/1471-2288-10-81

Katalog-ID:	SPR027362310

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allfields	10.1186/1471-2288-10-81 doi (DE-627)SPR027362310 (SPR)1471-2288-10-81-e DE-627 ger DE-627 rakwb eng Vergouw, David verfasserin aut The search for stable prognostic models in multiple imputed data sets 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vergouw et al; licensee BioMed Central Ltd. 2010 Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. Multiple Imputation (dpeaa)DE-He213 Shoulder Pain (dpeaa)DE-He213 Model Stability (dpeaa)DE-He213 Complete Case Analysis (dpeaa)DE-He213 Model Composition (dpeaa)DE-He213 Heymans, Martijn W aut Peat, George M aut Kuijpers, Ton aut Croft, Peter R aut de Vet, Henrica CW aut van der Horst, Henriëtte E aut van der Windt, Daniëlle AWM aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 10(2010), 1 vom: 17. Sept. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:10 year:2010 number:1 day:17 month:09 https://dx.doi.org/10.1186/1471-2288-10-81 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 10 2010 1 17 09
spelling	10.1186/1471-2288-10-81 doi (DE-627)SPR027362310 (SPR)1471-2288-10-81-e DE-627 ger DE-627 rakwb eng Vergouw, David verfasserin aut The search for stable prognostic models in multiple imputed data sets 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vergouw et al; licensee BioMed Central Ltd. 2010 Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. Multiple Imputation (dpeaa)DE-He213 Shoulder Pain (dpeaa)DE-He213 Model Stability (dpeaa)DE-He213 Complete Case Analysis (dpeaa)DE-He213 Model Composition (dpeaa)DE-He213 Heymans, Martijn W aut Peat, George M aut Kuijpers, Ton aut Croft, Peter R aut de Vet, Henrica CW aut van der Horst, Henriëtte E aut van der Windt, Daniëlle AWM aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 10(2010), 1 vom: 17. Sept. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:10 year:2010 number:1 day:17 month:09 https://dx.doi.org/10.1186/1471-2288-10-81 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 10 2010 1 17 09
allfields_unstemmed	10.1186/1471-2288-10-81 doi (DE-627)SPR027362310 (SPR)1471-2288-10-81-e DE-627 ger DE-627 rakwb eng Vergouw, David verfasserin aut The search for stable prognostic models in multiple imputed data sets 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vergouw et al; licensee BioMed Central Ltd. 2010 Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. Multiple Imputation (dpeaa)DE-He213 Shoulder Pain (dpeaa)DE-He213 Model Stability (dpeaa)DE-He213 Complete Case Analysis (dpeaa)DE-He213 Model Composition (dpeaa)DE-He213 Heymans, Martijn W aut Peat, George M aut Kuijpers, Ton aut Croft, Peter R aut de Vet, Henrica CW aut van der Horst, Henriëtte E aut van der Windt, Daniëlle AWM aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 10(2010), 1 vom: 17. Sept. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:10 year:2010 number:1 day:17 month:09 https://dx.doi.org/10.1186/1471-2288-10-81 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 10 2010 1 17 09
allfieldsGer	10.1186/1471-2288-10-81 doi (DE-627)SPR027362310 (SPR)1471-2288-10-81-e DE-627 ger DE-627 rakwb eng Vergouw, David verfasserin aut The search for stable prognostic models in multiple imputed data sets 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vergouw et al; licensee BioMed Central Ltd. 2010 Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. Multiple Imputation (dpeaa)DE-He213 Shoulder Pain (dpeaa)DE-He213 Model Stability (dpeaa)DE-He213 Complete Case Analysis (dpeaa)DE-He213 Model Composition (dpeaa)DE-He213 Heymans, Martijn W aut Peat, George M aut Kuijpers, Ton aut Croft, Peter R aut de Vet, Henrica CW aut van der Horst, Henriëtte E aut van der Windt, Daniëlle AWM aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 10(2010), 1 vom: 17. Sept. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:10 year:2010 number:1 day:17 month:09 https://dx.doi.org/10.1186/1471-2288-10-81 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 10 2010 1 17 09
allfieldsSound	10.1186/1471-2288-10-81 doi (DE-627)SPR027362310 (SPR)1471-2288-10-81-e DE-627 ger DE-627 rakwb eng Vergouw, David verfasserin aut The search for stable prognostic models in multiple imputed data sets 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vergouw et al; licensee BioMed Central Ltd. 2010 Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. Multiple Imputation (dpeaa)DE-He213 Shoulder Pain (dpeaa)DE-He213 Model Stability (dpeaa)DE-He213 Complete Case Analysis (dpeaa)DE-He213 Model Composition (dpeaa)DE-He213 Heymans, Martijn W aut Peat, George M aut Kuijpers, Ton aut Croft, Peter R aut de Vet, Henrica CW aut van der Horst, Henriëtte E aut van der Windt, Daniëlle AWM aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 10(2010), 1 vom: 17. Sept. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:10 year:2010 number:1 day:17 month:09 https://dx.doi.org/10.1186/1471-2288-10-81 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 10 2010 1 17 09
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author	Vergouw, David
spellingShingle	Vergouw, David misc Multiple Imputation misc Shoulder Pain misc Model Stability misc Complete Case Analysis misc Model Composition The search for stable prognostic models in multiple imputed data sets
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topic_title	The search for stable prognostic models in multiple imputed data sets Multiple Imputation (dpeaa)DE-He213 Shoulder Pain (dpeaa)DE-He213 Model Stability (dpeaa)DE-He213 Complete Case Analysis (dpeaa)DE-He213 Model Composition (dpeaa)DE-He213
topic	misc Multiple Imputation misc Shoulder Pain misc Model Stability misc Complete Case Analysis misc Model Composition
topic_unstemmed	misc Multiple Imputation misc Shoulder Pain misc Model Stability misc Complete Case Analysis misc Model Composition
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title	The search for stable prognostic models in multiple imputed data sets
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title_full	The search for stable prognostic models in multiple imputed data sets
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author_browse	Vergouw, David Heymans, Martijn W Peat, George M Kuijpers, Ton Croft, Peter R de Vet, Henrica CW van der Horst, Henriëtte E van der Windt, Daniëlle AWM
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doi_str_mv	10.1186/1471-2288-10-81
title_sort	search for stable prognostic models in multiple imputed data sets
title_auth	The search for stable prognostic models in multiple imputed data sets
abstract	Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. © Vergouw et al; licensee BioMed Central Ltd. 2010
abstractGer	Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. © Vergouw et al; licensee BioMed Central Ltd. 2010
abstract_unstemmed	Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability. © Vergouw et al; licensee BioMed Central Ltd. 2010
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title_short	The search for stable prognostic models in multiple imputed data sets
url	https://dx.doi.org/10.1186/1471-2288-10-81
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author2	Heymans, Martijn W Peat, George M Kuijpers, Ton Croft, Peter R de Vet, Henrica CW van der Horst, Henriëtte E van der Windt, Daniëlle AWM
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up_date	2024-07-04T01:31:59.935Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR027362310</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519222122.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2010 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/1471-2288-10-81</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR027362310</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)1471-2288-10-81-e</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="100" ind1="1" ind2=" "><subfield code="a">Vergouw, David</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The search for stable prognostic models in multiple imputed data sets</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</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="500" ind1=" " ind2=" "><subfield code="a">© Vergouw et al; licensee BioMed Central Ltd. 2010</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. 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