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Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model

BACKGROUND: Adjustment for baseline prognostic factors in randomized clinical trials is usually performed by means of sample-based regression models. Sample-based models may be incorrect due to overfitting. To assess whether overfitting is a problem in practice, we used simulated data to examine the...

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Autores principales: Perneger, Thomas, Combescure, Christophe, Poncet, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924183/
https://www.ncbi.nlm.nih.gov/pubmed/36782238
http://dx.doi.org/10.1186/s13063-022-07053-7
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author Perneger, Thomas
Combescure, Christophe
Poncet, Antoine
author_facet Perneger, Thomas
Combescure, Christophe
Poncet, Antoine
author_sort Perneger, Thomas
collection PubMed
description BACKGROUND: Adjustment for baseline prognostic factors in randomized clinical trials is usually performed by means of sample-based regression models. Sample-based models may be incorrect due to overfitting. To assess whether overfitting is a problem in practice, we used simulated data to examine the performance of the sample-based model in comparison to a “true” adjustment model, in terms of estimation of the treatment effect. METHODS: We conducted a simulation study using samples drawn from a “population” in which both the treatment effect and the effect of the potential confounder were specified. The outcome variable was binary. Using logistic regression, we compared three estimates of the treatment effect in each situation: unadjusted, adjusted for the confounder using the sample, adjusted for the confounder using the true effect. Experimental factors were sample size (from 2 × 50 to 2 × 1000), treatment effect (logit of 0, 0.5, or 1.0), confounder type (continuous or binary), and confounder effect (logit of 0, − 0.5, or − 1.0). The assessment criteria for the estimated treatment effect were bias, variance, precision (proportion of estimates within 0.1 logit units), type 1 error, and power. RESULTS: Sample-based adjustment models yielded more biased estimates of the treatment effect than adjustment models that used the true confounder effect but had similar variance, accuracy, power, and type 1 error rates. The simulation also confirmed the conservative bias of unadjusted analyses due to the non-collapsibility of the odds ratio, the smaller variance of unadjusted estimates, and the bias of the odds ratio away from the null hypothesis in small datasets. CONCLUSIONS: Sample-based adjustment yields similar results to exact adjustment in estimating the treatment effect. Sample-based adjustment is preferable to no adjustment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13063-022-07053-7.
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spelling pubmed-99241832023-02-14 Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model Perneger, Thomas Combescure, Christophe Poncet, Antoine Trials Methodology BACKGROUND: Adjustment for baseline prognostic factors in randomized clinical trials is usually performed by means of sample-based regression models. Sample-based models may be incorrect due to overfitting. To assess whether overfitting is a problem in practice, we used simulated data to examine the performance of the sample-based model in comparison to a “true” adjustment model, in terms of estimation of the treatment effect. METHODS: We conducted a simulation study using samples drawn from a “population” in which both the treatment effect and the effect of the potential confounder were specified. The outcome variable was binary. Using logistic regression, we compared three estimates of the treatment effect in each situation: unadjusted, adjusted for the confounder using the sample, adjusted for the confounder using the true effect. Experimental factors were sample size (from 2 × 50 to 2 × 1000), treatment effect (logit of 0, 0.5, or 1.0), confounder type (continuous or binary), and confounder effect (logit of 0, − 0.5, or − 1.0). The assessment criteria for the estimated treatment effect were bias, variance, precision (proportion of estimates within 0.1 logit units), type 1 error, and power. RESULTS: Sample-based adjustment models yielded more biased estimates of the treatment effect than adjustment models that used the true confounder effect but had similar variance, accuracy, power, and type 1 error rates. The simulation also confirmed the conservative bias of unadjusted analyses due to the non-collapsibility of the odds ratio, the smaller variance of unadjusted estimates, and the bias of the odds ratio away from the null hypothesis in small datasets. CONCLUSIONS: Sample-based adjustment yields similar results to exact adjustment in estimating the treatment effect. Sample-based adjustment is preferable to no adjustment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13063-022-07053-7. BioMed Central 2023-02-13 /pmc/articles/PMC9924183/ /pubmed/36782238 http://dx.doi.org/10.1186/s13063-022-07053-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Perneger, Thomas
Combescure, Christophe
Poncet, Antoine
Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
title Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
title_full Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
title_fullStr Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
title_full_unstemmed Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
title_short Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
title_sort adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus true model
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924183/
https://www.ncbi.nlm.nih.gov/pubmed/36782238
http://dx.doi.org/10.1186/s13063-022-07053-7
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