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Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group

BACKGROUND: The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, s...

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Autores principales: Rudrapatna, Vivek A., Ravindranath, Vignesh G., Arneson, Douglas V., Mosenia, Arman, Butte, Atul J., Wang, Shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546672/
https://www.ncbi.nlm.nih.gov/pubmed/37789257
http://dx.doi.org/10.1186/s12874-023-02020-5
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author Rudrapatna, Vivek A.
Ravindranath, Vignesh G.
Arneson, Douglas V.
Mosenia, Arman
Butte, Atul J.
Wang, Shan
author_facet Rudrapatna, Vivek A.
Ravindranath, Vignesh G.
Arneson, Douglas V.
Mosenia, Arman
Butte, Atul J.
Wang, Shan
author_sort Rudrapatna, Vivek A.
collection PubMed
description BACKGROUND: The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. METHODS: This work was conducted within the context of a broader effort to study comparative efficacy in Crohn’s disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn’s Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. RESULTS: Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. CONCLUSIONS: This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02020-5.
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spelling pubmed-105466722023-10-04 Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group Rudrapatna, Vivek A. Ravindranath, Vignesh G. Arneson, Douglas V. Mosenia, Arman Butte, Atul J. Wang, Shan BMC Med Res Methodol Research BACKGROUND: The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. METHODS: This work was conducted within the context of a broader effort to study comparative efficacy in Crohn’s disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn’s Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. RESULTS: Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. CONCLUSIONS: This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02020-5. BioMed Central 2023-10-03 /pmc/articles/PMC10546672/ /pubmed/37789257 http://dx.doi.org/10.1186/s12874-023-02020-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Research
Rudrapatna, Vivek A.
Ravindranath, Vignesh G.
Arneson, Douglas V.
Mosenia, Arman
Butte, Atul J.
Wang, Shan
Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
title Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
title_full Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
title_fullStr Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
title_full_unstemmed Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
title_short Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
title_sort sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546672/
https://www.ncbi.nlm.nih.gov/pubmed/37789257
http://dx.doi.org/10.1186/s12874-023-02020-5
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