On model-based time trend adjustments in platform trials with non-concurrent controls

Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using...

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Autores: Bofill Roig, Marta|||0000-0002-4400-7541, Krotka, Pavla, Burman, Carl-Fredrik, Glimm, Ekkehard, Gold, Stefan M., Hees, Katharina, Jacko, Peter, Koenig, Frank, Magirr, Dominic, Mesenbrink, Peter, Viele, Kert, Posch, Martin
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/416512
Acceso en línea:https://hdl.handle.net/2117/416512
https://dx.doi.org/10.1186/s12874-022-01683-w
Access Level:acceso abierto
Palabra clave:Adding arms
Non-concurrent controls
Platform trials
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
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spelling On model-based time trend adjustments in platform trials with non-concurrent controlsBofill Roig, Marta|||0000-0002-4400-7541Krotka, PavlaBurman, Carl-FredrikGlimm, EkkehardGold, Stefan M.Hees, KatharinaJacko, PeterKoenig, FrankMagirr, DominicMesenbrink, PeterViele, KertPosch, MartinAdding armsNon-concurrent controlsPlatform trialsÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitàriaPlatform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.</jats:p> </jats:sec>Peer ReviewedSpringer20222022-08-1520242024-10-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/416512https://dx.doi.org/10.1186/s12874-022-01683-wreponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4165122026-05-27T15:37:01Z
dc.title.none.fl_str_mv On model-based time trend adjustments in platform trials with non-concurrent controls
title On model-based time trend adjustments in platform trials with non-concurrent controls
spellingShingle On model-based time trend adjustments in platform trials with non-concurrent controls
Bofill Roig, Marta|||0000-0002-4400-7541
Adding arms
Non-concurrent controls
Platform trials
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
title_short On model-based time trend adjustments in platform trials with non-concurrent controls
title_full On model-based time trend adjustments in platform trials with non-concurrent controls
title_fullStr On model-based time trend adjustments in platform trials with non-concurrent controls
title_full_unstemmed On model-based time trend adjustments in platform trials with non-concurrent controls
title_sort On model-based time trend adjustments in platform trials with non-concurrent controls
dc.creator.none.fl_str_mv Bofill Roig, Marta|||0000-0002-4400-7541
Krotka, Pavla
Burman, Carl-Fredrik
Glimm, Ekkehard
Gold, Stefan M.
Hees, Katharina
Jacko, Peter
Koenig, Frank
Magirr, Dominic
Mesenbrink, Peter
Viele, Kert
Posch, Martin
author Bofill Roig, Marta|||0000-0002-4400-7541
author_facet Bofill Roig, Marta|||0000-0002-4400-7541
Krotka, Pavla
Burman, Carl-Fredrik
Glimm, Ekkehard
Gold, Stefan M.
Hees, Katharina
Jacko, Peter
Koenig, Frank
Magirr, Dominic
Mesenbrink, Peter
Viele, Kert
Posch, Martin
author_role author
author2 Krotka, Pavla
Burman, Carl-Fredrik
Glimm, Ekkehard
Gold, Stefan M.
Hees, Katharina
Jacko, Peter
Koenig, Frank
Magirr, Dominic
Mesenbrink, Peter
Viele, Kert
Posch, Martin
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Adding arms
Non-concurrent controls
Platform trials
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
topic Adding arms
Non-concurrent controls
Platform trials
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
description Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.</jats:p> </jats:sec>
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-08-15
2024
2024-10-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/416512
https://dx.doi.org/10.1186/s12874-022-01683-w
url https://hdl.handle.net/2117/416512
https://dx.doi.org/10.1186/s12874-022-01683-w
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
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