Cargando…

A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations

With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for su...

Descripción completa

Detalles Bibliográficos
Autores principales: Huber, Cynthia, Benda, Norbert, Friede, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6772173/
https://www.ncbi.nlm.nih.gov/pubmed/31270933
http://dx.doi.org/10.1002/pst.1951
_version_ 1783455853654835200
author Huber, Cynthia
Benda, Norbert
Friede, Tim
author_facet Huber, Cynthia
Benda, Norbert
Friede, Tim
author_sort Huber, Cynthia
collection PubMed
description With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model‐based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre‐specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis.
format Online
Article
Text
id pubmed-6772173
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-67721732019-10-07 A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations Huber, Cynthia Benda, Norbert Friede, Tim Pharm Stat Main Papers With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model‐based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre‐specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis. John Wiley and Sons Inc. 2019-07-03 2019 /pmc/articles/PMC6772173/ /pubmed/31270933 http://dx.doi.org/10.1002/pst.1951 Text en © 2019 The Authors Pharmaceutical Statistics Published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Main Papers
Huber, Cynthia
Benda, Norbert
Friede, Tim
A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
title A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
title_full A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
title_fullStr A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
title_full_unstemmed A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
title_short A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
title_sort comparison of subgroup identification methods in clinical drug development: simulation study and regulatory considerations
topic Main Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6772173/
https://www.ncbi.nlm.nih.gov/pubmed/31270933
http://dx.doi.org/10.1002/pst.1951
work_keys_str_mv AT hubercynthia acomparisonofsubgroupidentificationmethodsinclinicaldrugdevelopmentsimulationstudyandregulatoryconsiderations
AT bendanorbert acomparisonofsubgroupidentificationmethodsinclinicaldrugdevelopmentsimulationstudyandregulatoryconsiderations
AT friedetim acomparisonofsubgroupidentificationmethodsinclinicaldrugdevelopmentsimulationstudyandregulatoryconsiderations
AT hubercynthia comparisonofsubgroupidentificationmethodsinclinicaldrugdevelopmentsimulationstudyandregulatoryconsiderations
AT bendanorbert comparisonofsubgroupidentificationmethodsinclinicaldrugdevelopmentsimulationstudyandregulatoryconsiderations
AT friedetim comparisonofsubgroupidentificationmethodsinclinicaldrugdevelopmentsimulationstudyandregulatoryconsiderations