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http://hdl.handle.net/1942/35986
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DC Field | Value | Language |
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dc.contributor.author | THAS, Olivier | - |
dc.contributor.author | Tourny, Annelies | - |
dc.contributor.author | Verbist, Bie | - |
dc.contributor.author | Hawinkel, Stijn | - |
dc.contributor.author | Nazarov, Maxim | - |
dc.contributor.author | Mutambanengwe, Kathy | - |
dc.contributor.author | BIJNENS, Luc | - |
dc.date.accessioned | 2021-11-30T16:56:27Z | - |
dc.date.available | 2021-11-30T16:56:27Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2021-10-28T11:13:59Z | - |
dc.identifier.citation | PHARMACEUTICAL STATISTICS, 21(2), p. 345-360 | - |
dc.identifier.issn | 1539-1604 | - |
dc.identifier.uri | http://hdl.handle.net/1942/35986 | - |
dc.description.abstract | Combination therapies are increasingly adopted as the standard of care for various diseases to improve treatment response, minimise the development of resistance and/or minimise adverse events. Therefore, synergistic combinations are screened early in the drug discovery process, in which their potential is evaluated by comparing the observed combination effect to that expected under a null model. Such methodology is implemented in the BIGL R-package which allows for a quick screening of drug combinations. We extend the meanR and maxR tests from this package by allowing non-constant variance of the responses and by extending the list of null models (Loewe, Loewe2, HSA, Bliss). These new tests are evaluated in a comprehensive simulation study under various models for additivity and synergy, various monotherapeutic dose-response models (complete, partial and incomplete responders) and various types of deviation from the constant variance assumption. In addition, the BIGL package is extended with bootstrap confidence intervals for the individual off-axis points and for the overall synergy strength, which were demonstrated to have reliable coverage and can complement the existing tests. We conclude that the differences in performance between the different null models are small and depend on the simulation scenario. As a result, the choice of null model should be driven by expert knowledge on the particular problem. Finally, we demonstrate the new features of the BIGL package and the difference between the synergy models on a real dataset from drug discovery. The BIGL package is available at CRAN () and as a Shiny app (). | - |
dc.description.sponsorship | Janssen Pharmaceutical Companies of Johnson and Johnson | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.rights | 2021 John Wiley & Sons Ltd | - |
dc.subject.other | simulation study | - |
dc.subject.other | statistical tests | - |
dc.subject.other | synergy | - |
dc.title | Statistical detection of synergy: New methods and a comparative study | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 360 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 345 | - |
dc.identifier.volume | 21 | - |
local.format.pages | 16 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Hawinkel, S (corresponding author), Univ Ghent, Dept Data Anal & Math Modelling, Coupure Links 653, B-9000 Ghent, Belgium. | - |
dc.description.notes | stijn.hawinkel@psb.vib-ugent.be | - |
local.publisher.place | 111 RIVER ST, HOBOKEN 07030-5774, NJ USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/pst.2173 | - |
dc.identifier.pmid | 34608741 | - |
dc.identifier.isi | WOS:000703232600001 | - |
dc.identifier.eissn | 1539-1612 | - |
local.provider.type | wosris | - |
local.uhasselt.uhpub | yes | - |
local.description.affiliation | [Thas, Olivier; Bijnens, Luc] Hasselt Univ, Data Sci Inst, I Biostat, Hasselt, Belgium. | - |
local.description.affiliation | [Thas, Olivier] Univ Ghent, Dept Appl Math Comp Sci & Stat, Ghent, Belgium. | - |
local.description.affiliation | [Thas, Olivier] Univ Wollongong, Natl Inst Appl Stat Res Australia NIASRA, Wollongong, NSW, Australia. | - |
local.description.affiliation | [Tourny, Annelies; Hawinkel, Stijn] Univ Ghent, Dept Data Anal & Math Modelling, Coupure Links 653, B-9000 Ghent, Belgium. | - |
local.description.affiliation | [Verbist, Bie; Bijnens, Luc] Johnson & Johnson, Quantitat Sci, Janssen Pharmaceut Co, Beerse, Belgium. | - |
local.description.affiliation | [Nazarov, Maxim; Mutambanengwe, Kathy] Open Analyt, Antwerp, Belgium. | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2022 | - |
item.contributor | THAS, Olivier | - |
item.contributor | Tourny, Annelies | - |
item.contributor | Verbist, Bie | - |
item.contributor | Hawinkel, Stijn | - |
item.contributor | Nazarov, Maxim | - |
item.contributor | Mutambanengwe, Kathy | - |
item.contributor | BIJNENS, Luc | - |
item.accessRights | Restricted Access | - |
item.fullcitation | THAS, Olivier; Tourny, Annelies; Verbist, Bie; Hawinkel, Stijn; Nazarov, Maxim; Mutambanengwe, Kathy & BIJNENS, Luc (2022) Statistical detection of synergy: New methods and a comparative study. In: PHARMACEUTICAL STATISTICS, 21(2), p. 345-360. | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 1539-1604 | - |
crisitem.journal.eissn | 1539-1612 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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Pharmaceutical Statistics - 2021 - Thas - Statistical detection of synergy New methods and a comparative study.pdf Restricted Access | Published version | 2.05 MB | Adobe PDF | View/Open Request a copy |
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