Home > Publications database > Summarizing EC50 estimates from multiple dose-response experiments: a comparison of a meta-analysis strategy to a mixed-effects model approach. > print |
001 | 126072 | ||
005 | 20240228135023.0 | ||
024 | 7 | _ | |a 10.1002/bimj.201300123 |2 doi |
024 | 7 | _ | |a pmid:24478144 |2 pmid |
024 | 7 | _ | |a 0006-3452 |2 ISSN |
024 | 7 | _ | |a 0323-3847 |2 ISSN |
024 | 7 | _ | |a 1521-4036 |2 ISSN |
024 | 7 | _ | |a altmetric:17932810 |2 altmetric |
037 | _ | _ | |a DKFZ-2017-02187 |
041 | _ | _ | |a eng |
082 | _ | _ | |a 570 |
100 | 1 | _ | |a Jiang, Xiaoqi |0 P:(DE-He78)079436c5442874052d541943e53145ef |b 0 |e First author |u dkfz |
245 | _ | _ | |a Summarizing EC50 estimates from multiple dose-response experiments: a comparison of a meta-analysis strategy to a mixed-effects model approach. |
260 | _ | _ | |a Berlin |c 2014 |b Wiley-VCH |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1505392185_24986 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a Dose-response studies are performed to investigate the potency of a compound. EC50 is the concentration of the compound that gives half-maximal response. Dose-response data are typically evaluated by using a log-logistic model that includes EC50 as one of the model parameters. Often, more than one experiment is carried out to determine the EC50 value for a compound, requiring summarization of EC50 estimates from a series of experiments. In this context, mixed-effects models are designed to estimate the average behavior of EC50 values over all experiments by considering the variabilities within and among experiments simultaneously. However, fitting nonlinear mixed-effects models is more complicated than in a linear situation, and convergence problems are often encountered. An alternative strategy is the application of a meta-analysis approach, which combines EC50 estimates obtained from separate log-logistic model fitting. These two proposed strategies to summarize EC50 estimates from multiple experiments are compared in a simulation study and real data example. We conclude that the meta-analysis strategy is a simple and robust method to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments. |
536 | _ | _ | |a 313 - Cancer risk factors and prevention (POF3-313) |0 G:(DE-HGF)POF3-313 |c POF3-313 |f POF III |x 0 |
588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
650 | _ | 7 | |a Sodium Dodecyl Sulfate |0 368GB5141J |2 NLM Chemicals |
700 | 1 | _ | |a Kopp-Schneider, Annette |0 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |b 1 |e Last author |u dkfz |
773 | _ | _ | |a 10.1002/bimj.201300123 |g Vol. 56, no. 3, p. 493 - 512 |0 PERI:(DE-600)1479920-0 |n 3 |p 493 - 512 |t Biometrical journal |v 56 |y 2014 |x 0323-3847 |
909 | C | O | |o oai:inrepo02.dkfz.de:126072 |p VDB |
910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 0 |6 P:(DE-He78)079436c5442874052d541943e53145ef |
910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 1 |6 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |
913 | 1 | _ | |a DE-HGF |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-313 |2 G:(DE-HGF)POF3-300 |v Cancer risk factors and prevention |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |b Gesundheit |
914 | 1 | _ | |y 2014 |
915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b BIOMETRICAL J : 2015 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Thomson Reuters Master Journal List |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
920 | 1 | _ | |0 I:(DE-He78)C060-20160331 |k C060 |l Biostatistik |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-He78)C060-20160331 |
980 | _ | _ | |a UNRESTRICTED |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|