Subgroup analysis: Difference between revisions
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'''Subgroup analysis''', in the context of [[Design of experiments|design]] and analysis of experiments, refers to looking for pattern in a subset of the subjects<ref name="lagakos06">{{ cite journal |url=http://content.nejm.org/cgi/content/extract/354/16/1667 |author=Lagakos SW |journal=[[NEJM]] |title=The challenge of subgroup analyses--reporting without distorting |year = 2006 |month = April 20 |volume = 354 |issue = 16 |pages = 1667-9 |id = PMID 16625007 }}</ref>. | '''Subgroup analysis''', in the context of [[Design of experiments|design]] and analysis of experiments, refers to looking for pattern in a subset of the subjects<ref name="lagakos06">{{ cite journal |url=http://content.nejm.org/cgi/content/extract/354/16/1667 |author=Lagakos SW |journal=[[NEJM]] |title=The challenge of subgroup analyses--reporting without distorting |year = 2006 |month = April 20 |volume = 354 |issue = 16 |pages = 1667-9 |id = PMID 16625007 }}</ref>. | ||
Proposed best practice is to use adjust subgroup comparisons with multivariable risk prediction<ref name="pmid16613605">{{cite journal| author=Hayward RA, Kent DM, Vijan S, Hofer TP| title=Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. | journal=BMC Med Res Methodol | year= 2006 | volume= 6 | issue= | pages= 18 | pmid=16613605 | doi=10.1186/1471-2288-6-18 | pmc=1523355 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=16613605 }} </ref>; however, this is not commonly done<ref name="pmid27423688">{{cite journal| author=Gabler NB, Duan N, Raneses E, Suttner L, Ciarametaro M, Cooney E | display-authors=etal| title=No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals. | journal=Trials | year= 2016 | volume= 17 | issue= 1 | pages= 320 | pmid=27423688 | doi=10.1186/s13063-016-1447-5 | pmc=4947338 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=27423688 }} </ref>. | Proposed best practice is to use adjust by any stratification variables from randomization<ref name="pmid20332511">{{cite journal| author=Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ | display-authors=etal| title=CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. | journal=BMJ | year= 2010 | volume= 340 | issue= | pages= c869 | pmid=20332511 | doi=10.1136/bmj.c869 | pmc=2844943 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=20332511 }} </ref>, or to adjust subgroup comparisons with multivariable risk prediction<ref name="pmid16613605">{{cite journal| author=Hayward RA, Kent DM, Vijan S, Hofer TP| title=Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. | journal=BMC Med Res Methodol | year= 2006 | volume= 6 | issue= | pages= 18 | pmid=16613605 | doi=10.1186/1471-2288-6-18 | pmc=1523355 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=16613605 }} </ref>; however, this is not commonly done<ref name="pmid27423688">{{cite journal| author=Gabler NB, Duan N, Raneses E, Suttner L, Ciarametaro M, Cooney E | display-authors=etal| title=No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals. | journal=Trials | year= 2016 | volume= 17 | issue= 1 | pages= 320 | pmid=27423688 | doi=10.1186/s13063-016-1447-5 | pmc=4947338 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=27423688 }} </ref>. | ||
Selectively missing data may affect subroup analyses. | |||
Slide set: [[File:Subgroup and Interaction Analysis.pdf]] | Slide set: [[File:Subgroup and Interaction Analysis.pdf]] | ||
Latest revision as of 18:51, 20 May 2020
Subgroup analysis, in the context of design and analysis of experiments, refers to looking for pattern in a subset of the subjects[1].
Proposed best practice is to use adjust by any stratification variables from randomization[2], or to adjust subgroup comparisons with multivariable risk prediction[3]; however, this is not commonly done[4].
Selectively missing data may affect subroup analyses.
Slide set: File:Subgroup and Interaction Analysis.pdf
See also
References
- ↑ Lagakos SW (2006). "The challenge of subgroup analyses--reporting without distorting". NEJM. 354 (16): 1667–9. PMID 16625007. Unknown parameter
|month=ignored (help) - ↑ Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ; et al. (2010). "CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials". BMJ. 340: c869. doi:10.1136/bmj.c869. PMC 2844943. PMID 20332511.
- ↑ Hayward RA, Kent DM, Vijan S, Hofer TP (2006). "Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis". BMC Med Res Methodol. 6: 18. doi:10.1186/1471-2288-6-18. PMC 1523355. PMID 16613605.
- ↑ Gabler NB, Duan N, Raneses E, Suttner L, Ciarametaro M, Cooney E; et al. (2016). "No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals". Trials. 17 (1): 320. doi:10.1186/s13063-016-1447-5. PMC 4947338. PMID 27423688.