How to use a subgroup analysis: users' guide to the medical literature.
Clinicians, when trying to apply trial results to patient care, need to individualize patient care and, potentially, manage patients based on results of subgroup analyses. Apparently compelling subgroup effects often prove spurious, and guidance is needed to differentiate credible from less credible subgroup claims. We therefore provide 5 criteria to use when assessing the validity of subgroup analyses: (1) Can chance explain the apparent subgroup effect; (2) Is the effect consistent across studies; (3) Was the subgroup hypothesis one of a small number of hypotheses developed a priori with direction specified; (4) Is there strong preexisting biological support; and (5) Is the evidence supporting the effect based on within- or between-study comparisons. The first 4 criteria are applicable to individual studies or systematic reviews, the last only to systematic reviews of multiple studies. These criteria will help clinicians deciding whether to use subgroup analyses to guide their patient care.
Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, Chinaj2Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Xin
X
Sun
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California4Stanford Prevention Research Center, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, Cali.
John P A
JP
Ioannidis
Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Thomas
T
Agoritsas
Heart Failure and Transplantation Program, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.
Ana C
AC
Alba
Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Gordon
G
Guyatt
D003627:Data Interpretation, Statistical; D003657:Decision Making; D019317:Evidence-Based Medicine; D017408:Guidelines as Topic; D015201:Meta-Analysis as Topic; D005791:Patient Care; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D012196:Review Literature as Topic