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Users' Guides to the Medical Literature |

How to Use a Subgroup Analysis:  Users’ Guide to the Medical Literature

Xin Sun, PhD1,2; John P. A. Ioannidis, MD, DSc3,4,5; Thomas Agoritsas, MD2; Ana C. Alba, MD6; Gordon Guyatt, MD, MSc2
[+] Author Affiliations
1Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, Chinaj
2Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
3Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California
4Stanford Prevention Research Center, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California
5Department of Statistics, Stanford University School of Humanities and Sciences, Meta-Research Innovation Center at Stanford (METRICS), Stanford, California
6Heart Failure and Transplantation Program, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
JAMA. 2014;311(4):405-411. doi:10.1001/jama.2013.285063.
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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.

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Figure 1.
Inappropriate Statistical Comparison

The figure presents the results of a hypothetical analysis of subgroups 1 and 2 and their pooled results. Error bars indicate 95% confidence intervals. The size of the data markers (squares) refelects the amount that each group contributes to the pooled estimates.

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Figure 2.
Meta-analysis of Studies Addressing the Effect of Vitamin D on Nonvertebral Fractures

The size of the data markers (squares) reflect the amount that each study contributes to the pooled estimates. This is based on Bischoff-Ferrari et al.30

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