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Table 10 Subpopulation-specific summary table with example

From: An approach to addressing subpopulation considerations in systematic reviews: the experience of reviewers supporting the U.S. Preventive Services Task Force

Study name (quality rating) Subgroup analysis credibility rating (from phase II) (A) What is the definition of the subgroup in this study? (B) What are the results of the subgroup-specific interaction test? (C) What are the results of subgroup-specific analyses for this subpopulation in this study? (D) What are the results of other subgroup-relevant analyses for this subpopulation in this study?
Study A
(enter quality rating)
Enter subgroup analysis credibility rating from phase II Clearly define the subgroup (or subpopulation) as described in the study (e.g., ages 65 years and older). Abstract results of formal tests for interaction, and indicate the presence or absence of statistical significance (i.e., not significant (NS), significant (S), or not reported (NR)) and all available p values.
Note: The correct analysis is not to test the significance of the intervention effect in one subgroup or another, but whether the effect differs significantly between subgroups [54, 59].
Abstract results of subgroup-specific stratified analyses conducted in the study (e.g., p values and intervention-effect measures of association [odds ratios, relative risks, mean changes] reported by subgroup) [53]. Enter the intervention effect with 95% confidence intervals for the main average and subgroup-specific analyses. Report results of subgroup analyses as absolute and relative risk reductions. Absolute risk reduction estimates give the probability an individual will benefit from an intervention [60].
Note: Estimates can be generated for patients with differing baseline risks that represent types of patients seen in clinical practice by multiplying baseline risk by a pooled homogeneous relative risk estimate [61, 62].
Abstract numerical results and statistical tests of other types of relevant subgroup analyses (e.g., findings with and without subgroup-adjustment in a logistic regression model, multivariable analyses predicting outcomes including subgroup variables).
Women’s Health Study Ridker, 2005 [63] (Good) Moderate Age groups:
45–55 years
55–64 years
≥65 years
Interaction test for outcome of total myocardial infarction (MI): p = 0.03 (S) Relative risk reduction (95% CI) for total MI:
Main average effect: 1.02 (0.84 to 1.25), p = 0.83
45–55 years: 1.23 (0.87 to 1.75), p = 0.25
55–64 years: 1.17 (0.86 to 1.59), p = 0.32
≥65 years: 0.66 (0.44 to 0.97), p = 0.04
Absolute risk reduction (95% CI) for total MI (calculated):
Main average effect: 0.000 (−0.002 to 0.002)
45–55 years: −0.001 (−0.003 to 0.001)
55–64 years: −0.002 (−0.006 to 0.002)
≥65 years: 0.010 (0.001 to 0.020)
p = 0.04