Abstract
Objectives
Subgroup analyses of clinical trial data can be an important tool for understanding
when treatment effects differ across populations. That said, even effect estimates
from prespecified subgroups in well-conducted trials may not apply to corresponding
subgroups in the source population. While this divergence may simply reflect statistical
imprecision, there has been less discussion of systematic or structural sources of
misleading subgroup estimates.
Study Design and Setting
We use directed acyclic graphs to show how selection bias caused by associations between
effect measure modifiers and trial selection, whether explicit (e.g., eligibility
criteria) or implicit (e.g., self-selection based on race), can result in subgroup
estimates that do not correspond to subgroup effects in the source population. To
demonstrate this point, we provide a hypothetical example illustrating the sorts of
erroneous conclusions that can result, as well as their potential consequences. We
also provide a tool for readers to explore additional cases.
Conclusion
Treating subgroups within a trial essentially as random samples of the corresponding
subgroups in the wider population can be misleading, even when analyses are conducted
rigorously and all findings are internally valid. Researchers should carefully examine
associations between (and consider adjusting for) variables when attempting to identify
heterogeneous treatment effects.
Keywords
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Article info
Publication history
Published online: June 18, 2020
Accepted:
June 16,
2020
Footnotes
Funding: MJF was supported by NIH/NHLBI R01HL118255.
Conflict of interest: The authors have no conflict of interest to disclose.
Conflict of interest: No conflict of interest or competing interests to disclose.
Identification
Copyright
© 2020 Elsevier Inc. All rights reserved.
