Subgroup analyses have specious appeal. They seem logical and intuitive-and even fun-to both investigators and readers. However, this insidious appeal causes important problems. Multiplicity and naivetΓ© combine to encourage interpretational missteps in trial conduct and reporting. The subgroup treatment effects revealed in many reports might be illusory.
By contrast, investigators cannot avoid interim analyses if data monitoring is indicated. Neither can they use their normal statistical approaches at interim analyses. Statistical stopping methods, essentially statistical adjustments for warning rather than stopping, must be used in support of data monitoring. Unfortunately, those methods baffle investigators and readers alike. Statistics frequently proves confusing anyway without throwing in second-order complications of stopping methods.
Multiplicity issues from subgroup and interim analyses pose similar problems to those from multiple endpoints and treatment groups. 1 Investigators frequently datadredge by doing many subgroup analyses and undertaking repeated interim analyses. Also, researchers conduct unplanned subgroup and interim analyses. Yet, some of the approaches to multiplicity problems from subgroup and interim analyses differ from those for endpoints and treatments.
"All these subgroup analyses should, perhaps, be taken less as evidence about who benefits than as evidence that such analyses are potentially misleading."