How subgroup analyses can miss the trees for the forest plots: A simulation study.

Area of Science:

• Biostatistics
• Clinical Trials
• Epidemiology

Background:

• Subgroup analyses are crucial for understanding treatment effect variations across populations.
• However, subgroup estimates from trials may not accurately reflect effects in the broader source population.
• Systematic or structural sources of misleading subgroup estimates are often overlooked.

Purpose of the Study:

• To illustrate how selection bias can distort subgroup estimates in clinical trials.
• To demonstrate the potential for erroneous conclusions and their consequences.
• To provide a tool for exploring cases of misleading subgroup estimates.

Main Methods:

• Utilizing directed acyclic graphs (DAGs) to model selection bias.
• Examining associations between effect measure modifiers and trial selection processes (explicit and implicit).
• Developing a hypothetical example to showcase potential misinterpretations.

Main Results:

• Selection bias, stemming from associations between effect modifiers and trial selection, can create subgroup estimates that diverge from source population effects.
• Explicit criteria (e.g., eligibility) and implicit factors (e.g., self-selection by race) can both introduce bias.
• The study highlights the risk of drawing incorrect conclusions about treatment effectiveness in specific subgroups.

Conclusions:

• Treating clinical trial subgroups as direct samples of the source population can be misleading.
• Researchers need to scrutinize associations between variables when investigating heterogeneous treatment effects.
• Careful examination and potential adjustment for confounding variables are essential for accurate subgroup effect estimation.