New theorems establish that the odds ratio lacks logic-respecting and dilution properties when subgroups are combined, making it inappropriate for subgroup analysis, while the relative response satisfies both.
Risk ratio, odds ratio, risk difference
5 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
fields
stat.ME 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
Non-collapsible marginal effect measures depend on joint distributions of effect modifiers and prognostic variables, so unadjusted anchored indirect comparisons can be biased even without individual-level treatment effect heterogeneity.
Marginal and conditional summary measures generally do not coincide, so their naive pooling in evidence synthesis can produce bias and requires care for transportability across studies.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.
citing papers explorer
-
Subgroup analysis in randomized controlled trials with binary outcomes: dilution and logic-respecting properties
New theorems establish that the odds ratio lacks logic-respecting and dilution properties when subgroups are combined, making it inappropriate for subgroup analysis, while the relative response satisfies both.
-
Transportability of model-based estimands in evidence synthesis
Non-collapsible marginal effect measures depend on joint distributions of effect modifiers and prognostic variables, so unadjusted anchored indirect comparisons can be biased even without individual-level treatment effect heterogeneity.
-
Marginal and conditional summary measures: transportability and compatibility across studies
Marginal and conditional summary measures generally do not coincide, so their naive pooling in evidence synthesis can produce bias and requires care for transportability across studies.
-
Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
-
Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.