Adequacy radii for stepped-wedge trials with time-varying effects

Complete stepped-wedge designs guarantee eventual intervention delivery, but their final periods contain no concurrent controls. Under exposure-time heterogeneity this creates a design-analysis tension: a constant-effect analysis can be precise while targeting a design-dependent contrast, whereas an exposure-time indicator (ETI) analysis targets a prespecified time-averaged effect but may be much less precise. We develop an adequacy radius that quantifies the size of exposure-time heterogeneity compatible with a prespecified tolerance for estimand mismatch, and combine it with precision calculations to compare complete wedges with rollout-constrained hybrid designs that retain a wait-list arm. For a balanced wedge with a wait-list under the Hussey-Hughes model, the wait-list share and the correlation act on the constant-effect GLS estimand through a single scalar; we prove that the radius increases strictly with the wait-list share and decreases strictly with the within-cluster correlation, but remains bounded. Numerical optimisation and a model-based redesign of a weekend allied-health trial show that intermediate hybrids make estimand-aligned ETI analysis materially more affordable, giving a robustness-precision frontier rather than a binary choice between a complete wedge and a parallel trial. A second trial, of digital adherence support for tuberculosis treatment, has the same radius but no detectable heterogeneity; its test of a constant effect would detect heterogeneity large enough to halve the coverage of the nominal interval only 37% of the time. The contribution is a diagnostic and design principle: when exposure-time heterogeneity is plausible, retaining concurrent controls reduces the price of analysing the estimand one intends to report.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-26
DOI
https://doi.org/10.48550/arxiv.2610.35853
Primary Topic
Statistical Methods in Clinical Trials
Type
preprint
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preprint

Adequacy radii for stepped-wedge trials with time-varying effects

Sergey Alexeev, Stanisław Marek Halkiewicz
arXiv (Cornell University)
Statistical Methods in Clinical Trials
preprint

Adequacy radii for stepped-wedge trials with time-varying effects

Sergey Alexeev, Stanisław Marek Halkiewicz
preprint en

Abstract

Complete stepped-wedge designs guarantee eventual intervention delivery, but their final periods contain no concurrent controls. Under exposure-time heterogeneity this creates a design-analysis tension: a constant-effect analysis can be precise while targeting a design-dependent contrast, whereas an exposure-time indicator (ETI) analysis targets a prespecified time-averaged effect but may be much less precise. We develop an adequacy radius that quantifies the size of exposure-time heterogeneity compatible with a prespecified tolerance for estimand mismatch, and combine it with precision calculations to compare complete wedges with rollout-constrained hybrid designs that retain a wait-list arm. For a balanced wedge with a wait-list under the Hussey-Hughes model, the wait-list share and the correlation act on the constant-effect GLS estimand through a single scalar; we prove that the radius increases strictly with the wait-list share and decreases strictly with the within-cluster correlation, but remains bounded. Numerical optimisation and a model-based redesign of a weekend allied-health trial show that intermediate hybrids make estimand-aligned ETI analysis materially more affordable, giving a robustness-precision frontier rather than a binary choice between a complete wedge and a parallel trial. A second trial, of digital adherence support for tuberculosis treatment, has the same radius but no detectable heterogeneity; its test of a constant effect would detect heterogeneity large enough to halve the coverage of the nominal interval only 37% of the time. The contribution is a diagnostic and design principle: when exposure-time heterogeneity is plausible, retaining concurrent controls reduces the price of analysing the estimand one intends to report.

arXiv (Cornell University)
Statistical Methods in Clinical Trials
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