Generalizing the information content for stepped wedge designs: A marginal modeling approach

Fan Li, Jessica Kasza, Elizabeth L. Turner, Paul J. Rathouz, Andrew B. Forbes and John S. Preisser

Scandinavian Journal of Statistics, 2023, vol. 50, issue 3, 1048-1067

Abstract: Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll‐out. While a complete design requires clusters to collect data in all periods, resource and patient‐centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance‐stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric.

Date: 2023
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