Speaker
James P. Hughes, PhD
Professor Emeritus of Biostatistics, University of Washington
Keywords
Design, Analysis, Stepped-Wedge Trial
Key Points
- Stepped-wedge design is typically run by clusters that are randomized. Each of the clusters receive the control and treatment, and each cluster is sequenced to the intervention at different times.
- There are several key design considerations for stepped-wedge design, including defining the estimand or what are you trying to estimate in the analysis, the key sources of variation, and how outcome data will be collected.
- How will the treatment effect the outcome? Magnitude of effect is a key power consideration. It is important to think about the variation in effect over exposure time. Classic analyses of stepped-wedge trials assume that the effect of the treatment occurs instantaneously once the intervention is applied and that the effect of the intervention is constant over exposure time. But this assumption may not be true in all studies or for all types of treatment.
- A key question is what is the estimand and what is most scientifically meaningful? Classic stepped-wedge analyses assume an instantaneous treatment effect. What happens if you assume the treatment effect is immediate and constant, but it’s not? Researchers need to think very carefully about what is the possible exposure time curve and what is it the study is trying to estimate in the stepped-wedge trial?
- An alternative approach to estimation in stepped-wedge trials compared to the standard immediate treatment approach is to use an exposure time indicator model, wherein researchers estimate a treatment effect for every exposure time. In this case the estimated treatment effect is going to be a weighted average of the exposure times.
- Another issue to think about with stepped-wedge trials is potential sources of variation. For any cluster randomized trial, researchers are used to thinking about the cluster means, or how much variation you expect in the cluster means (the absence of treatment). For stepped-wedge trials there may be potential variation in treatment effect and variation in cluster means over time.
- Key design recommendations for stepped-wedge trials: Don’t assume the intervention is immediate and constant (IT model) unless well-justified; if a transition period is planned, include data from the transition period; if the estimand is the effect at a point in time, maximize the number of observations at that exposure time; including more variance components in the power calculation reduces the possibility of an underpowered trial; and power calculations in SW trials can sometimes seem counterintuitive.
- Key analysis recommendations for stepped-wedge trials: Fit flexible study time effect; avoid fitting IT model unless you are very confident that the intervention effect is immediate and constant; it is better to overfit than underfit random effects; use small sample correction if necessary.
Discussion Themes
-Most people have analyzed stepped-wedge designs with the immediate treatment model. Should they go back and analysis with a more flexible model? We are trying to assemble a large set of datasets to understand how often this issue occurs in practice. At this point it is an open question for how big of a practical problem this is.
-In the stepped-wedge design there are no clusters that are always treated or never treated. Do you have thoughts about adding these to a stepped-wedge design? Part of the issue is that the stepped-wedge concept arose from a need to provide the intervention to all clusters. If you had a cluster that was not treated it would probably improve your power but misses the point of the stepped-wedge design. There are other designs that work better in that instance, such as a parallel design.
Tags
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