1. Alan Girling University of Birmingham, UK A.J.Girling@bham.ac.uk
Optimality in stepped wedge cluster trials: When and Where to take measurements
Introduction
Alan Girling
University of Birmingham, UK
Funding support (AG) from the NIHR through:
The NIHR Collaborations for Leadership in Applied Health Research and Care for West Midlands (CLAHRC WM).
The HiSLAC study (NIHR ref 12/128/07)
SCT/ICTMC Liverpool, May 2017

2. Standard Stepped Wedge Layout
Clusters 
Time  1
0: Control (untreated) Cell
1: Treated Cell
Aim: maximise the precision of the treatment effect estimate by changing the design in some way

3. Essential Constraints
Allow for Time Effects
Otherwise get a “Before & After” Design
Treatment implementation cannot be reversed
If it can a Cross-Over Design is best

4. 1. Optimising the layout in a ‘complete’ trial
Assume uniform sampling over time in each cluster
Choose when to implement the treatment in each cluster.
For example, Is better than ?
And what is the best layout overall?
Lawrie J, Carlin JB, Forbes AB. (2015) Optimal stepped wedge designs. Statistics & Probability Letters , 99:
Girling, A. J., and Hemming, K. (2016) Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models. Statist. Med., 35: 2149–2166.

5. For ‘large studies’ the best design looks like:
Parallel Clusters
100R%
Stepped-Wedge Clusters
A ‘Hybrid’ combination of Parallel and Stepped Wedge clusters

6. 2. Optimising the sampling rate in each cluster over time
In a standard SW design, some of the cells contribute almost nothing to the treatment effect estimate
Better estimates can be got by re-allocating observations from less to more influential cells
Optimal allocation depends on constraints
E.g. Fixed total number of observations; Fixed number in each cluster….
Solution can be a sparse design

7. E.g. A Sparse Design with 6 groups of clusters5 8 9
This is Optimal for fixed total number of observations unless this number, or the ICC, is ‘too large’. (unpubl)

8. The Way Ahead Exact Results Algorithmic Approaches
Useful designs (Optimal, or near-Optimal)

9. Speakers Jennifer Thompson (LSHTM) Jessica Kasza (Monash):
The optimal design of the stepped-wedge trial and a comparison to other trial designs
Jessica Kasza (Monash):
Information content of cluster-periods in stepped-wedge trials
Richard Hooper (Queen Mary)
Trimming the fat from stepped wedge trials: an algorithmic search for the trial design requiring the fewest participants