An introduction to the stepped wedge cluster randomised trial
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Transcript of An introduction to the stepped wedge cluster randomised trial
05/03/2023
The stepped wedge trial (SW-CRT): Recommendations for research methods
and reporting
1: University of Birmingham, UK2: University of Warwick, UK
3: University of Ottawa, Canada
Karla Hemming1
Alan Girling1, James Martin1, Celia Brown2 Richard Lilford2, Peter Chilton1 Monica Taljaard3
What is a SW-CRT• Modification of cross-over design:
- All clusters start in control- Clusters (or groups of clusters) cross to intervention at
randomly assigned times until all have received intervention- Outcome typically observed at each time point
1 2 3 4 5 6
TimeSTEPS (Cluster or Group of Clusters)
1
2
3
4
5
Exposed to intervention Unexposed to intervention
Cross-sectional designs only
• Assume all participants at each step or time point are different
• Other types of designs include:
– Cohort design – where individuals have repeated measures
– Open cohort – where individuals have repeated measures and new individuals can join the study over its duration
Example: the EPOCH trial
• Intervention:– Service delivery intervention to improve care of patients
undergoing emergency laparotomy• Setting:
– Includes 90 hospitals– Rolled out to 15 geographically close hospitals at a time
• Outcome:– 90 day mortality
• Sample size:– TSS is 27,500– 90% power to detect a change from 25% to 22%
Example: The EPOCH study
Systematic review of SW-CRTs Rapid update
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5
10
15
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1985 1990 1995 2000 2005 2010 2015
Cum
ulati
ve n
umbe
r of S
W-C
RTs p
ublis
hed
Year
Cumulative completed
Cumulative protocols (excludes subsequently completed and published studies)
Quality of reporting of cluster effects
Protocols published in journals(N=14)
Results papers published in journals
(N=18)
SS calculation reported 14 (100%) 12 (67%)
ICC stated in methods 12 (86%) 5 (28%)
Uncertainty of ICC considered in methods
3 (21%) 1 (6%)
Results fully accounted for clustering
N/A 6 (33%)
ICC given in results N/A 1 (6%)
Some unresolved (or debated) issues:
• Design: –How to determine sample size (number
clusters, cluster size)?–Which is the most efficient design?
• Analysis: How to analyse these studies:–Temporal confounding
Design
Conventional representation of designs
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This representation leads us to believe…
• The SW-CRT is of longer duration than the PCRT and CRT-BA
• The cluster sizes in the SW-CRT are larger than those in the PCRT
• The SW-CRT allows staggered roll-out and the PCRT does not
This representation leads us to believe…
• The SW-CRT is of longer duration than the PCRT and CRT-BA– We claim: false
• The cluster sizes in the SW-CRT are larger than those in the PCRT– We claim: false
• The SW-CRT allows staggered roll-out and the PCRT does not – We claim: false
Alternative representation (unified framework)
Motivating example…
• PCRT in primary care• GP practices are clusters• Patients presenting with a new diagnosis of diabetes are the
individuals• These patients wont all present at a fixed point in time• Rather they will become eligible for the study over a prolonged
period of time
Time
Using this representation:
• The SW-CRT is of the same duration as the PCRT and CRT-BA
• The cluster sizes in the SW-CRT are the same as those in the PCRT
• The SW-CRT allows staggered roll-out and so does the PCRT
The staggered PCRT
• One often cited reason for the SW-CRT is the phased implementation
• This is possible under parallel design
• Balanced on time, so no time effects
0 1 2 3 4 5
Time (step)Cluster
1
2
3
4
5
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Intervention
Control
Staggered Cluster Study
Cluster unexposed to intervention
Cluster in transition period
Cluster exposed to intervention
Efficiency
How to determine which design to use?
Efficiency – depends on ICC ICC=0.01 ICC=0.1
PCRT PCRT-BA SW-CRT PCRT PCRT-BA SW-CRTNumber of clusters 20 20 20 20 20 20Cluster size 50 50 50 50 50 50Total sample size 1000 1000 1000 1000 1000 1000 Number of steps 0 1 4 0 1 4Number of clusters per step 10 5 10 5
Power 0.97 0.87 0.88 0.50 0.77 0.82Study to detect a moderate effect size of 0.3 (SD 1) at 5% significance
Sometimes the CRT is infeasible
• Example:– NI is 788: 0.2 80% power and 5% significance – ICC is 0.10– 30 clusters
• Can’t run this trial using a parallel design:– minimum number of clusters is (p*NI)– i.e. 788*0.1=79
• Under SW-CRT with 4 steps:– Need 75 observations in 30 clusters– TSS of 2250
Efficiency comparisons
Minimise total sample size or clusters?
• Design constraints– NI is 6,426; 10% to 8%; ICC 0.05– Cluster trial– Over each year possible to recruit M=800 per cluster
• SW-CRT :– 4 steps, 26 clusters, 1 year, TSS=20,800
• CRT:– M=800, 330 clusters, 1 year, TSS=264,000!!!!– M=200, 354 clusters, 3 months, TSS=70,000– M=20, 630 clusters, Random Sample, TSS=12,500
Sample size and power
How do we work it out?
Simple notation
Notation Sample size for RCT NI
ICC pNumber clusters kNumber steps tCluster size per step mTotal cluster size M
Sample size calculations
• Accommodate:– Clustering– Time effects
• Seminal paper by Hussey and Hughes– Power for fixed design
• Algebraically complicated, BUT:– Stata Function
0 1 2 3 4 5
Time (step)Cluster
1
2
3
4
5
6
Intervention
Control
Conventional Stepped Wedge Study
Stata power function
Extensions allow:• Two levels
– i.e. wards within hospitals
• Transition periods – i.e. training periods
• Varying cluster size– (work in progress)
Hemming K, Girling A. A menu driven facility for sample size for power and detectable difference calculations in stepped wedge randomised trials. STATA Journal. 2014
Determining number of clusters:
• Design effect (Woerterman, 2013):
• Sample size needed:
N=TSSRCT *DESW * (t+1)
Hemming K, Girling A. The efficiency of stepped wedge vs. cluster randomized trials: stepped wedge studies do not always require a smaller sample size. J Clin Epidemiol. 2013;66(12):1427-8.
Determining number of clusters:
• Design effect (Woerterman, 2013):
• Sample size needed:
N=TSSRCT *DESW * (t+1)
Hemming K, Girling A. The efficiency of stepped wedge vs. cluster randomized trials: stepped wedge studies do not always require a smaller sample size. J Clin Epidemiol. 2013;66(12):1427-8.
Need this!
What cluster size do I need?
Setting straight the sample size determination for stepped wedge and cluster randomised trials: design effects and illustrative examples Karla Hemming and Monica Taljaard Submitted to J Clin Epi
Analysis
Analysis • Summarise key characteristics by exposure / unexposed status
– Identify selection biases
• Analysis either GEE or mixed models– Clustering– Time effects
• Imbalance of calendar time between exposed / unexposed:– The majority of the control observations will be before the
majority of the intervention observations– Time is a confounder!
• Unadjusted effect meaningless
Hemming K., Haines T.P., Chilton P.J., Girling A.J., Lilford R.J. The stepped wedge cluster randomised trial: rationale, design, analysis and reporting. The BMJ, in press
31
Example 1: Maternity sweeping
• Objective: evaluate a training scheme to improve the rate of membrane sweeping in post term pregnancies
– Primary outcome:• Proportion of women having a membrane sweep
– Cluster design:• 10 teams (clusters)• Pragmatic design – rolled out when possible• Transition period to allow training
32
Example 1: Maternity sweeping (transition period)
Example 1: Underlying trend 0
.2.4
.6.8
Prop
. wom
en sw
ept (
95%
CI)
20005/03/12 23/04/12 18/06/12 13/08/12week commencing
P-value for trend <0.05
Example 1: results
Unexposed to
interventionn=1417
Exposed to intervention
n=1356Relative Risk P-
value
Number of women offered and accepting membrane sweepingNumber (%) 629 (44.4%) 634 (46.8%) Cluster adjusted 1.06 (0.97, 1.16) 0.21Time and cluster adjusted Fixed effects time 0.88 (0.69, 1.12) 0.30Linear time effect 0.90 (0.73, 1.11) 0.34
Example 1: results
Unexposed to
interventionn=1417
Exposed to intervention
n=1356Relative Risk P-
value
Number of women offered and accepting membrane sweepingNumber (%) 629 (44.4%) 634 (46.8%) Cluster adjusted 1.06 (0.97, 1.16) 0.21Time and cluster adjusted Fixed effects time 0.88 (0.69, 1.12) 0.30Linear time effect 0.90 (0.73, 1.11) 0.34
Going up!
Example 1: results
Unexposed to
interventionn=1417
Exposed to intervention
n=1356Relative Risk P-
value
Number of women offered and accepting membrane sweepingNumber (%) 629 (44.4%) 634 (46.8%) Cluster adjusted 1.06 (0.97, 1.16) 0.21Time and cluster adjusted Fixed effects time 0.88 (0.69, 1.12) 0.30Linear time effect 0.90 (0.73, 1.11) 0.34
Going up!
Going down!
Explanations
• Rising tide– General move towards improving care – perhaps due
to very initiative that prompted study investigators to do this study
• Contamination– Unexposed clusters became exposed before their
randomisation date• Lack of precision
– Intervention wasn’t ruled out as being effective
Recommendations
Recommendations• SW-CRT a pragmatic study design which reconciles the need for
robust evaluations with political or logistical constraints.– But, can have a staggered parallel CRT
• The SW-CRT design is recommended when:– Higher the ICC (process outcomes)– Limited number of clusters– Routinely collected outcome data
• Design and analysis– Appropriate consideration of time effects in power and analysis
Next steps …
• Published a set of recommendations for reporting in BMJ.. out soon…
• Updating the systematic review of quality of reporting– Look at quality of reporting of SS calculations– Looking at ethical issues around recruitment and concealment of
allocation
• Consort Extension for SW-CRTs
• Alan and James – numerical work on varying cluster size
AcknowledgementsWe acknowledge financial support from:
• The National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care for West Midlands (CLAHRC WM).
• The Medical Research Council Midland Hub for Trials Methodology Research [grant number G0800808].
References
• Hemming K, Girling A. The efficiency of stepped wedge vs. cluster randomized trials: stepped wedge studies do not always require a smaller sample size. J Clin Epidemiol. 2013;66(12):1427-8.
• Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007;28(2):182-91.
• Hemming K, Girling A. A menu driven facility for sample size for power and detectable difference calculations in stepped wedge randomised trials. STATA Journal. 2014;[In Press]