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Design Choices for Treatment Arms
Parallel Group Design
This is the most common design for both individually and cluster randomised trials. Under this design, each cluster remains in the arm it was randomly allocated to throughout the whole trial.
Three arm trials
Given the expense and logistical complexity associated with CRTs, and the difficulty in enrolling enough clusters to provide an adequate sample size in each treatment arm, the great majority of CRTs follow a study design in which clusters are randomised to only two treatment arms. Three-arm trials are sometimes feasible, however, CRTs with more than three arms are very uncommon. However, when considered, they follow two main approaches: The first compares two different interventions with a control arm, and the second compares the same intervention given at varying levels of intensity with a control arm to produce a dose response analysis.
Conventionally, to estimate the effect of two interventions would require either designing two trials or conducting a three-arm trial, which has the disadvantage of a smaller sample size in each arm. Factorial designs allow the study of the independent effects of two interventions in the same trial. This has the advantage of being cost efficient, and conserving sample size. The design takes a 2 X 2 layout resulting in four treatment arms: one arm receiving the first intervention, another receiving the second intervention, an arm receiving both interventions, and finally a control arm. The model results in four treatment arms, however, estimating the effect of each intervention is done by comparing a relevant combination of two of the arms against the combination of the remaining two arms. This approach is only valid if there is no interaction between the interventions. Where interactions are expected, or desired, factorial designs can be used to identify the joint effect of two interventions, however larger sample sizes may be required.
Cross Over Design
The aim of this design is to control for time trend. This design is commonly used in individually randomised trials, and has been adopted for CRTs. Each cluster receives two treatments, one after the other. There is often a period in between called the washout period to avoid any carry-over effects.
Stepped Wedge Design
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Type and Size of Clusters
One of the first decisions to be taken when designing a CRT relates to the choice and definition of the clusters that are to be randomised during the trial. There is a wide variety of types and sizes of clusters ranging from families or households with a few individuals, to large geographic areas containing millions of individuals. The practical elements of implementing such trials are very different. Chapter 4 considers the different types of study cluster and discusses the key issues to be considered in choosing cluster size.
Contamination occurs when responses in one cluster are distorted because of contact with individuals from outside the cluster, and this may still occur and pose an important problem in CRTs. This could happen due to contact between the intervention clusters and the control clusters. It could also happen due to contact between the intervention clusters or the control clusters and the wider population. Strategies to reduce the degree of contamination in a CRT include selecting clusters that are sufficiently distant and well separated from each. In circumstances when geographic zones are assigned to either the intervention or control arms rather than specific communities, buffer zones are used to that clusters do not have common boundary between them. These two strategies are used to ensure that contamination does not occur between the intervention and control clusters. The ‘fried egg design’ is a strategy used to reduce contact between the intervention or control clusters and the wider population. The ways by which contamination occurs and the strategies to reduce them are discussed further in Chapter 4.
Approaches to measuring outcomes from individuals
The outcomes of interest are measured from a sample of individuals selected from each cluster. There are two main approaches to the measuring individuals, depending on the outcome: cross sectional surveys or cohorts. A full discussion on when each may be used and their advantages and disadvantages is found in Chapter 8.
Repeated cross sectional Samples
Cross sectional surveys require taking a repeated sample from each cluster at different times. It is used when the measure of the outcome is a binary outcome (such as HIV or smoking prevalence) or a quantitative endpoint (such as the mean cholesterol level or mean height of children).
Cohort Follow Up
The cohort approach involves following up selected individuals over time. This is used when the measure of the outcome is a rate or risk of events occurring during a specified follow up period. The cohort can consist of the total population of a cluster or a random sample from that cluster. When the total population is to be followed up, it must be specified whether new people entering the population at a later date will be considered or to limit the study to only those seen at baseline.
When designing a CRT, sample size is one of the most important factors to consider. Inadequate sample size increases the random error, reduces the power of the study, and thus reduces the ability to quantify effect accurately. Chapter 7 sets out in detail the methods needed to select an appropriate sample size for a CRT. This includes methods for unmatched, matched, and stratified study designs as well as methods to select an appropriate sample size for each cluster.
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