The main difference when reporting a cluster trial, as opposed to an individually randomised trial, is that there are two levels of inference rather than one: the cluster level and the individual level.24
Thus, to allow readers to interpret the results appropriately, it is important to indicate explicitly the level at which the interventions were targeted, the hypotheses were generated, the outcomes were measured, and randomisation was done.
Item 3: Eligibility criteria for participants and clusters and the settings and locations where the data were collected.
The study comprised 41 practices in Wessex... Inclusion criteria were ≥ 4 medical partners; list size > 7000; a diabetes register with > 1% of practice population; and a diabetes service registered with the health authority... Nurses reported all new cases of diabetes to the trial office. Willing patients aged 30-70 were included in the trial. Patients were excluded if they were private patients, housebound, mentally ill, had severe learning difficulties, or were subsequently found to have been diagnosed previously with, or not to have, diabetes, or were found to have type 1 diabetes.25
Because there are two levels of inference, the eligibility criteria for clusters, as well as participants, need to be reported. In a cluster trial, the primary eligibility criterion is often all the clusters in a defined geographical area.
Item 4: Precise details of the interventions intended for each group, whether they pertain to the individual level, the cluster level, or both, and how and when they were actually administered.
We... paired the 14 [urban sectors of Trujillo, Venezuela] according to the incidence of cutaneous leishmaniasis in the 12 months before the baseline household survey. For each of the seven pairs we randomly allocated one sector... to the intervention group and the other to the control group... In the intervention group the windows of all 241 houses (with a total of 1336 inhabitants) were covered with loosely hanging polyester curtains impregnated with the pyrethroid insecticide... In the 222 houses in six of the control sectors the windows were covered with non-impregnated curtains and in one randomly selected control sector with 106 houses no curtains were provided.”26
Again, if the intervention was targeted at the cluster level, specific details of how it was administered should be described.
Item 5: Specific objectives and hypotheses, and whether they pertain to the individual level, the cluster level, or both.
We aimed to compare the effectiveness of three different interventions for improving the secondary preventive care for patients with coronary heart disease delivered at the level of general practice.27
Descriptions of specific objectives and hypotheses need to make it clear whether they pertain to the individual level, the cluster level, or both. Knowing the level of inference will subsequently aid interpretation of the statistical methods.
Item 6: Report clearly defined primary and secondary outcome measures, whether they pertain to the individual level, the cluster level, or both, and, when applicable, any methods used to enhance the quality of measurements (eg multiple observations, training of assessors).
We evaluated the effect of a computer based clinical decision support system and cardiovascular risk chart [both targeted at physicians—that is, clusters] on patient centred outcomes of absolute cardiovascular risk and blood pressure.28
Whether an intervention is evaluated at the cluster level or the participant level has implications for the appropriate analysis of the outcome data. It is therefore important that the level at which outcomes are measured be explicit in the trial report.
Item 7: How total sample size was determined (including method of calculation, number of clusters, cluster size, a coefficient of intracluster correlation (ICC or k), and an indication of its uncertainty) and, when applicable, explanation of any interim analyses and stopping rules.
We calculated sample size with a method that takes into account the intracluster correlation coefficient, the number of events, the expected effect, and the power of the study. We assumed an intracluster correlation of ρ = 0.2, a minimum of 25 patients for each practice, and a worst case control rate of 50%. Under these assumptions we anticipated a power of 87% to detect a difference of 15% in rates between the two groups with α = 0.05 with 60 practices for each intervention group.29
A principal difference between a cluster randomised trial and an individually randomised trial is the calculation of the sample size. As indicated above, to retain equivalent power to an individually randomised trial, the number of individuals in a cluster randomised trial needs to be increased. The key determinants of the increase required are the intracluster correlation and the cluster size. Reports of cluster randomised trials should state the assumptions used when calculating the number of clusters and the cluster sample size.
Item 8: Method used to generate the random allocation sequence, including details of any restriction (eg blocking, stratification, matching).
To help ensure comparability of the intervention and comparison communities with respect to baseline HIV and STD prevalences and risk factors for infection, the communities were matched into six pairs according to the following criteria: roadside, lakeshore, island, or rural location; geographical area (paired communities were generally in the same district and less than 50 km apart); and prior STD attendance rates at the health centre.30
Cluster randomised trials may use a simple, completely randomised design; a matched cluster design; or a stratified design. In individually randomised trials random assignment generally ensures that any baseline differences in group characteristics are the result of chance rather than some systematic bias.31
This cannot be assumed, however, for the cluster randomised trial.
Although the assumption holds for cluster specific characteristics (that is, characteristics of the randomly allocated clusters), the researcher has little control over the individuals within each cluster7
and the number of clusters is usually relatively small. As a result, some form of constraint (matching or stratification) is often imposed on randomisation in a cluster randomised design in an attempt to minimise imbalance across treatment groups. Any constraint imposed on the cluster randomised trial affects the sample size and the analysis and thus should be reported.
Item 9: Method used to implement the random allocation sequence, specifying that allocation was based on clusters rather than individuals and clarifying whether the sequence was concealed until interventions were assigned.
Practices agreeing to participate were... assigned by simple random allocation to use the computer decision support system... Randomisation was performed with a table of random numbers by a researcher not involved in the study and who was blind to the identity of the practices.28
In individually randomised trials, adequate concealment of the treatment allocation is crucial to minimising potential bias. If the person recruiting participants has foreknowledge of the allocation, bias can result.32
In a cluster randomised trial, allocation of treatment is predetermined for each member of the cluster. Hence the potential for selection bias (selective inclusion of patients into the trial) within clusters is particularly high.4,5
It is, therefore, particularly important that authors outline any strategies that were implemented to minimise the possibility of selection bias—for example, whether all patients within a cluster were included, or, if not, whether recruitment of patients was by a person masked to the cluster allocation.
Item 12: Statistical methods used to compare groups for primary outcome(s) indicating how clustering was taken into account; methods for additional analyses, such as subgroup analyses and adjusted analyses.
Because we randomised obstetric units... we analysed rates of marker clinical practices by obstetric units.22
We used cluster specific methods because practices rather than patients were randomised... We used hierarchical logistic regression.29
Identification of the level of inference allows readers to evaluate the methods of analysis. For example, if the intervention was targeted at the cluster level (at general practitioners rather than patients, for example) and outcomes were aggregated at the cluster level, sophisticated cluster adjusted analyses are not needed (as in the first example above). If outcomes were measured at the individual patient level, however, the analysis would need to adjust for potential clustering in the data (as in the second example above).