In most clinical trials participants are randomised as individuals to different treatments. Sometimes individual allocation is not possible or desirable, and groups of individuals are randomised instead: this is known as cluster or group randomisation. Many reasons for using cluster allocation exist. For example, evaluation of clinical guidelines or medical education on patient outcomes almost always requires that healthcare professionals are the “unit” of allocation.
Although randomised trials are the most robust evaluative method, poorly conducted studies are susceptible to different forms of selection bias that can make their results unsound. Methodological reviews of individually randomised trials have shown that rigorously conducted trials produce different effect estimates from poorly conducted studies.1,2
Less attention has been paid, however, to cluster trials. Cluster trials are generally more difficult to design and execute than individually randomised studies, and some design features of a cluster trial may make it especially vulnerable to a range of threats that can introduce selection bias.
In cluster trials potential bias in the execution of the trial can occur at two levels, the first of which is the cluster level. Randomisation of clusters needs to be undertaken carefully and preferably independently. Otherwise, biased allocation may occur (certain clusters being allocated to a particular arm on the basis of reasons that might affect outcome). It is theoretically possible for allocation of clusters to be subverted, as has happened in individually randomised trials.3
Similarly, once clusters have been allocated it is important, as with individually randomised trials, to try to retain the cluster in its allocated group and avoid the cluster dropping out, to avoid the risk of attrition bias.
The second level at which bias can occur in cluster trials is after the clusters have been allocated and when individual participants are recruited into the study. Sometimes identification and recruitment of participants and assessment of outcome in a cluster trial are relatively straightforward with little scope for bias. For example, in an evaluation of the effect of offering routine influenza vaccination to healthcare workers on patient mortality, hospitals were randomised to offer routine vaccination to staff or not.w1 Any differences between the groups were then observed by using mortality data. Two important methodological aspects to this trial, and other similar cluster trials, limit the risk of bias. These are complete identification and inclusion of participants, partly owing to the fact that consent was not needed for either treatment or collection of data. Because all the participants were identified and included at the point of randomisation, except for chance imbalances the two groups should be similar at baseline (assuming that the allocation procedure was fair), which avoids the threat of selection bias.
In some cluster trials identification and inclusion of participants and assessment of outcome are less straightforward. Often participants have to be recruited prospectively after randomisation. For example, in a trial of the effectiveness of a training package for general practitioners, patients had to be identified prospectively after the general practitioner had been randomised.w2
The prospective inclusion of participants can potentially lead to selection bias through the recruitment of different types of participant by the researcher or clinician. If the person prospectively recruiting participants has “foreknowledge” of the allocation group then, as shown in individually randomised trials, bias can result.3
In addition to this source of selection bias, another can be introduced by the participant if consent is needed after randomisation.
Selection bias can be introduced if consent is withheld for either treatment or data collection. This is a well known disadvantage of acquiring consent after randomisation in individually randomised trials (known as Zelen's method4
), because some refusal of treatment or data collection will usually occur.5
This is less of a problem in non-Zelen designs, as participants are told in advance about the treatment options and if they decline to be exposed to one of the options they are not randomised (although some may decline in the period between allocation and receipt of treatment).
Several ways of avoiding the biases outlined above exist. One is to try to identify trial participants before randomisation and obtain consent for treatment, data collection, or both before allocation. Use of prior identification and prior consent avoids potential biases occurring through foreknowledge of the allocation schedule, by the researcher and patient. If this is not possible, identification and recruitment of participants should ideally be undertaken by someone blinded to the group allocation.
Another problem that can lead to bias, in both individual and cluster randomised trials, is the differential application of inclusion and exclusion criteria. Differential exclusion between groups in an individually randomised trial of breast cancer screening, identified in a systematic review, has led to questions about its rigour.6
Again this problem can be reduced if the person applying the criteria is blinded to the group allocation.
In this paper we review some recently published cluster trials to determine the extent of their risk of bias. We also describe the steps that some authors took to reduce these risks.