Moberg J, Kramer M (2015). A brief history of the cluster randomized trial design.

© Jenny Moberg and Michael Kramer. Contact author: Jenny Moberg, Norwegian Knowledge Centre for the Health Services, Boks 7004 St Olavsplass, N-0130 Oslo, Norway. Email:

Cite as: Moberg J, Kramer M (2015). A brief history of the cluster randomized trial design. JLL Bulletin: Commentaries on the history of treatment evaluation (


The cluster randomized trial (CRT) is commonly considered a relatively new research study design (Donner and Klar 2000; Eldridge and Kerry 2012; Murray 1998). Here we trace to a few very early reports the idea of comparing interventions applied to groups of individuals, through the evolution of this idea to the modern-day CRT.  This has been defined as a comparative study in which the units randomized are pre-existing (natural or self-selected) groups whose members have an identifiable feature in common, and in which outcomes are measured in all, or a representative sample of the individual members of the groups (Donner and Klar 2000). Summaries of the reports of many of the examples of CRTs published before the methodological review of this research design by Donner and his colleagues (1990) can be viewed in the James Lind Library (link to JLL bibliography; see Appendix for details of our literature search).

What is a cluster?

The groups used in CRTs vary widely and range in size from families to entire communities. The common feature shared by members of a cluster may be:

Why use cluster randomized trials?

CRTs are well suited and are now commonly used to evaluate public health, health policy and health system interventions. They are ideal for testing interventions when the decision (policy) about whether or not to implement the intervention will be taken on behalf of a group. CRTs are also useful when the nature of the intervention carries a high risk of contamination, that is, when individuals randomized to different comparison groups are in frequent contact with one another and thus may be influenced (‘contaminated’), in either or both directions, by the alternative treatment(s). Contamination is likely to occur in comparisons of public health promotion interventions within the same community, and of different approaches to health care provided by the same clinician to patients under his or her care. In addition to these scientific reasons, cluster designs can also have practical advantages over individual randomization because of lower implementation costs, or administrative convenience.

Early examples of group allocation

The earliest mentions of which we are aware of treatment comparisons in which the intervention was assigned to a group, rather than to an individual, are centuries old. In 1648, Van Helmont proposed a trial of his new methods of treating febrile patients without purging and blood-letting, in which the participants would be put into groups then randomized by “casting lots” to decide which group would receive which of the treatments to be compared (Van Helmont 1648). It is unlikely that this trial ever took place, but the idea of cluster randomization is there.

In 1657, Starkey proposed a trial in defence of van Helmont’s treatment methods, in which groups of patients were to be assigned to be treated by Starkey (according to van Helmont’s methods) or by those of van Helmont’s critics.  Starkey seems to have appreciated that the process of treatment allocation should be designed to prevent confounding by differences between the groups receiving different treatments. He suggested that patients first be grouped in tens. Starkey and his opponent should then alternately divide each ten into two groups of five, allowing those who did not do the dividing to choose one of the groups of five patients. The ‘divider’ should then treat the remaining five patients. As in van Helmont’s proposed trial, the groups of patients did not exist prior to the trial but were created specifically for the trial (Starkey 1657).

Celli’s 1900 trial may be the first in which pre-existing groups were allocated to treatment – an important step towards the modern CRT design (Celli 1900; Ferroni et al. 2011). Celli studied whether mosquito netting reduced malaria in households of Italian railway workers. The households were selected (although not randomized) to receive or not receive the intervention. Neighboring households were used as controls. This trial heralds one of the most common uses of CRTs today: the evaluation of infectious disease control methods and, in particular, of methods to prevent malaria.

The clinical trial reported by Amberson and his colleagues in 1931, challenging the use of gold for treating pulmonary tuberculosis, was an early trial using a single coin toss to allocate two matched comparison groups either to injections of a gold-containing treatment (sanocrysin), or to control injections of distilled water. In addition, patients and the investigators measuring the trial outcomes were blinded to the participants’ treatment allocation (Amberson et al. 1931). This trial has sometimes been considered well designed and conducted, but it falls short of the current standards for a cluster randomized trial; the clusters were created for the trial and only two clusters were randomized (Diaz and Neuhauser 2004).

Many early CRTs in non-medical fields were school-based evaluations of educational interventions. Indeed, methodological discussion of the cluster randomized design appears to have begun in 1940 with Lindquist’s book on methods in education research in schools (Lindquist 1940; Klar and Donner 2004). Much of what Lindquist wrote, however, also applies to clinical and public health interventions.

Recent developments

Only sparse use of CRTs was evident before the 1980s (Bland 2004). However, the last half century has seen a steady increase in the number of CRTs published in the medical literature: from one a year in the 1960s; to seven in 1990, when Donner, Brown and Brasher published their methodological review of CRTs (Donner et al. 1990); to over 120 in 2008.

Every pre-1960s CRT of which we are aware tested some aspect of infectious disease prevention or treatment (Donner et al. 1990; Coburn 1944; Mellanby et al. 1948; Comstock 1962).  In the 1970’s, CRTs were used extensively for such trials, particularly in low-income countries (Storey et al. 1973; Sutter and Ballard 1983; Isaakidis and Ioannidis 2003). CRTs were also recognized as being suitable for evaluating public health interventions aiming to change health behavior, such as improving dental care (Reiss 1976), promoting hand-washing (Black et al. 1981), and attending for immunization (Yokley and Glenwick 1984).

The risk of ‘contamination’ between comparison groups is high in studies evaluating screening interventions. In the 1980’s two large-scale CRTs of screening interventions showed how this design can be used to reduce the influence of contamination on the effects of an intervention. A trial by Grant and colleagues (1989) evaluated the effect of routine counting of fetal movement by pregnant women on the likelihood of antepartum stillbirth. This showed how the CRT design can be useful for assessing the effects of interventions which would otherwise be compromised by a high risk of contamination (Grant et al. 1989).

The Swedish screening mammography study published in 1985 by Tabár and colleagues (1985) is an example of the use of a CRT to evaluate complex public health screening interventions applied to large populations. By offering screening to a community selected at random from matched communities, separated by 200km on average, contamination among communities was reduced, and trial implementation became more practicable (Tabár et al. 1985). These features of CRTs – reduction of contamination, and practicability of very large scale public health trials – are also well illustrated in a trial of the effect of vitamin A supplementation on childhood mortality, morbidity, and preschool growth (reported in Sommer et al. 1986; West et al. 1988; Abdeljaber et al. 1991).

CRTs were also shown to be useful for evaluating the impact of multi-faceted approaches to health improvement, for example, trials of nutritional supplementation and maternal education in expectant mothers and infants at risk of malnutrition (Waber et al. 1981), and a trial of breast cancer screening methods and the nurses who implemented them (Roberts et al. 1984).

Schools have often been used in public health CRTs. They are convenient places to implement health education interventions relevant to children and adolescents, such as prevention of tobacco, alcohol, and drug use; promotion of sexual health; and primary prevention of chronic disease through promotion of healthy eating and physical activity. Entire schools or classes within schools are ready-made clusters (Dwyer et al. 1983; Lloyd 1983; Simons-Morton 1984; Dielman et al. 1989; Schinke et al. 1986). School clusters have also been used to evaluate interventions aimed at helping children to become ‘health messengers’, as in a trial assessing whether hypertension education of children had an impact on the blood pressure of their parents (Fors et al. 1989).

CRTs have become recognized as being valuable in evaluating many different types of health system interventions – health care delivery (Bass et al. 1986; Choi et al. 1986; Seto et al. 1989), governance, financial arrangements, and implementation strategies. A CRT reported by Vogt et al. in 1983 was used to compare methods to increase reporting of notifiable diseases by doctors (Vogt et al. 1983). This trial also illustrates an important group of CRTs in which clusters consist of patients treated by the same clinician. These trials are often called ‘professional cluster randomized trials’. A clinician is randomly assigned to the intervention, and the intervention is targeted at individual clinicians – not at his or her patients. Such clinician-targeted interventions often aim to modify the behaviour of healthcare providers in some way, for example, by using clinical guidelines, training, or decision support systems (Chassin and McCue 1986; McDonald 1984; Stross et al. 1986; Evans et al. 1986).

In some such trials, clinicians implement the intervention without involving patients in the decision, for example, in reporting cases of a notifiable disease (Vogt et al. 1983), or arranging for medical assistants to screen for and manage patients with hypertension (Bass et al. 1986). With other clinician-targeted interventions, the intervention is intended to impact on both clinician practices and on patient outcomes, for example, educational programmes to help physicians improve blood pressure control among their patients (Evans et al. 1986).

Through the 1990s, the number of published trials including cluster randomization increased (Bland 2004), and the terms ‘group randomized’ (Murray 1998), ‘community randomized’ (Donner et al. 1990; Donner and Klar 2000), and even ‘place randomized’ were all used to describe CRTs. A BMJ series on statistics in 1997 and 1998 used the term ‘cluster randomized’ (Bland and Kerry 1997; Kerry and Bland 1998). By the early 2000s, with the published extension of the CONSORT statement on reporting guidelines for CRTs (Campbell et al. 2004) and several reviews of CRTs (Puffer 2003; Isaakidis and Ioannidis 2003; Eldridge et al. 2004), the term ‘cluster randomized trial’ had become the most commonly used term for this design. The publication of important, large scale, well-conducted CRTs in this century, such as those evaluating the effects of community groups on birth and other outcomes in poor rural populations (Manandhar 2004; Morrison et al. 2011; Azad et al. 2010; Tripathy et al. 2010; Lewycka et al. 2013), can be considered as a ‘coming of age’ of the CRT design.

Current and future challenges  

Study design
As experience with CRTs has increased over time, difficulties and problems have become apparent. The design (especially with respect to blinding), analysis, and conduct of CRTs are often more complicated than for individually randomized trials. CRTs are conducted with as few as one intervention and one control cluster, and insufficient numbers of clusters (inadequate sample sizes) is a persistent problem. We have not included CRTs with fewer than two clusters in each arm in the JLL, or as examples in this article. Stratification and matching have been used to increase the comparability of the clusters, which helps increase precision with small numbers of clusters. While blinding of participants and outcome assessors is ideal in all randomized trials, it is often difficult or even impossible in CRTs. The units of randomization and the units of observation may be different, and this affects informed consent, recruitment, sample sizes, randomization, and analysis.

Study analysis
CRTs can be analysed in the same way as any individually randomized trial by each cluster providing one data item into the analysis, for example average blood pressure among all patients of a randomized physician. By using all the individual data points in each cluster in the analysis, the statistical power of a trial can be increased. However the effect of clustering must be considered. As early as 1940, Lindquist recognized the need to account for clustering in the analysis of CRTs (Lindquist 1940; Klar and Donner 2004). In 1978, Cornfield pointed out the need for special consideration of the statistical features of CRTs in health research and, in particular, the need to account for between- cluster variation (Cornfield 1978). And as Donner and Klar pointed out in 2000, analysis must also take into consideration variation in cluster size, which is often substantial (Donner and Klar 2000).

Members of clusters are more likely to have similar outcomes than a randomly selected sample of individuals from the same population, particularly when members self-select into a cluster. The most commonly used measure of the degree of similarity among members of a cluster is the intra-class correlation coefficient (ICC). The larger the ICC, the larger the number clusters of individuals needed to achieve comparable statistical power to a trial using individual randomization. Analysing a CRT without accounting for clustering yields a falsely low estimate of variance and hence inflates statistical significance. And as shown by Kramer et al. (2009), loss of statistical power is even more dramatic when the outcome measurements are also clustered with treatment (‘double jeopardy’).

A recent study re-analysing the results from CRTs of health system interventions using time series methods shows that, if data from CRTs are analysed without taking account of trends over time, the findings may be misleading (Fretheim et al. 2014). Fretheim and colleagues suggest adding times series approaches to the overall comparison of randomized groups, so as to gauge changes in effect of the intervention over time.

The quality of reports of CRTs has been very variable. Some studies are reported simply as ‘randomized trials’, leaving readers unaware that the unit of randomization is anything other than the individual. Specific key words that would identify CRTs are often not provided in abstracts, so full-text publications have to be retrieved to establish whether or not the study reported was a CRT. Despite extension of the CONSORT statement on reporting guidelines for CRTs (Campbell et al. 2004), the titles or abstracts of 50% of CRTs still fail to indicate this (Taljaard et al. 2010). Conversely, other papers report CRTs in their titles but, on careful inspection, are clearly not CRTs (Bland 2004).

Summing up
CRTs have a long history in both educational and health research and, over the last several decades, have assumed an increasing role in rigorous evaluations of complex clinical, public health, and health system interventions in which individual randomization is likely to be ‘contaminated’ by contact among individual participants randomized. CRTs can also help overcome the administrative barriers and economic costs inherent in contacting, recruiting, and randomizing large numbers of individuals. Major challenges include ensuring statistical power by recruiting adequate numbers of clusters, possible use of randomized crossover of clusters (Connolly et al. 2013; Bellomo et al. 2013; Stockwell et al. 2015), minimizing intra-cluster correlation of outcomes and measurements, and better reporting.

We are grateful to Tikki Pang for providing WHO support to assist in the identification of reports of cluster randomized trials, to Marit Johansen for searching for them, and to Allan Donner and Atle Fretheim for helpful comments on an earlier draft.

This James Lind Library article has been republished in the Journal of the Royal Society of Medicine 2015;108:192-198. Print PDF


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Appendix: Literature search for pre-1990 reports of Cluster Randomized Trials (CRTs)

We identified reports of cluster randomized or quasi-randomized trials trials (CRTs) of any healthcare intervention published before 1990 with at least two clusters in each arm.

We searched records submitted to the Cochrane Central Register of Controlled Trials (CENTRAL) by the following six Cochrane groups: Effective Practice and Organization of Care (EPOC); Consumers and Communication (COMMUN); Public Health (PUBHEALTH); HIV/AIDS (HIV); Sexually Transmitted Diseases (STD); and Infectious Diseases (INFECTN). The combined search strategies are shown below. We also hand-searched the reference lists of key review and methodology papers, and unpublished databases found through personal contacts. We screened titles and abstracts from these sources and retrieved full texts for all possible candidate studies.

Search for pre 1990 CRTs

Cochrane Central Register of Controlled Trials (CENTRAL),

  1. MeSH descriptor Cluster Analysis, this term only
  2. MeSH descriptor Small-Area Analysis, this term only
  3. cluster*:ti,ab,kw
  4. (randomi* or randomly) NEAR/3 (group or groups or community or communities or site or sites or district or districts or institution or institutions or hospital or hospitals or ward or wards or unit or units or clinic or clinics or department or departments or facility or facilities or center or centers or centre or centres or school or schools or village or villages):ti,ab,kw
  5. (community or communities) NEAR/3 intervention*:ti,ab,kw
  6. (#1 OR #2 OR #3 OR #4 OR #5)
  7. (#6), to 1990

Search for the register of policy CRTs

Cochrane Central Register of Controlled Trials (CENTRAL) 2010, Issue 3, part of The Cochrane Library.

  1. MeSH descriptor Cluster Analysis, this term only
  2. MeSH descriptor Small-Area Analysis, this term only
  3. cluster*:ti,ab,kw
  4. (randomi* or randomly) NEAR/3 (group or groups or community or communities or site or sites or district or districts or institution or institutions or hospital or hospitals or ward or wards or unit or units or clinic or clinics or department or departments or facility or facilities or center or centers or centre or centres or school or schools or village or villages):ti,ab,kw
  5. (community or communities) NEAR/3 intervention*:ti,ab,kw
  6. (#1 OR #2 OR #3 OR #4 OR #5)
  7. “SR-EPOC” or “SR-COMMUN” or “SR-PUBHLTH” or “SR-HIV” or “SR-INFECTN” or “SR-STD”
  8. (#6 AND #7)

Reviews, methodology papers, books searched

Bowater RJ, Abdelmalik SM, Lilford RJ (2009). The methodological quality of cluster randomized controlled trials for managing tropical parasitic disease: a review of trials published from 1998 to 2007 .Trans R Soc Trop Med Hyg 103(5):429-36.

Donner A, Klar N (2000). Design and analysis of cluster randomization trials in health research. London: Arnold.

Eldridge SM, Ashby D, Feder GS, Rudnicka AR, Ukoumunne OC (2004). Lessons for cluster randomized trials in the twenty-first century: a systematic review of trials in primary care. Clin Trials 1(1):80-90.

Eldridge S, Ashby D, Bennett C, Wakelin M, Feder G (2008). Internal and external validity of cluster randomized trials: systematic review of recent trials. BMJ 336(7649):876-80.

Handlos LN, Chakraborty H, Sen PK (2009). Evaluation of cluster-randomized trials on maternal and child health research in developing countries. Trop Med Int Health 14(8):947-56.

Isaakidis P, Ioannidis JP (2003). Evaluation of cluster randomized controlled trials in sub-Saharan Africa. Am J Epidemiol 158(9):921-6.

Kramer MS, Martin RM, Sterne JA, Shapiro S, Mourad D, Platt RW (2009). The double jeopardy of clustered measurement and cluster randomisation. BMJ 339:503-505.

Taljaard M, McGowan J, Grimshaw JM, Brehaut JC, McRae A, Eccles MP, Donner A (2010). Electronic search strategies to identify reports of cluster randomized trials in MEDLINE: low precision will improve with adherence to reporting standards. BMC Med Res Methodol 16;10:15.