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Author's personal copy Vaccine 27 (2009) 1928–1931

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Designing phase III or IV trials for vaccines: Choosing between individual or cluster randomised trial designs Paul Vaucher ∗ University General Medicine Unit, University of Lausanne, Bugnon 44, 1011 Lausanne, Switzerland

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Article history: Received 6 November 2008 Received in revised form 20 January 2009 Accepted 22 January 2009 Available online 31 January 2009 Keywords: Vaccines Clinical trials as topic Research design

a b s t r a c t The choice of design between individual randomisation, cluster or pseudo-cluster randomisation is often made difficult. Clear methodological guidelines have been given for trials in general practice, but not for vaccine trials. This article proposes a decisional flow-chart to choose the most adapted design for evaluating the effectiveness of a vaccine in large-scale studies. Six criteria have been identified: importance of herd immunity or herd protection, ability to delimit epidemiological units, homogeneity of transmission probability across sub-populations, population’s acceptability of randomisation, availability of logistical resources, and estimated sample size. This easy to use decisional method could help sponsors, trial steering committees and ethical committees adopt the most suitable design. © 2009 Elsevier Ltd. All rights reserved.

1. Introduction Vaccines have become one of the most important interventions on which public health relies on to reduce incidence and prevalence of disease and even to eradicate some of them. With vaccines, individual immunity is sought to protect an individual from developing a particular disease when exposed to the infectious agent. However, vaccines can also protect a community from developing an epidemic even if a proportion of the population remained unimmunised. This is due to the “herd effects” [1–3] of vaccination. Benefit and safety of vaccination programs can therefore not only be observed at an individual level but also at a community level. Individual randomised trials (IRTs) remain the “gold standard” for determining which participant will receive either the vaccine or the control treatment as the probability that participants’ known or unknown confounders will be balanced between treatment groups is maximised by this approach [4]. “However, through shedding, live vaccines can induce an immune reaction for those that have not been vaccinated (herd immunity). In IRT this could introduce contamination between treatment arms. Furthermore, IRT do not make it possible to estimate the total effect for large-scale trials where indirect effect is also believed to take place (herd immunity or herd protection). In IRT, both treatment arms are equally affected by herd effects making of this design the privileged choice to estimate direct effects of vaccination. In large-scale trials, where an important proportion of the population is vaccinated, the vaccine’s total effect can therefore be underestimated; the risk of falsely pre-

∗ Tel.: +41 21 314 61 16; fax: +41 21 314 75 90. E-mail addresses: [email protected], [email protected]. 0264-410X/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.vaccine.2009.01.107

suming the intervention not to be effective (type II errors) could be increased. To compensate this loss of power, Slymen and Hovell [5] have proposed to adjust the sample size in IRTs using an inflation factor. This will however not prevent IRT to measure a direct effect bellow clinical relevance for a vaccine which would be considered as effective had we also considered indirect effects which takes place in both treatment arms.” Since 1991, cluster randomisation instead of individual randomisation has been suggested to differentiate direct from indirect vaccine effects [3]. In this design, communities instead of individuals are randomised to interventions. Contrarily to individual randomisation trials, in cluster randomised trials (CRTs), events are not assumed to occur independently one from another. It is therefore also possible to infer effects of an intervention at a group level also taking communities’ ecological characteristics into consideration [6,7]. This is feasible as long as epidemiological units are respected and that sufficient clusters are involved to satisfy statistical exigencies. CRTs therefore not only limit the loss of power due to herd effects but also can give a better estimate of the true effect of a vaccine on different communities. Nevertheless, CRT designs have raised concerns about their ability to maintain blinding and the difficulty in assessing their external validity [8]. To maintain blinding and control for contamination, Borm et al. [9] proposed a double randomisation procedure (pseudo-cluster randomised trials (pCRTs)) which can also be used to differentiate direct from indirect effects and total from overall effects in vaccine trials (Fig. 1). Choosing CRTs or pCRTs instead of individual randomised trials (iRCT) requires careful considerations as they are often more complex to plan (ethical approval, personals’ training, blinding from allocation, assuring baseline balance for recruitment rate and for confounders, restraining drop-outs, etc.), they usually require larger

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Fig. 1. Two level randomisation: cluster level (A) and individual level (B), in pseudo-cluster randomised trials.

sample size and larger financial resources, and remain vulnerable to contamination at a cluster level (migration between clusters). CRTs or pCRTs for vaccine trials were therefore the privileged choice of design for those who also whished to estimate herd effects of vaccine or who wanted to control for ecological factors at a community level. However, recent development of geographic information systems (GIS) has made it possible to estimate these effects in IRTs [6,10] making the choice of the design for vaccine trials even more difficult. Methodological guidelines have been given for choosing designs in clinical trials in general practice [11–13], but not for vaccine trials. This article proposes an approach to help choose the most appropriate design for large-scale vaccine trials (phase III or phase IV). 2. Decisional flow chart From the literature, six factors have been identified which ought to be examined when choosing cluster randomisation trials for vaccines. These are: importance of indirect effects (herd immunity or herd protection), ability to delimit epidemiological units, homogeneity of transmission probability across sub-populations, population’s acceptability of randomisation, availability of logistical resources, and estimated sample size. We have constructed a decisional flow-chart (Fig. 2) which can be used to plan the most appropriate design for phase III or IV vaccine clinical trials. A step by step model is proposed. Each point to consider is argued in the following sections. 2.1. Transmission probability In field trials, the effect of a vaccine can be observable under two conditions; individuals have either received the vaccine or a control, and they were exposed to the infectious agent. The exposure to the prophylactic intervention is easily controlled by allocating vac-

cines or controls randomly, whereas the exposure to the disease is assigned by nature. The transmission probability therefore not only depends on the vicinal status, but also of many other factors [14] such as: (a) the concentration of infected people, (b) the geographical distribution of disease’s vectors, if such vectors exists, (c) efficiency of pathogen transmission within a community; contamination can necessitate more than one exposure to the infectious agent and also depends on the probability of repeated contacts, (d) the infectious history of the population, (e) the immunity status of the population prior to vaccination, (f) the proportion of infected individuals which develop the disease, (g) the possibility of multiple episodes in an individual, (h) individual host factors such as genetic susceptibility, underlying medical conditions, age, (i) geographic and spatial conditions which can either favour or disfavour transmission, (j) social or behavioural dynamics which might influence either the transmission or the development of the disease (e.g. breastfeeding habits), and (k) epidemics of other infections which could favour the development of the disease (e.g. HIV). Furthermore, the effect of the vaccine can be modified: (l) through different age groups, (m) through different areas depending of the distribution of serotypes, (n) through time with the development of resistance and (o) through time by increased herd protection. Individual randomisation in IRT assures that transmission probability is well balanced between both arms and therefore gives a precise estimate of the vaccine’s “average” effect over the entire population. However, transmission probability is not necessarily homogenous and could cluster geographically. If the heterogeneity is important enough, the overall estimate could overestimate the true efficiency for certain areas and underestimate it for others. This could have important implications for public health decisions as targeting intervention programs on specific areas could then become more cost-effective. Adjusting for geographical heterogeneity can partially be achieved by using cluster randomisation if epidemiological units can be well delimited. This would nevertheless require balancing all these factors between clusters which is more difficult

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Fig. 2. Decision flow chart for choosing the randomisation procedure in vaccine trials.

to achieve than between individuals in a IRT. Stratifying, matching or restricted randomisation should be considered to improve balance between groups in CRTs. A simpler solution is to control for disparities of transmission probability by using GIS. This makes it possible to secondarily spatially estimate the vaccine’s effect over a large geographical area. Effects of time can unfortunately only be evaluated by repeating observations through time independently of the design. This could be important as indirect effects of vaccination are time dependant. Geographical disparities are therefore worth considering in large-scale trials for secondary analysis where vaccine policies could differ between areas.

ulation, expected vaccine efficacy, and time. This seems important as herd effects commonly take some time to have an effect. Herd effects are much more important than homogeneity of transmission probability in deciding on a trial design. Not adapting the design to herd effect remains possible as long as the transmission probability in the control group is not expected to be reduced to a point where the study estimate of direct effects does not clinically make sense anymore. If there is a clear interest in differentiating direct from indirect, or total from overall effects, pCRT or individual randomisation with GIS designs [6] should be adopted. 2.3. Epidemiological units

2.2. Herd effects Herd effects can attenuate the apparent effectiveness of vaccination in an IRT [3]. Those who are to decide of wide vaccine programs might require measures of vaccine’s indirect effects to set their goal of vicinal coverage. This could require adapting the trial design to make it possible to measure indirect effects [15]. To evaluate contamination through herd effects, two protective mechanism have to be considered: herd immunisation and herd protection [16]. Herd immunisation is observed when shedding from the attenuated bacteria or viruses (live vaccine) is spread and brought to contact with those that were not immunised inducing an immune response. Herd protection is observed when the unimmunised are less at risk of becoming infected as their chance of meeting the infectious agent decreases. Herd protection will therefore not appear if the infectious agent is not transmissible from human to human (or an intermediate vector) [6], or if the ratio of those that are immunised by the vaccine compared to those that remain potentially contagious remains small enough not to have any effect on the population’s transmission probability. Factors that may influence the importance of herd effects therefore include the proportion vaccinated in the study population, characteristics of the infectious agent (e.g. reproduction number), contact patterns within the pop-

The most important assumption for CRTs is that clusters remain independent one from another. Therefore, epidemiological units have to be well delimitated to prevent contamination between clusters. Choosing the type of clusters (housing, villages, communities, administration units, geographic zones, health units, workplaces, schools, etc.), and relying on geographically separated areas, buffer zones or a “fried egg” designs are essential in preventing contamination and assuming homogeneity in each cluster. Possible contamination between individuals form different clusters has to be documented (migration). The primary analyses should be intention to treat—describing migration etc and a per protocol analysis adjusting for migration should be secondary, since it essentially ignores the randomisation. If more than one fifth of the population is estimated to enter in contact with individuals from another cluster, adjusting for this contamination will become difficult. Secondly, if the study design wishes to infer results at an individual level and not at a community level, it is important to either plan a cluster randomisation with epidemiological units which have a homogenous infection probability or to collect individual data to take heterogeneity into consideration. Using a GIS can make this possible in both cluster and individual randomised designs. Coupling the GIS system to measures of migration in an individ-

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ual randomised design could also be a solution if contamination through migration makes it unreasonable to plan a cluster randomised trial. GIS not only makes it possible to measure indirect effects but also to observe the effects of epidemiological factors on the vaccine [7,17]. 2.4. Populations’ acceptability Lack of equipoise and resentfulness towards clinical trials can often be problematic in vaccine trials. Vaccines are often known to be effective in other settings where they are already administered in large-scale vaccination programs. For those that have not yet been tested on a large population, phase II or phase IIb trials usually support the potential benefit of vaccination. Both these reasons can make it difficult for those receiving placebo to feel advantaged. Therefore, some communities do not accept to either receive a placebo or to be randomised. Cluster randomisation using two different vaccines for independent diseases can overcome this problem. 2.5. Logistical management Cluster randomisation can facilitate logistical site organisation for trials comparing two vaccines which require different handling procedures. It is therefore important to assess the availability of logistical resources making it possible to handle randomisation of individuals to two different treatment regiments on each site (vaccine and control). As vaccination often requires repeated contacts with the vaccine to be efficient, it is also important to make sure the same intervention is given to each participant. 2.6. Sample size calculations Cluster randomisation is expected to require a larger sample size than individual randomisation. However, if contamination between individuals (herd effects) is important enough, the inflation factor could increase the sample size to a larger extend than if cluster randomisation was chosen. Calculating sample size for each design, taking both contamination and migration into consideration can help opt for the design which will require the smallest sample size [11]. Equations used to estimate sample size will depend on the nature of the measure of effect. The appropriate methods have been widely discussed by Haynes and Benett [18] and by Campbell et al. [19]. For these calculations, it is important to have appropriate estimates of the treatment effect (including herd effects), the expected number of participants in each cluster, and the within cluster correlation [20]. 3. Conclusion The major advantage of cluster randomisation over individual randomisation in vaccine trials was to take indirect effects into

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consideration. Since the development of GIS, measuring indirect effect is also possible in IRTs. Ethical or logistic problems are therefore the major reasons to opt for a cluster randomised trial. In rare situations where herd effects are expected to be very important, cluster randomisation instead of individual randomisation can be advantageous as this design could require a smaller sample size [11]. Choosing the most advantageous design therefore requires having many prior indicators. These are estimates of the population’s transmission probability, the vaccine’s herd effects, the appropriateness of epidemiological units, their number and size, and finally the population’s migration and cultural habits which could affect the trial. The choice of the design can then be straightforward. References [1] Anderson RM, May RM. Immunisation and herd immunity. Lancet 1990;335(8690 (March)):641–5. [2] Fine PE. Herd immunity: history, theory, practice. Epidemiol Rev 1993;15(2):265–302. [3] Halloran ME, Haber M, Longini Jr IM, Struchiner CJ. Direct and indirect effects in vaccine efficacy and effectiveness. Am J Epidemiol 1991;133(4 (February)):323–31. [4] Farrington CP, Miller E. Vaccine trials. Mol Biotechnol 2001;17(1 (January)):43–58. [5] Slymen DJ, Hovell MF. Cluster versus individual randomization in adolescent tobacco and alcohol studies: illustrations for design decisions. Int J Epidemiol 1997;26(4 (August)):765–71. [6] Ali M, Clemens JD. Ecological aspects in vaccine trials. Expert Rev Vaccines 2008;7(3 (April)):279–81. [7] Emch M, Ali M, Acosta C, Yunus M, Sack DA, Clemens JD. Efficacy calculation in randomized trials: global or local measures? Health Place 2007;13(1 (March)):238–48. [8] Eldridge S, Ashby D, Bennett C, Wakelin M, Feder G. Internal and external validity of cluster randomised trials: systematic review of recent trials. BMJ 2008;336(7649 (April)):876–80. [9] Borm GF, Melis RJ, Teerenstra S, Peer PG. Pseudo cluster randomization: a treatment allocation method to minimize contamination and selection bias. Stat Med 2005;24(23 (December)):3535–47. [10] Deen JL, Clemens JD. Issues in the design and implementation of vaccine trials in less developed countries. Nat Rev Drug Discov 2006;5(11 (November)):932–40. [11] Teerenstra S, Melis RJ, Peer PG, Borm GF. Pseudo cluster randomization dealt with selection bias and contamination in clinical trials. J Clin Epidemiol 2006;59(4 (April)):381–6. [12] Murphy AW, Esterman A, Pilotto LS. Cluster randomized controlled trials in primary care: an introduction. Eur J Gen Pract 2006;12(2):70–3. [13] Elley CR, Chondros P, Kerse NM. Randomised trials—cluster versus individual randomisation. Primary Care Alliance for Clinical Trials (PACT) network. Aust Fam Physician 2004;33(9 (September)):759–63. [14] Struchiner CJ, Halloran ME. Randomization and baseline transmission in vaccine field trials. Epidemiol Infect 2007;135(2 (February)):181–94. [15] Halloran ME, Struchiner CJ. Study designs for dependent happenings. Epidemiology 1991;2(5 (September)):331–8. [16] Paul Y. Herd immunity and herd protection. Vaccine 2004;22(3–4):301–2. [17] Ali M, Emch M, von Seidlein L, Yunus M, Sack DA, Rao M, et al. Herd immunity conferred by killed oral cholera vaccines in Bangladesh: a reanalysis. Lancet 2005;366(9479 (July)):44–9. [18] Hayes RJ, Bennett S. Simple sample size calculation for cluster-randomized trials. Int J Epidemiol 1999;28(2 (April)):319–26. [19] Campbell MK, Thomson S, Ramsay CR, MacLennan GS, Grimshaw JM. Sample size calculator for cluster randomized trials. Comput Biol Med 2004;34(2 (March)):113–25. [20] Campbell MK, Fayers PM, Grimshaw JM. Determinants of the intracluster correlation coefficient in cluster randomized trials: the case of implementation research. Clin Trials 2005;2(2 (April)):99–107.