Design and analysis issues in cluster-randomized trials of interventions against infectious diseases.
This paper discusses the application of the cluster-randomized trial (CRT) design to evaluate the effectiveness of interventions against infectious diseases. In addition to the usual rationale for this design, there are a number of other advantages that are peculiar to the study of infectious diseases. In particular, CRTs are able to measure the overall effect of an intervention at the population level, capturing both the direct effect of an intervention on an individual's susceptibility to infection, and also the indirect effects due to changes in risks of transmission to other individuals, or to the mass effect or 'herd immunity' resulting from intervening in a large proportion of the population. We briefly review published CRTs of interventions against infectious diseases, most of which have been conducted in the developing countries where such diseases predominate. The focus is on trials in which communities or other large groupings are randomized, and in which impacts on infectious disease incidence or mortality are assessed. We then discuss three issues that are of special relevance to CRTs of infectious diseases. First, issues relating to the definition and size of clusters; secondly, the role of matching or stratification, and the choice of matching factors; and thirdly, the definition of direct and indirect effects of intervention, and methods of assessing these components in a CRT. We conclude by outlining some areas for future research.
Hayes
R J
RJ
Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, UK. richard.hayes@lshtm.ac.uk
Alexander
N D
ND
Bennett
S
S
Cousens
S N
SN
D001699:Biometry; D016000:Cluster Analysis; D003140:Communicable Disease Control / Q000379:methods / Q000706:statistics & numerical data*; D003141:Communicable Diseases / Q000628:therapy* / Q000635:transmission; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D018401:Sample Size