Analysing disease incidence data from designed experiments by generalized linear mixed models

As a result of aggregation or clustering of sampling units, disease incidence data from designed experiments frequently show overdispersion relative to the binomial distribution. This paper discusses generalized linear mixed models (GLMM) suitable for analysing overdispersed disease incidence data. The methods are exemplified using data from a randomized complete block experiment on the incidence of downy mildew ( Plasmopara viticola ) of grape ( Vitis lambrusca ). Hints are given regarding implementation of the methods using the %GLIMMIX macro for the SAS system.