Design-based analysis of surveys: a bovine herpesvirus 1 case study

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Abstract
This paper critically assesses the design implications for the analysis of surveys of infections. It indicates the danger of not accounting for the study design in the statistical investigation of risk factors. A stratified design often implies an increased precision while clustering of infection results in a decreased precision. Through pseudo-likelihood estimation and linearisation of the variance estimator, the design effects can be taken into account in the analysis. The intra-cluster-correlation can be investigated through a logistic random effect model and a generalised estimating equation (GEE), allowing the investigation of the extent of spread of infections in a herd (cluster). The advantage of using adaptive Gaussian quadrature in a logistic random effect model is discussed. Applicable software is briefly reviewed. The methods are illustrated with data from a bovine herpesvirus 1 (BHV-1) serosurvey of Belgian cattle.

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