18. Non-Independent Data

Many infectious disease experiments result in non-independent data because of spatial autocorrelation across fields (such as discussed in Chap. 13), repeated measures on experimental animals (such as the in-host Plasmodium data discussed in Sect. 8.9), or other sources of correlated experimental responses among experimental units (such as the possibility of correlated infection fates among the rabbit littermates discussed in Sect. 5.2). Statistical methods that assume independence of observations are not strictly valid and/or fully effective on such data (e.g., Legendre, 1993; Keitt et al., 2002). Mixed-effects models and generalized linear mix-effects models (GLMMs) have been/are being developed to optimize the analysis of such data (Pinheiro & Bates, 2006; Bolker et al., 2009; Bates et al., 2015).