TY - JOUR

T1 - Combining data from multiple spatially-referenced prevalence surveys using generalized linear geostatistical models

AU - Giorgi, Emanuele

AU - Sesay, Sanie S. S.

AU - Terlouw, Dianne J.

AU - Diggle, Peter John

PY - 2015/2

Y1 - 2015/2

N2 - Data from multiple prevalence surveys can provide information on common parameters of interest, which can therefore be estimated more precisely in a joint analysis than by separate analyses of the data from each survey. However, fitting a single model to the combined data from multiple surveys is inadvisable without testing the implicit assumption that all of the surveys are directed at the same inferential target. We propose a multivariate generalized linear geostatistical model that accommodates two sources of heterogeneity across surveys to correct for spatially structured bias in non-randomized surveys and to allow for temporal variation in the underlying prevalence surface between consecutive survey periods. We describe a Monte Carlo maximum likelihood procedure for parameter estimation and show through simulation experiments how accounting for the different sources of heterogeneity among surveys in a joint model leads to more precise inferences. We describe an application to multiple surveys of the prevalence of malaria conducted in Chikhwawa District, Southern Malawi, and discuss how this approach could inform hybrid sampling strategies that combine data from randomized and non-randomized surveys to make the most efficient use of all available data.

AB - Data from multiple prevalence surveys can provide information on common parameters of interest, which can therefore be estimated more precisely in a joint analysis than by separate analyses of the data from each survey. However, fitting a single model to the combined data from multiple surveys is inadvisable without testing the implicit assumption that all of the surveys are directed at the same inferential target. We propose a multivariate generalized linear geostatistical model that accommodates two sources of heterogeneity across surveys to correct for spatially structured bias in non-randomized surveys and to allow for temporal variation in the underlying prevalence surface between consecutive survey periods. We describe a Monte Carlo maximum likelihood procedure for parameter estimation and show through simulation experiments how accounting for the different sources of heterogeneity among surveys in a joint model leads to more precise inferences. We describe an application to multiple surveys of the prevalence of malaria conducted in Chikhwawa District, Southern Malawi, and discuss how this approach could inform hybrid sampling strategies that combine data from randomized and non-randomized surveys to make the most efficient use of all available data.

KW - Convenience sampling

KW - Generalized linear geostatistical models

KW - Malariamapping

KW - Monte Carlo maximum likelihood

KW - Multiple surveys

KW - Spatiotemporal models

U2 - 10.1111/rssa.12069

DO - 10.1111/rssa.12069

M3 - Journal article

VL - 178

SP - 445

EP - 464

JO - Journal of the Royal Statistical Society: Series A Statistics in Society

JF - Journal of the Royal Statistical Society: Series A Statistics in Society

SN - 0964-1998

IS - 2

ER -