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Besides large efforts regarding field work, provision of valid databases requires statistical and informational infrastructure to enable long-term access to longitudinal data sets on height, weight and related issues.To foster use of longitudinal data sets within the scientific community, provision of valid databases has to address data-protection regulations. It is, therefore, of major importance to hinder identifiability of individuals from publicly available databases. To reach this goal, one possible strategy is to provide a synthetic database to the public allowing for pretesting strategies for data analysis. The synthetic databases can be established using multiple imputation tools. Given the approval of the strategy, verification is based on the original data.Multiple imputation by chained equations is illustrated to facilitate provision of synthetic databases as it allows for capturing a wide range of statistical interdependencies. Also missing values, typically occurring within longitudinal databases for reasons of item non-response, can be addressed via multiple imputation when providing databases.The provision of synthetic databases using multiple imputation techniques is one possible strategy to ensure data protection, increase visibility of longitudinal databases and enhance the analytical potential.

Given the availability of large longitudinal data sets on human height and weight, different modelling approaches are at hand to access quantities of interest relating to important diagnostic aims. Statistical modelling frameworks for longitudinal data on human height and weight have to consider the issues of individual heterogeneity and time dependence to provide an accurate statistical characterisation. Further, missing values inevitably occurring within longitudinal data sets have to be addressed adequately to allow for valid inference. The Bayesian framework is illustrated to facilitate stringent comparison of available non-nested model frameworks addressing these issues using simulated and empirical data sets. Comparing random-effects and fixed-effects modelling approaches with the Preece–Baines (PB) model reveals that, for simulated data, the Bayesian approach towards model comparison is effective in discriminating between different model specifications. With regard to analysis of 14 longitudinal data sets, the implicit trade-off between model fit, that is, description of the data, and a parsimonious parameterisation favouring prediction is often best addressed via the PB model. The Bayesian approach is illustrated to allow for effective comparison in case model specifications for longitudinal data are not linked directly via parametric restrictions.

BACKGROUND/OBJECTIVES Recent evidence suggests clustering of human body height. We want to assess the consequences of connectedness in a spatial network on height clustering in an artificial society. SUBJECTS/METHODS We used an agent-based computer modelling technique (Monte Carlo simulation) and compared simulated height in a spatial network with characteristics of the observed geographic height distribution of three historic cohorts of Swiss military conscripts (conscripted in 1884-1891; 1908-1910; and 2004-2009). RESULTS Conscript height shows several characteristic features: (1) height distributions are overdispersed. (2) Conscripts from districts with direct inter-district road connections tend to be similar in height. (3) Clusters of tall and clusters of short stature districts vary over time. Autocorrelations in height between late 19th and early 21st century districts are low. (4) Mean district height depends on the number of connecting roads and on the number of conscripts per district. Using Monte Carlo simulation, we were able to generate these natural characteristics in an artificial society. Already 5% height information from directly connected districts is sufficient to simulate the characteristics of natural height distribution. Very similar observations in regular rectangular networks indicate that the characteristics of Swiss conscript height distributions do not so much result from the particular Swiss geography but rather appear to be general features of spatial networks. CONCLUSIONS Spatial connectedness can affect height clustering in an artificial society, similar to that seen in natural cohorts of military conscripts, and strengthen the concept of connectedness being involved in the regulation of human height.