Papers 2018

Anonymisation with noise applications_

Individual-level data require protection from unauthorised access. Effective anonymisation techniques should minimise the probability of re-identification while allowing informative data analysis. ‘Probabilistic anonymisation’ alters the data by addition of random noise. In this paper, we describe the implementation of one such technique and demonstrate its application to analysis of asthma-related data from the ALSPAC cohort study.

Using weights in a Bayesian modelling framework

Pseudonymisation at source

Level 1 variation in repeated measures growth models

This presents a flexible model for repeated measures longitudinal growth data within individuals that allows trends
over time to incorporate individual-specific random effects. These may reflect the timing of growth events and
characterise within-individual variability which can be modelled as a function of age.
Subjects and methods: A Bayesian model is developed that includes random effects for the mean growth function, an
individual age-alignment random effect and random effects for the within-individual variance function. This model is
applied to data on boys’ heights from the Edinburgh longitudinal growth study and to repeated weight measurements of a
sample of pregnant women in the Avon Longitudinal Study of Parents and Children cohort.
Results: The mean age at which the growth curves for individual boys are aligned is 11.4 years, corresponding
to the mean ‘take off’ age for pubertal growth. The within-individual variance (standard deviation) is found to
decrease from 0.24 cm2 (0.50 cm) at 9 years for the ‘average’ boy to 0.07 cm2 (0.25 cm) at 16 years. Change
in weight during pregnancy can be characterised by regression splines with random effects that include a large
woman-specific random effect for the within-individual variation, which is also correlated with overall weight and
weight gain.