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Unobserved heterogeneity will be explored using random coefficients and scale-adjusted latent class modelling.
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Models with potential moderators were explored using random effects meta-regression.
The degree of variation in prevalence between practices and geographical areas will be explored using two level random-effects logistic regression models (with practice/site as a random effect and area as a fixed effect).
Factors associated with psychiatric diagnoses will be explored using univariate and multivariate random-effects generalised linear models This analysis will use repeated measures of the psychiatric diagnosis variables at 1, 2, 4 and 12 months, with participant as a level 2 unit (cluster) and measurement occasion as a level 1 unit, to allow for correlations of measurements within patients.
In a separate analysis, the effect of changes in population structure in respect of the mother's characteristics (i.e. exposure of interest) on percentage of skilled birth attendants over the period was explored using the random-effects generalized least squares (GLS) regression.
The parameter space was explored using a Gaussian random walk.
Rates of complications are reported and statistical pooling of proportion estimates was explored using fixed and random effect models within StatsDirect © Version 3.
The tree space was explored using Markov Chain Monte Carlo (MCMC) analyses with random starting trees, five simultaneous, sequentially heated independent chains sampled every 500 trees during five million generations.
Finally, the graph space was explored using the greedy hill-climbing algorithm with random restarts to get the most likely graph G for lncRNAs and protein-coding genes.
Risk factors for infection were explored using multilevel multivariable logistic regression with farm as a random effect.
The uncertainty in measurement errors and the effects of random variation of microstructural features on the small-crack growth were explored using Monte Carlo (MC) simulations.
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