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Biomedical subjects

Trevor C Bailey

Publications and source records attributed to Trevor C Bailey.

2 recordsLinked to original sources

Random-effects models in investigating the effect of vitamin A in childhood diarrhea.

PURPOSE: By adopting more appropriate and powerful statistical methods that fully exploit longitudinal structure, we re-analyze and extend previously published results from a large community trial to investigate the effect of vitamin A supplementation on the prevalence and severity of diarrhea in young children. METHODS: Generalized linear mixed models were used to allow for repeated measures in a reanalysis of a double-blind, randomized, placebo-controlled community trial conducted in a cohort of children in northeastern Brazil during 1 year. The response variable was weekly number of days with diarrhea for each child, and Markov Chain Monte Carlo methods were used to estimate model parameters. RESULTS AND CONCLUSIONS: Random effects suitably accounted for the underlying heterogeneity between and within children, and our longitudinal analysis shows a significant beneficial effect of vitamin A supplementation that was inconclusive in previously reported simple summary analyses of these data. Risk for diarrhea infection was estimated to be 1.57 times greater for a child administered a placebo as opposed to vitamin A (95% credible interval, 1.17-2.12). Additionally, we identified previously unreported temporal effects in these data, showing a decreasing daily probability of diarrhea for both groups during the trial and treatment-time interaction.

Bayes Theorem↗

Biclustering models for structured microarray data.

Microarrays have become a standard tool for investigating gene function and more complex microarray experiments are increasingly being conducted. For example, an experiment may involve samples from several groups or may investigate changes in gene expression over time for several subjects, leading to large three-way data sets. In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. This model-based method lends itself to the incorporation of any additional structure such as external grouping or repeated measures. We describe how the extended models may be fitted and illustrate their use on real data.

Algorithms↗