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As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic(More)
Two-level data with hierarchical structure and mixed continuous and polytomous data are very common in biomedical research. In this article, we propose a maximum likelihood approach for analyzing a latent variable model with these data. The maximum likelihood estimates are obtained by a Monte Carlo EM algorithm that involves the Gibbs sampler for(More)
A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both(More)
OBJECTIVE To assess the epidemiological evidence for an increase in the risk of lung cancer resulting from exposure to environmental tobacco smoke. DESIGN Reanalysis of 37 published epidemiological studies previously included in a meta-analysis allowing for the possibility of publication bias. MAIN OUTCOME MEASURE Relative risk of lung cancer among(More)
A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously , with the covariance structure modelled by a Gaussian process regression model and the mean structure modelled by a functional regression model. The model allows the inclusion of covariates in both(More)
Multi-arm trials meta-analysis is a methodology used in combining evidence based on a synthesis of different types of comparisons from all possible similar studies and to draw inferences about the effectiveness of multiple compared-treatments. Studies with statistically significant results are potentially more likely to be submitted and selected than(More)
There is no simple method of correcting for publication bias in systematic reviews. We suggest a sensitivity analysis in which different patterns of selection bias can be tested against the fit to the funnel plot. Publication bias leads to lower values, and greater uncertainty, in treatment effect estimates. Two examples are discussed. An appendix lists the(More)