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Full-Text Articles in Biostatistics
On Variable Selections In High-Dimensional Incomplete Data, Tao Sun
On Variable Selections In High-Dimensional Incomplete Data, Tao Sun
Major Papers
Modern Statistics has entered the era of Big Data, wherein data sets are too large, high-dimensional, incomplete and complex for most classical statistical methods. This analysis of Big data firstly focuses on missing data. We compare different multiple imputation methods. Combining the characteristics of medical high-throughput experiments, we compared multivariate imputation by chained equations (MICE), missing forest (missForest), as well as self-training selection (STS) methods. A phenotypic data set of common lung disease was assessed. Moreover, in terms of improving the interpretability and predictability of the model, variable selection plays a pivotal role in the following analysis. Taking the Lasso-Poisson …
Accurate Confidence Intervals For Risk Difference In Meta-Analysis With Rare Events, Tao Jiang, Baixin Cao, Guogen Shan
Accurate Confidence Intervals For Risk Difference In Meta-Analysis With Rare Events, Tao Jiang, Baixin Cao, Guogen Shan
Environmental & Occupational Health Faculty Publications
Background: Meta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies. When the outcome is binary and it is rare (e.g., safety data in clinical trials), the traditionally used methods may have unsatisfactory performance. Methods: We propose using importance sampling to compute confidence intervals for risk difference in meta-analysis with rare events. The proposed intervals are not exact, but they often have the coverage probabilities close to the nominal level. We compare the proposed accurate intervals with the existing intervals from the fixed- or random-effects models and the interval by Tian et al. (2009). Results: We …