Mendelian randomization (MR) employs genetic variations to infer causal effects of modifiable exposures on various outcomes, with two-sample MR using GWAS summary statistics from separate studies for exposure and outcome traits. However, challenges …
The test-negative design has become popular for evaluating the effectiveness of post-licensure vaccines using observational data. In addition to its logistical convenience on data collection, the design is also believed to control for the …
Understanding the causal pathogenic mechanisms of diseases is crucial in clinical research. When randomized controlled experiments are not available, Mendelian Randomization (MR) offers an alternative, leveraging genetic mutations as a natural …
Combining dependent p-values to evaluate the global null hypothesis presents a longstanding challenge in statistical inference, particularly when aggregating results from diverse methods to boost signal detection. P-value combination tests using …
Cell type deconvolution is a computational approach to infer proportions of individual cell types from bulk transcriptomics data. Though many new methods have been developed for cell type deconvolution, most of them only provide point estimation of …