Wei Meng Email and Phone Number

• Provide statistical support to development and clinical research projects.• Maintain expertise in state-of-the-art data manipulation and statistical analyses.• Perform quality control for statistical analyses by other biostatisticians to ensure analysis quality.• Interact with research personnel to ascertain needs and develop appropriate design and statistical solutions.• Conduct randomization and blinding of samples.• Develop data entry spreadsheets as needed.• Check raw datasets, generate analysis datasets and prepare summary tables, listings and figures (TLFs).• Troubleshoot and improve current statistical designs or existing statistical macros.• Review Statistical Analysis Plans (SAPs) and statistical reports for use by internal and external customers.

• Performed drug administration data management and survival analysis of a Phase II clinical trial by using SAS proc lifetest procedure. Experienced in learning CDISC standard.• Determined the association between vitamin C and diabetes mellitus by using SAS proc surveyfreq and proc surveylogistic procedure to perform weighted Chi-squared test, weighted logistic regression, cross-validation and model diagnostic. Data source: NHANES 2005–2006 Survey Data.• Determined the association between smoking status and steroid hormones levels of non-pregnant women using Kruskall-Wallis tests, BH-FDR adjustment of p-value, generalized linear model and nonparametric ANCOVA by R.• Compared Kaplan-Meier survival curves of progression-free survival time and overall survival time by using SAS proc lifetest procedure. Fitted Cox proportional hazards model to explore the relationship between survival time of breast cancer patients and potential factors by using SAS proc phreg procedure.• Wrote complete reports to specify analysis process and results of each project for supervisory professor in supporting decision making.• Worked on projects independently and coordinated with supervisory professor on a one-on-one basis.

• Built a shiny app, a web application frame for R to turn the statistical analysis into interactive web applications• Showed the results of linear regression model with outliers and without outliers• Imputed missing values by using MICE package in R• Visualized the degree of the correlation between different variables by using gplots package in R

• Discovered potential biomarkers of Alzheimer’s disease by using randomForest package, rfUtilities package and party package in R to perform random forest predictive model• Conducted multivariate linear regression model by using SAS proc glmselect, proc reg and proc univariate procedure to explore whether smoking, gestional age, sex, race, maternal education, etc are factors that predict birthweight• Wrote a complete report including tables and figures to record the process and results of each project to supervisor in supporting decision making• Normalized RNA-seq counts data and identified differential expressed genes (DEGs) between patients and healthy controls by using edgeR package in R• Completed gene annotation using hgu133plus2.db package in R• Performed enrichment analysis on the significant genes using DAVID software and pathway analysis using GSEA software• Found significantly different expressed genes between cancer group and normal group by using limma package in R. Data type: Microarray data and miRNA data