Summary

A motivated SAS programmer and statistical analyst with more than three years of experience in public health and clinical trial data analysis. Advanced knowledge of statistical methods routinely used in public health and clinical trials data analysis. Excellent hands-on experience including SAS/BASE, SAS/STAT, and SAS/ODS, SAS/SQL, SAS/MACRO, and SAS/GRAPH, R and STATA.

EXPERIENCE

Biostatistician and Data control specialist

Confidential

• Developed and maintained databases using Microsoft Access.
• Developed SAS programs for data cleaning and validation.
• Extracted data using SAS/ACCESS, SAS/SQL procedure and created SAS data sets.
• Designed analytic data set specifications, manipulated, merged and restructured analytical data sets from large-scale data in SAS.
• Created complex and reusable MACROs and extensively used existing MACROs.
• Generated scientific tables, listings and graphs using SAS for journal papers and presentations.
• Conducted data analysis on obesity, diabetes, cardiovascular disease study using statistical techniques such as regression models, analysis of variance, covariance, survival analysis, survey data analysis, mixed model, generalized estimating equation approach GEE , categorical and longitudinal data analysis, etc.
• MACRO study
• Set up Access database for MACRO study, maintenance database.
• Data quality control. PROC COMPARE was used to check double entry problems, PROC SORT, SET with FIRST.SORT VAR and LAST.SORT VAR were used to check duplicate observations, PROC MEANS, PROC FREQ, PROC FORMAT with PROC TABULATE and PROC SQL were used to check missing values, and some logical problems.
• Collaborated with clinical research coordinator to ensure correct data entry.
• A random-effects linear model that was fitted to all continuous response variable.
• Generalized estimating equations under the logistic regression model for correlated longitudinal binary outcomes implemented in the GENMOD procedure in SAS.
• Displayed data graphically, explained statistical results to medical researchers.

Bone Density and Protein from MESA

• Checked missing value and combined datasets.
• Created general report such as mean, STD, percentages.
• Performed trend test.
• ANCOVA and robust regression was used to assess the relationship between bone mineral density and animal protein, vegetable protein and total protein in different gender, race.

EDAIC study

• Imported data from various sources in varying formats to create SAS data sets.
• Combined and restructured datasets according to the requirement of analysis.
• Generated summary report with PROC MEANS, PROC FREQ, PROC TTEST, and PROC GLM.
• Performed logistic regression to get the odds ratio PROC LOGISTIC .
• Quantile regression was implemented to the skewed continuous outcomes.
• Geometric mean was calculated for skewed continuous variables.

Low-carbohydrate Dietary Pattern and Mortality from NHANES

• Understood complex sampling survey design of NHANES.
• Read data from NHANES datasets and create STATA datasets for the analysis.
• Sampling survey analysis is performed by STATA.
• Descriptive analysis for continuous and categorical variables.
• Cox proportional-hazard models was implemented to get relative risk in STATA

Relationship between the occurrence of lymphoma and maintenance on thiopurine

medications

• Imported data into SAS
• Manipulated SAS data set.
• Cox proportional-hazard models with time-variant dependent variable.
• Generated cumulative hazard plot.

Instructor for SAS and STATA programming

Confidential

SAS:

• Read data into SAS by data steps or import wizard from text file, CS file, Excel file or ACCEESS database.
• Combine multiple data sets into one data set by merging or concatenating. Sort, split, select subsets of data and compute new variables.
• Compute summary measures statistics mean, standard deviation, standard errors proportions, frequency distributions and graphical displays histograms, bar chart, box plots, scatterplots using a variety of data sets.
• Analyze continuous data using simple linear regression and correlation and the one-way analysis of variance. Analyze categorical data using proportion test, correlation test, trend test and logistic regression.
• Apply MACROs to make SAS program more efficient.
• Instruction to PROC SQL, such combine data vertically or horizontally, generate table.
• Instruction to ODS OUTPUT to make output more usable.
• STATA:
• Apply procedures to explore data and import data and modify data
• Apply procedures to append, merge, collapse data.
• Construct tables, summary statistics.
• Analyze survey data and interpret results.

Research associate

Confidential

• Designed research experiment design and Tracked experiments.
• Collected data and manipulated data in database.
• Conducted data analysis in SAS and EXCEL.
• Generated scientific tables and graphs for research presentation and publications.

Teaching assistant

Confidential

• Instructed undergraduate students for descriptive statistics and inferential statistical methods.
• Instructed students to program in SAS for basic statistical analysis such as confidence interval estimation, hypothesis testing for population means and proportions, one-way analysis of variance, simple linear regression and correlation, chi-square, analysis of categorical data.
• Tutored students and graded homework.