Abdullah Al Masud has over 10 years of experience in biostatistics and clinical research, currently working as a senior research statistician at AbbVie. He has extensive experience analyzing oncology clinical trial data and developing statistical analysis plans. His qualifications include published research, programming skills in SAS and R, and a PhD in Biostatistics from Indiana University.

1. Abdullah Al Masud
900 Mark Lane#302,
Wheeling, Illinois 60090
317-712-1627 (Cell)
tomal22@hotmail.com
abdullah.masud@abbvie.com
Overall Career Goal
To be a proactive member and active contribution in an organization or a novel drug
development in statistics, biostatistics, bioinformatics, and epidemiology
Summary of Qualifications
• Working experience in oncology study, interim analysis, survival data analysis, lon-
gitudinal data analysis, study design and sample size calculation, linear and non-
linear modeling, predictive modeling and variable selection, developing algorithms
and statistical programming, simulation study and bootstrap method, clinical trials,
Bayesian analysis and hierarchical modeling, nonparametric analysis, meta analysis,
sensitivity analysis, cluster analysis, machine learning, data mining, data visualiza-
tion, genomics data analysis and sequencing, and economic data analysis
• Novel ideas and successful delivery within project timelines
• Experience defining standard analysis practices and implementing clinical data anal-
ysis workflows
• Developing protocol and statistical analysis plan (SAP) for oncology clinical trial
• Contributing to Case Report Form (CRF) development and reviewing edit check
specification
• Leverage collaborations with internal clinical development teams gaining novel in-
sights into disease indication and response data to enhance oncology study pipeline
• Participation in collaborative research with manager and colleagues
Work Experience
AbbVie Inc January 2017 – Present
Senior Research Statistician Chicago, Illinois
1. Work as a lead clinical statistician for cancer study with Chronic Lymphocytic
Leukemia (CLL) and Non-Hodgkin Lymphoma (NHL) disease indications
2. Provide lead statistical inputs on cancer study for designing, conducting, and report-
ing
3. Develop protocol and statistical analysis plan (SAP)
4. Support clinical team for conducting interim analyses and reviewing safety data
5. Create TLG template and derived analysis datasets specification, perform QC of
TLG
6. Support cross functional team to develop case report form (CRF) and electronic data
capture (EDC) system
7. Design necessary statistical edit check specification for EDC and SAS database
8. Collaborate with cross functional team members to provide statistical outputs for
clinical meetings and publications
9. Create mocked TLG for interim review and clinical data reporting to support pro-
gramming team
10. Use quantitative skills for analyzing safety lab data to guide a dose during the ramp-
up schedules
Research and Internship Experiences
Bristol-Myers Squibb May 2016 –August 2016
Biostatistician Intern Wallingford, Connecticut
1. Reviewed protocols and statistical analysis plans (SAP) for oncology clinical trials
2. Analyzed immuno-oncology study data
3. Supported the clinical and regulatory teams with providing statistical outputs
4. Introduced a novel endpoint of a randomized phase 3 trial
5. Evaluated the significant tests in the interim stage
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2. Abdullah Al Masud
6. Assessed a varieties of weighted log-rank tests for the analysis of time to treatment
failure
7. Conducted a sensitivity analysis of the study
8. Wrote R and SAS programming scripts for simulation and the lung cancer study data
Pfizer Inc May 2015 – August 2015
Statistical Intern New York, New York
1. Derived a novel statistical F-test for composite (multiple) endpoints of cardiovascular
therapeutic study in phase 3 trial
2. Developed and implemented non-parametric bootstrap technique for composite end-
points of cardiovascular therapeutic study in phase 3 trial
3. Assessed the new statistical tests using simulation studies and HIV-AIDS dataset
4. Wrote R programming codes and algorithms for clinical trial study with composite
endpoints
5. Reported the analysis document to managers for publication
Pinnacle Solutions Inc May 2014 – August 2014
Statistical Intern Indianapolis, Indiana
1. Analyzed and modeled repeated measurement dataset
2. Conducted cluster analysis and factor analysis on insurance claim data
3. Supported clients to conduct statistical analysis
4. Developed SAS macro codes for team
5. Reported and visualized statistical analysis to managers
Indiana University-School of Medicine August 2012 – December 2016
Department of Biostatistics Indianapolis, Indiana
Research Assistant
1. Survival Analysis And Observational Study:
• Collaborated with physicians and researchers at Regenstrief institute
• Analyzed the survival data and the hospital cost data from the electronic health
records (EHR)
• Utilized the Cox model for time-to-mortality analysis from a longitudinal study for
cardiovascular problem–Mitral regurgitation
• Analyzed correlated survival data for an oral examination study
• Developed SAS macro code and R code for analysis
• Reported and presented the analysis results to supervisors and collaborators
2. Model Selection And Specification in Survival Data with Long-Term Survivors:
• Analyzed the Cox model for survival or failure time data in SAS and R
• Applied predictive modeling technique such as LASSO and adaptive LASSO in sur-
vival models with long-term survivors
• Constructed a data-driven model building procedure to select the structure of cure
rate models or survival models with long-term survivors
• Developed model selection procedure to discover nonlinear variables in the mixture
models for recurrent survival events
• Derived expectation-maximization (EM) algorithm to analyze survival data
• Used B-spline function to approximate nonlinear functions in survival analysis
• Implemented and wrote R programming scripts for computation
Statistical Consult August 2009 –December 2014
3. Conducted observational study:
• Applied mixed effect model and linear regression model to air quality data
• Utilized piecewise spline technique in mixed effect model
• Analyzed missing data in an observational study
Utah State University August 2009 –May 2012
Graduate Assistant Logan, Utah
1. Taught undergraduate level Statistics and Mathematics courses
2. Conducted statistical analysis on genetics and financial data:
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3. Abdullah Al Masud
• Applied multiple hypothesis testing to control false discovery rate (FDR), and family-
wise error rate (FWER) in gene expression data
• Assessed statistical power of test for multiple testing using Monte Carlo simulation
• Applied time series regression analysis on stock returns and economic data
Skills
Statistical languages: SAS, SAS macro, SAS SQL, JMP, R, and WinBUGS
Programming languages: Python, and C++
Document preparation: LaTex, MS Office
Operating system: Linux, and Microsoft Windows
Education
PhD, Major in Biostatistics August 2012–December, 2016
(Minor in Epidemiology) Advisor: Dr. Wanzhu Tu
School of Medicine and Public Health, and Dr. Zhangsheng Yu
Indiana University, Indianapolis, Indiana
MS, Statistics and Financial Economics August 2009–July 2012
Utah State University, Logan, Utah
Bachelor of Science, Statistics Graduated 2008
University of Dhaka, Bangladesh, Dhaka
Theses
PhD Thesis
• Masud, A A (2016). Determination of The Composition of Failure Time Mod-
els with Long-Term Survivors. IUPUI ScholarWorks. https://doi.org/10.7912/
C2ZS36.
MS Thesis
• Masud, A A (2011).Controlling Error Rates with Multiple Positively-Dependent
Tests. All Graduate Reports and Creative Projects. Paper 30.
http://digitalcommons.usu.edu/gradreports/30.
• Masud, A A (2012).The Effect of Kurtosis on the Cross-Section of Stock Returns.
All Graduate Plan B and other Reports. Paper 180.
http://digitalcommons.usu.edu/gradreports/180.
Published Paper
• Khemka, A, Gradus-Pizlo, I, Kovacs, R, Tu, W, Hayden, R, Masud, A A , Eckert,
G, and Tierney, W (2017). Using Clinical Data Repositories to Assess the Clinical
and Financial Burden of Disease: The Example of Mitral Regurgitation. Journal of
Health and Medical Informatics, 8 (3): 266. doi: 10.4172/2157-7420.1000266
• Stevens, J R, Masud, A A, and Suyundikov, A. (2017). A Comparison of Multi-
ple Testing Adjustment Methods with Block-Correlation Positively-Dependent Tests.
PLoS ONE 12(4): e0176124. https://doi.org/10.1371/journal.pone.0176124
• Masud A A, Tu, Wanzhu, and Yu, Zhangsheng (2016). Variable Selection for
Mixture and Promotion Time Cure Rate Models. Statistical Methods in Medical
Research, 0(0):1–15.
http://journals.sagepub.com/doi/pdf/10.1177/0962280216677748
• Kwag, A, Masud, A A (2013). Modeling and Predicting Stock Returns: The Rule
of Parsimony. POSRI Business Economic Research Article, 13(2): 149–187.
• Chowdhury, Z, Leah, T L, Karen C C, Masud, A A, Alauddin, M, Hossain, M,
Zakaria, ABM, Hopke, P H (2012). Quantification of Indoor Air Pollution from Using
Cook Stoves and Estimating its Health Effects in Northwest Bangladesh. Aerosol and
Air Quality Research, 12: 463–475.
• Chowdhury, Z, Campanella, L, Gray, C., Masud, A A, Pennise, D.,Zhuzhang, X
(2012). Evaluation and Modeling of Indoor Air Pollution in Rural Households with
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4. Abdullah Al Masud
Multiple Stove Interventions in Yunnan, China. Atmospheric Environment, 67: 161–
169.
Working Paper
• Masud, A A,Yu, Z, and Tu, W. (2017). Variable Selection And Nonlinear Effect
Discovery in Partially Linear Mixture Cure Rate Models, submitted to Biostatistics
and Epidemiology.
• Masud, A A,Weerahandi, S, and Yu, C. (2018). Evaluating Treatment Efficacy by
Combining Multiple Measures in Clinical Trial Applications, submitted to Pharma-
ceutical Statistics.
• Gradus-Pizlo, I, Khemka, A, Kovacs, R, Tu, W, Hayden, R, Eckert, G, Masud, A A,
and Tierney, W (2018). Understanding the Health Burden of Moderate Functional
Mitral Regurgitation through Clinical Data Repositories, submitted to Journal of
General internal Medicine.
• Masud, A A, Tu, W, and Yu, Z (2016). Variable Selection in Semi-parametric
Linear Mixture Survival Models for correlated failure-time data, In Progress.
Presentation
• Masud, A A, Yu, Z., and Tu, W (2017). Variable Selection And Nonlinear Ef-
fect Discovery in Partially Linear Mixture Cure Rate Models. Oral Presentation at
International Chinese Statistical Association (ICSA) Midwest Chapter and North
Illinois Chapter Meeting; October 2017; Glenview, Illinois. http://midwest-icsa.
org/wp-content/uploads/2017/10
• Masud, A A, Yu, Z., and Tu, W (2017). Variable Selection And Nonlinear Effect
Discovery in Partially Linear Mixture Cure Rate Models. Poster Presentation at
Joint Statistical Meeting (JSM); August 2017; Baltimore, Maryland.
• Gradus-Pizlo, I, Khemka, A, Kovacs, R, Tu, W, Hayden, R, Masud, A A, Eckert,
G, and Tierney, W. Impact of Moderate Mitral Regurgitation on Patients: Mortality
May Be Underestimated-Analysis of Clinical Data Repositories. Poster Presenta-
tion at American College of Cardiology, 66th Annual Scientific Session; March 2017;
Washington, D.C.
• Khemka, A, Kovacs, R, Tu, W, Hayden, R, Eckert, G, Masud, A A, Tierney, W, and
Gradus-Pizlo, I. Understanding the Health Burden of Mitral Regurgitation through
Clinical Data Repositories. Paper Presented at The 20th Annual Scientific Meeting-
Heart Failure Society of America (HFSA), September 2016; Kissimmee, Florida.
Abstract retrieved from https://doi.org/10.1016/j.cardfail.2016.06.228
• Masud AA. Improved Finkelstein-Schoenfeld (FS) Test in Clinical Trial applica-
tions. Oral Presentation at Global Innovative Pharma Business (GIPB), Pfizer Inc;
August 2015; Manhattan, New York.
• Masud A A and Yu, Zhangsheng. Variable Selection for Mixture And Promo-
tion Time Cure Rate Models. Oral Presentation at International Chinese Statistical
Association (ICSA) Applied Statistics Symposium; June 2016; Atlanta, Georgia.
• Masud A A and Stevens, J R. A Comparison of Weighted P-Values And Multi-Stage
Analyses in Multiple Hypothesis Testing. Poster Presentation at Applied Statistics
in Agriculture; April 2010; Manhattan, Kansas.
Professional Membership
American Statistical Association, International Biometric Society, and International
Chinese Statistical Association
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