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The Section for Statistical Programmers and Analysts promotes discussions of

• Programming concepts, theories, and techniques that are used for statistical analysis
• Activities and pre-analysis tasks (e.g., data management) that affect statistical programming
• Career paths and opportunities

SSPA Executive Committee Meeting - by invitation only

Time: Sunday, August 2: 5:00 PM - 6:30 PM

Location: Westin Boston Seaport District, Room: W-Stone

A.M. Roundtable Discussion (Added Fee)

1. Format: Roundtables - Breakfast

Time: Monday, August 3: 7:00 AM - 8:15 AM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-Ballroom Foyer

Presenting Author: Caleb King , JMP

ML07: Trust but Verify: Sharing Testing and Validation Techniques for Statistical Software Applications

Leadership Development for Statisticians, Data Scientists and Programmers

Format: Professional Development Course/CE

Time: Monday, August 3: 8:30 AM - 5:00 PM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-151B

Presenting Author: Gina-Maria Pomann

Contributed Poster Presentations

Format: Contributed Posters

Time: Monday, August 3: 10:30 AM - 12:20 PM

Location: Thomas M. Menino Convention & Exhibition Center, CC-Exhibit Hall A

13. Automating Lab Reference Range Extraction for Clinical Trials

Presenting Author: Dainel Jin, Winchester High School

View poster information

Emerging Methods and Practical Innovations in Applied Statistics and Data Science

Format: Contributed Papers

Time: Monday, August 3: 10:30 AM - 12:20 PM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-108

Chair: Dhuly Chowdhury, RTI International

1.  Intercurrent Event Monitoring in Pragmatic Clinical Trials of Externally-Managed Interventions

Time: 10:35 AM - 10:50 AM

Presenting Author: Mary Ryan Baumann, University of Wisconsin-Madison

2. A New Stata Command for Zero-Inflated Binomial Modeling

Time: 10:50 AM - 11:05 AM

Presenting Author: Adiba Bilqees Promiti, University of South Carolina

Co-author: James Hardin

3. Improving Forecast Accuracy of Motor Vehicle Deaths by Demographic Factors

Time: 11:05 AM - 11:20 AM

Presenting Author: Tadesse Haileyesus, UGA

Co-authors: Daniel Jung (University of Georgia), Jessica Smith (University of Georgia), and Carlos Siordia (Emory University)

4. Statistical and Machine Learning Approaches to Forecasting Long-Memory CPI Data

Time: 11:20 AM - 11:35 AM

Presenting Author: Changzhi Ma, The College of Wooster

Co-author: Haimeng Zhang, University of North Carolina at Greensboro

5. Comparing Traditional and Modern Techniques for Propensity Score Estimation in Clinical Trials

Time: 11:35 AM - 11:50 AM

Presenting Author: Kangwoo Lee, Edwards Lifesciences

Co-authors: Songtao Jiang and Jennifer Mares , Edwards Lifesciences

6. Launching a Statistical Operations Team to Support a Multi-Site Clinical Trial Network

Time: 11:50 AM - 12:05 PM

Presenting Author: Katherine Young, University of Kansas Medical Center

Co-authors: Alexandra Brown (State of Kansas), Junqiang Dai , Lauren Clark (University of Kansas Medical Center), Ron Krebill , Mohammod Mahmudur Rahman (University of Kansas Medical Center), Jianzheng Wu , Dinesh Pal Mudaranthakam , and Byron Gajewski (University of Kansas Medical Center)

SSPA Mixer

Time: Monday, August 3: 5:00 PM - 6:30 PM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-251

Single Arm Trials - Good or Bad?

Format: Topic-Contributed Panel Session

Time: Tuesday, August 4: 10:30 AM - 12:20 PM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-258B

Panelists: Vipin Arora ; Xiaofei Wang (Duke University Medical Center); and Melvin Munsaka (AbbVie)

Adaptive Borrowing as an Alternative to Randomized Clinical Trials

Format: Invited Panel Session

Time: Wednesday, August 5: 8:30 AM - 10:20 AM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-258B

Panelists: Vipin Arora and Antara Majumdar (GSK)

P.M. Roundtable Discussion (Added Fee)

1. Format: Roundtables - Lunch

Time: Wednesday, August 5: 12:30 PM - 1:50 PM

Location: Thomas M. Menino Convention & Exhibition Center, Room: CC-Ballroom Foyer

Presenting Author: Kuolung Hu, AbbVie

WL11: Harmonizing Safety Data: Strategies and Challenges in Integrated Analyses for Drug Submissions

Other Information:

2027 JSM, August 8 – 12, 2027, Chicago, Illinois

• The SSPA members count was 1,130 on July 3, 2023.
• Information about SSPA webinars
• Spring 2025 Newsletter
• Fall 2024 Newsletter
• Spring 2024 Newsletter
• SSPA is now on Twitter at @SSPA_amstat !!!
• 2016 Business Meeting Group Picture
• SSPA Newsletter Archives

We are delighted to announce the recipient of the 2026 Award for Innovation in Statistical Programming and Analytics (AISPAA). This award recognizes individuals and teams who have made significant contributions to the development and enhancement of tools, software, and methodologies that address problems in statistical programming and analytics. It also honors those who have made substantial contributions to the community of statistical programmers and analysts.

This year, we proudly present the award to:

Yunzhe (Bella) Qian, MS

Biostatistician II, Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center

Yunzhe (Bella) Qian is recognized for her leadership in developing spatialAtomizeR, the first R package to implement Bayesian atom-based regression models for spatially misaligned areal data. She began developing the software while completing her MS in Biostatistics at the Harvard T.H. Chan School of Public Health, where she graduated in 2025. She served as the lead developer of the package and is the primary author of the accompanying software manuscript.

Spatial misalignment occurs when variables are collected or reported over geographic areas whose boundaries do not coincide, for example, when health outcomes are measured by county while policy or environmental variables are measured by congressional district, census tract, or another administrative region. Conventional approaches often redistribute such data deterministically, without adequately accounting for uncertainty. Bayesian atom-based regression models offer a more rigorous alternative, but their computational complexity and extensive spatial bookkeeping have historically made them difficult for applied researchers to use.

spatialAtomizeR addresses these challenges by automating the construction and management of spatial atoms and providing a practical Bayesian framework for analyzing spatially misaligned data. The package extends atom-based regression methods beyond their earlier reliance on Poisson models to support multiple data types, including count, proportion, and continuous variables. It also incorporates correlation among covariates and provides rigorous uncertainty quantification for analyses involving multiple spatially referenced variables.

Implemented using NIMBLE and compiled C++ code, spatialAtomizeR combines advanced statistical methodology with accessible software tools, intuitive syntax, validation procedures, worked examples, and comprehensive documentation. By automating many of the most technically demanding steps, the package makes Bayesian atom-based regression models available to researchers without requiring specialized expertise in spatial computation.

The package has broad potential applications in epidemiology, environmental health, public policy, political science, urban planning, and other fields in which outcomes, exposures, policies, and contextual variables are frequently measured over different geographic boundaries. Through this work, Bella has helped transform spatial misalignment from a specialized methodological obstacle into a tractable analytical problem for applied researchers.

spatialAtomizeR is freely available through:

• CRAN: spatialAtomizeR
• GitHub: bellayqian/spatialAtomizeR

We congratulate Yunzhe (Bella) Qian on this outstanding achievement and her valuable contribution to statistical programming and analytics. Her work exemplifies the spirit of the AISPAA by combining methodological rigor, computational innovation, open-source software development, and practical impact.

Please join us in celebrating the accomplishments of the 2026 AISPAA recipient!

The Section for Statistical Programmers and Analysts (SSPA) is pleased to announce the recipients of the 2026 SSPA Conference Grants. Through this program, SSPA provides financial support to student members and recent graduates to help offset registration and travel expenses for ASA-sponsored conferences. The program reflects the section's commitment to encouraging professional development, fostering participation in the statistical community, and supporting the next generation of statistical programmers and analysts.

We congratulate the following recipients of the 2026 SSPA Conference Grants:

• Adiba Bilqees Promiti
  University of South Carolina
• Prithwish Ghosh
  North Carolina State University
• Emmanuel Kubuafor
  University of New Mexico
• Arpan Kumar
  North Carolina State University
• Haoling Wang
  University of Pittsburgh
• Hanna Venera
  University of Michigan

We congratulate this year's recipients and look forward to their continued contributions to statistical programming, analytics, and the broader statistical community. We hope this support enhances their conference experience and provides valuable opportunities to share their research, build professional networks, and engage with colleagues across academia, industry, and government.

For more information about the SSPA Conference Grant Program, including eligibility requirements and future application opportunities, please visit the SSPA Conference Grants page .

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The SSPA officers meet on the first Friday of each month at 2:00 p.m. Central Time. 

1. We are currently reviewing the SSPA Charter.
2. JSM-related: The list of SSPA-sponsored or co-sponsored sessions (invited, contributed, and poster) included in the JSM program is available. 

• SSPA provides a venue for us statistical programmers to discuss interests, share experiences, knowledge and challenges, and support each other across organizations, career stages and geographic locations.
• Joining SSPA is an investment in my career and provides a solid platform to me for interacting effectively with peers.
• SSPA increases awareness of our roles in the entire statistical and analytics community.