Johns Hopkins Bloomberg School of Public Health
University of California at Santa Barbara
Adobe Research
Carnegie Mellon University
University of California at Berkeley
For the papers, “US Abortion Bans and Fertility” and “US Abortion Bans and Infant Mortality” in the Journal of the American Medical Association
The Outstanding Statistical Application Award was established in 1986 to recognize the authors of papers that demonstrate an outstanding application of statistics in any substantive field. Award recipients are presented with $1,000 which may be divided evenly among the recipients or, the award committee may recognize an additional $1,000 winner each year if they wish. The award is presented annually if, in the opinion of the awards committee, an eligible and worthy work is nominated.
The Outstanding Statistical Application Award is bestowed upon a distinguished individual or individuals based on the following criteria:
Eligible work includes papers, monographs, reports, and other substantive evidence appearing no more than two years prior to the nomination deadline of the award. All nominated work must have been subject to external peer review and accepted for publication by the nomination deadline.
The award recipient(s) are responsible for providing a current photograph and general information, along with permission to reprint the work in ASA promotional materials.
Nominations are due by March 1 and require the following:
Please contact the committee chair.
2024: Irina Degtiar, Tim Layton, Jacob Wallace, Sherri Rose, for their paper, “Conditional Cross-Design Synthesis Estimates for Generalizability in Medicaid” 2023: Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew Rigby, Yi Cao, Noel Cressie, for their paper, “WOMBAT v1.0: A Fully Bayesian Global Flux-Inversion Framework” Geoscientific Model Development”. 2022: Xu Shi, Xiaoou Li, Tianxi Cai, for their paper, “Spherical Regression Under Mismatch Corruption with Application to Automated Knowledge Translation” 2021: Qian Guan, Brian J. Reich, Eric B. Laber, Dipankar Bandyopadhyay, for their paper “Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals” 2020: Youjin Lee, Katharine Laughon Grantz, Mei-Cheng Wang, Rajeshwari Sundaram, for their paper, “Joint Modeling of Competing Risks Data and Current Status Data: An Application to Spontaneous Labour Study”. 2020: Yiming Hu, Brian W. Kunkle, Mo Li, B. Shubhabrata Mukherjee, Qiongshi Lu, Pradeep Natarajan, Haoyi Weng, Adam Naj, Jiawei Wang, Amanda Kuzma, Seyedeh M. Zekavat, Yi Zhao, Zhaolong Yu, Paul K. Crane, Boyang Li, Alzheimer's Disease Genetics Consortium, Jianlei Gu, Hui Lu, Sydney Muchnik, Hongyu Zhao, Yu Shi, for their paper, “A Statistical Framework for Cross-Tissue Transcriptome-Wide Association Analysis”.
2019: Liangyuan Hu, Joseph Hogan, Ann Mwangi, Abraham Siika, for their novel development and applications of statistical approaches to assess the causal effects of treatment initiation time on patient survival using electronic health records (EHR). The authors developed a structural causal hazard model for drawing causal inferences about the optimal timing of treatment initiation on a continuous scale using observational EHR data. Their work leads to impactful outcomes in medical practice. Their paper, “Modeling the Causal Effect of Treatment Initiation Time on Survival: Application to HIV/TB Co-Infection,” was published in Biometrics in 2018. 2018: Peijie Hou, Joshua M. Tebbs, Christopher R. Bilder, and Christopher S. McMahan for their novel development of a statistical framework that quantifies the operating characteristics of hierarchical group testing for multiple rare diseases. The authors cleverly reformulate the pool decoding process as a time‐inhomogeneous, finite‐state Markov chain and provide analytical solutions of prediction accuracy and the expected number of tests. Their methodology is having tremendous impact on public health screening, and their paper, “Hierarchical Group Testing for Multiple Infections,” was published in Biometrics in 2017. 2017: Abhirup Datta, Sudipto Banerjee, Andrew O. Finley, Nicholas A. S. Hamm, and Martijn Schaap for “Non-Separable Dynamic Nearest-Neighbor Gaussian Process Models for Spatio-Temporal Data with an Application to Particulate Matter Analysis,” published in the Annals of Applied Statistics in 2016. 2016: Edoardo M. Airoldi and Johnathan M. Bischof for “A Regularization Scheme on Word Occurrence Rates That Improves Estimation and Interpretation of Topical Content,” published in the Journal of the American Statistical Association in 2015. 2015: Anne R. Cappola, Leslie J. Crofford, Wensheng Guo, and Ziyue Liu for “Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models,”, published in the Journal of the American Statistical Association in 2014. 2014: Christopher R. Bilder, Christopher S. McMahan, and Joshua M. Tebbs for “Two-Stage Hierarchical Group Testing for Multiple Infections with Application to the Infertility Prevention Project,” published in Biometrics in 2013. 2013: Robert E. Kass, Ryan C. Kelly, and Wei-Liem Loh for “Assessment of Synchrony in Multiple Neural Spike Trains Using Loglinear Point Process Models,” published in The Annals of Applied Statistics in 2011. 2012: Chae Young Lim and Sarat C. Dass for “Assessing Fingerprint Individuality Using EPIC: A Case Study in the Analysis of Spatially Dependent Marked Processes,” published in Technometrics in 2011. 2011: Adrian E. Raftery, Miroslav Kárný, and Pavel Ettler