@article{lin_gu_wheeler_young_holloway_sunny_moore_zeng_2022, title={Effectiveness of Covid-19 Vaccines over a 9-Month Period in North Carolina}, ISSN={["1533-4406"]}, DOI={10.1056/NEJMoa2117128}, abstractNote={The duration of protection afforded by coronavirus disease 2019 (Covid-19) vaccines in the United States is unclear. Whether the increase in postvaccination infections during the summer of 2021 was caused by declining immunity over time, the emergence of the B.1.617.2 (delta) variant, or both is unknown.We extracted data regarding Covid-19-related vaccination and outcomes during a 9-month period (December 11, 2020, to September 8, 2021) for approximately 10.6 million North Carolina residents by linking data from the North Carolina Covid-19 Surveillance System and the Covid-19 Vaccine Management System. We used a Cox regression model to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), and Ad26.COV2.S (Johnson & Johnson-Janssen) vaccines in reducing the current risks of Covid-19, hospitalization, and death, as a function of time elapsed since vaccination.For the two-dose regimens of messenger RNA (mRNA) vaccines BNT162b2 (30 μg per dose) and mRNA-1273 (100 μg per dose), vaccine effectiveness against Covid-19 was 94.5% (95% confidence interval [CI], 94.1 to 94.9) and 95.9% (95% CI, 95.5 to 96.2), respectively, at 2 months after the first dose and decreased to 66.6% (95% CI, 65.2 to 67.8) and 80.3% (95% CI, 79.3 to 81.2), respectively, at 7 months. Among early recipients of BNT162b2 and mRNA-1273, effectiveness decreased by approximately 15 and 10 percentage points, respectively, from mid-June to mid-July, when the delta variant became dominant. For the one-dose regimen of Ad26.COV2.S (5 × 1010 viral particles), effectiveness against Covid-19 was 74.8% (95% CI, 72.5 to 76.9) at 1 month and decreased to 59.4% (95% CI, 57.2 to 61.5) at 5 months. All three vaccines maintained better effectiveness in preventing hospitalization and death than in preventing infection over time, although the two mRNA vaccines provided higher levels of protection than Ad26.COV2.S.All three Covid-19 vaccines had durable effectiveness in reducing the risks of hospitalization and death. Waning protection against infection over time was due to both declining immunity and the emergence of the delta variant. (Funded by a Dennis Gillings Distinguished Professorship and the National Institutes of Health.).}, journal={NEW ENGLAND JOURNAL OF MEDICINE}, author={Lin, Dan-Yu and Gu, Yu and Wheeler, Bradford and Young, Hayley and Holloway, Shannon and Sunny, Shadia-Khan and Moore, Zack and Zeng, Donglin}, year={2022}, month={Jan} }
 @article{huang_callahan_wu_holloway_brochu_lu_peng_tzeng_2022, title={Phylogeny-guided microbiome OTU-specific association test (POST)}, volume={10}, ISSN={["2049-2618"]}, url={https://doi.org/10.1186/s40168-022-01266-3}, DOI={10.1186/s40168-022-01266-3}, abstractNote={Abstract Background The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effects of microbes, and phylogenetic information should be incorporated in a data-supervised fashion. Results In this work, we propose a local collapsing test called phylogeny-guided microbiome OTU-specific association test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenetic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community level to OTU level. Using simulation studies, we show that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, in real data applications on bacterial vaginosis and on preterm birth, we find that POST can identify similar or more outcome-associated OTUs that are of biological relevance compared to existing methods. Conclusions Using POST, we show that adaptively leveraging the phylogenetic information can enhance the selection performance of associated microbiome features by improving the overall true-positive and false-positive detection. We developed a user friendly R package POSTm which is freely available on CRAN ( https://CRAN.R-project.org/package=POSTm ).}, number={1}, journal={MICROBIOME}, author={Huang, Caizhi and Callahan, Benjamin John and Wu, Michael C. and Holloway, Shannon T. and Brochu, Hayden and Lu, Wenbin and Peng, Xinxia and Tzeng, Jung-Ying}, year={2022}, month={Jun} }
 @article{tsiatis_davidian_holloway_2021, title={Estimation of the odds ratio in a proportional odds model with censored time-lagged outcome in a randomized clinical trial}, volume={12}, ISSN={["1541-0420"]}, url={https://doi.org/10.1111/biom.13603}, DOI={10.1111/biom.13603}, abstractNote={In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR; active agent vs control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, for example, because ascertainment of the outcome may not be possible until some prespecified follow-up time. Accordingly, the outcome from these subjects can be viewed as censored. A valid interim analysis can be based on data only from those subjects with full follow-up; however, this approach is inefficient, as it does not exploit additional information that may be available on those for whom the outcome is not yet available at the time of the interim analysis. Appealing to the theory of semiparametrics, we propose an estimator for the OR in a proportional odds model with censored, time-lagged categorical outcome that incorporates additional baseline and time-dependent covariate information and demonstrate that it can result in considerable gains in efficiency relative to simpler approaches. A byproduct of the approach is a covariate-adjusted estimator for the OR based on the full data that would be available at a final analysis.}, journal={BIOMETRICS}, author={Tsiatis, Anastasios A. and Davidian, Marie and Holloway, Shannon T.}, year={2021}, month={Dec} }
 @book{tsiatis_davidian_holloway_laber_2019, title={Dynamic Treatment Regimes}, ISBN={9780429192692}, url={http://dx.doi.org/10.1201/9780429192692}, DOI={10.1201/9780429192692}, abstractNote={Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors' website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.}, publisher={Chapman and Hall/CRC}, author={Tsiatis, Anastasios A. and Davidian, Marie and Holloway, Shannon T. and Laber, Eric B.}, year={2019}, month={Dec} }
 @article{marceau_lu_holloway_sale_worrall_williams_hsu_tzeng_2015, title={A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction}, volume={39}, ISSN={["1098-2272"]}, DOI={10.1002/gepi.21909}, abstractNote={ABSTRACT Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene‐gene or gene‐environment interactions, incorporating variance‐component based methods for population substructure into rare‐variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the “expectation‐maximization (EM)” algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene‐environment interaction, we propose a computationally efficient and statistically rigorous “fastKM” algorithm for multikernel analysis that is based on a low‐rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single‐kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM‐based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene‐by‐vitamin effects on recurrent stroke risk and gene‐by‐age effects on change in homocysteine level.}, number={6}, journal={GENETIC EPIDEMIOLOGY}, author={Marceau, Rachel and Lu, Wenbin and Holloway, Shannon and Sale, Michele M. and Worrall, Bradford B. and Williams, Stephen R. and Hsu, Fang-Chi and Tzeng, Jung-Ying}, year={2015}, month={Sep}, pages={456–468} }
 @article{laber_tsiatis_davidian_holloway_2014, title={Combining Biomarkers to Optimize Patient Treatment Recommendations Discussions}, volume={70}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12187}, abstractNote={BiometricsVolume 70, Issue 3 p. 707-710 BIOMETRIC PRACTICE Discussion of “Combining biomarkers to optimize patient treatment recommendation” Eric B. Laber, Corresponding Author Eric B. Laber Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.email: [email protected]Search for more papers by this authorAnastasios A. Tsiatis, Anastasios A. Tsiatis Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.Search for more papers by this authorMarie Davidian, Marie Davidian Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.Search for more papers by this authorShannon T. Holloway, Shannon T. Holloway Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.Search for more papers by this author Eric B. Laber, Corresponding Author Eric B. Laber Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.email: [email protected]Search for more papers by this authorAnastasios A. Tsiatis, Anastasios A. Tsiatis Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.Search for more papers by this authorMarie Davidian, Marie Davidian Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.Search for more papers by this authorShannon T. Holloway, Shannon T. Holloway Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695–8203, U.S.A.Search for more papers by this author First published: 02 June 2014 https://doi.org/10.1111/biom.12187Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Barto, A. and Dieterich, T. (2004). Reinforcement learning and its relation to supervised learning. In Handbook of Learning and Approximate Dynamic Programming, J. Si, A. G. Barto, W. B. Powell, and D. Wunsch (eds), 45–63. 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International Journal of Biostatistics 6, Article 8. Robins, J. M. (2004). Optimal structured nested models for optimal sequential decisions. In Proceedings of the Second Seattle Symposium on Biostatistics, D. Y. Lin and P. J. Heagerty (eds), 189–326. New York: Springer. Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models. Journal of the American Statistical Association 94, 1096–1120. Schulte, P., Tsiatis, A., Laber, E., and Davidian, M. (in press). Q-and A-learning methods for estimating optimal dynamic treatment regimes. Statistical Science. Zhang, B., Tsiatis, A. A., Davidian, M., Zhang, M., and Laber, E. (2012a). Estimating optimal treatment regimes from a classification perspective. Stat 1, 103–114. Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2012b). A robust method for estimating optimal treatment regimes. Biometrics 68, 1010–1018. Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2013). Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. Biometrika 100, 681–694. Zhao, Y., Zeng, D., Rush, A. J., and Kosorok, M. R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association 107, 1106–1118. Zhao, Y., Zeng, D., Socinski, M. A., and Kosorok, M. R. (2011). Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer. Biometrics 67, 1422–1433. Citing Literature Volume70, Issue3September 2014Pages 707-710 ReferencesRelatedInformation}, number={3}, journal={BIOMETRICS}, author={Laber, Eric B. and Tsiatis, Anastasios A. and Davidian, Marie and Holloway, Shannon T.}, year={2014}, month={Sep}, pages={707–710} }