@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={Abstract Background 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. Methods 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. Results 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. Conclusions 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"]}, DOI={10.1186/s40168-022-01266-3}, abstractNote={Abstract}, 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={Abstract}, 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}, 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}, 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={We congratulate the Kang, Janes, and Huang (hereafter KJH) on an interesting and powerful new method for estimating an optimal treatment rule, also referred to as an optimal treatment regime. Their proposed method relies on having a high-quality estimator for the regression of outcome on biomarkers and treatment, which the authors obtain using a novel boosting algorithm. Methods for constructing treatment rules/regimes that rely on outcome models are sometimes called indirect or regression-based methods because the treatment rule is inferred from the outcome model (Barto and Dieterich, 1988). 
 
Regression-based methods are appealing because they can be used to make prognostic predictions as well as treatment recommendations. While it is common practice to use parametric or semiparametric models in regression-based approaches (Robins, 2004; Chakraborty and Moodie, 2013; Laber et al., 2014; Schulte et al., 2014), there is growing interest in using nonparametric methods to avoid model misspecification (Zhao et al., 2011; Moodie et al., 2013). In contrast, direct estimation methods, also known as policy-search methods, try to weaken or eliminate dependence on correct outcome models and instead attempt to search for the best treatment rule within a pre-specified class of rules (Orellana, Rotnitzky, and Robins, 2010; Zhang et al., 2012a,b; Zhao et al., 2012; Zhang et al., 2013). Direct estimation methods make fewer assumptions about the outcome model, which may make them more robust to model misspecification but potentially more variable. 
 
We derive a direct estimation analog to the method of KJH, which we term value boosting. The method is based on recasting the problem of estimating an optimal treatment rule as a weighted classification problem (Zhang et al., 2012a; Zhao et al., 2012). We show how the method of KJH can be used with existing policy-search methods to construct a treatment rule that is interpretable, logistically feasible, parsimonious, or otherwise appealing.}, 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} }