@article{reeve_berry_xiao_ferguson_thuerk_goetz_2015, title={Benefits of Model-based Drug Development: A Rigorous, Planned Case Study}, volume={44}, ISSN={["1532-4141"]}, DOI={10.1080/03610918.2013.833232}, abstractNote={Model-based drug development (MBDD) is useful to make better quantitative decisions within drug development. However, rigorous evaluation of the benefits has been scarce in the literature. In this study, we take a completed development program, retrospectively repeat it in a virtual setting using MBDD methodologies, and compare it to the traditional drug development process. The conclusion is that the use of MBDD could have facilitated more efficient use of resources.}, number={9}, journal={COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION}, author={Reeve, Russell and Berry, Seth and Xiao, Wei and Ferguson, Bradley and Thuerk, Marcel and Goetz, Ruediger}, year={2015}, pages={2210–2222} }
 @article{xiao_wu_zhou_2015, title={ConvexLAR: An Extension of Least Angle Regression}, volume={24}, ISSN={["1537-2715"]}, DOI={10.1080/10618600.2014.962700}, abstractNote={The least angle regression (LAR) was proposed by Efron, Hastie, Johnstone and Tibshirani in the year 2004 for continuous model selection in linear regression. It is motivated by a geometric argument and tracks a path along which the predictors enter successively and the active predictors always maintain the same absolute correlation (angle) with the residual vector. Although it gains popularity quickly, its extensions seem rare compared to the penalty methods. In this expository article, we show that the powerful geometric idea of LAR can be generalized in a fruitful way. We propose a ConvexLAR algorithm that works for any convex loss function and naturally extends to group selection and data adaptive variable selection. After simple modification, it also yields new exact path algorithms for certain penalty methods such as a convex loss function with lasso or group lasso penalty. Variable selection in recurrent event and panel count data analysis, Ada-Boost, and Gaussian graphical model is reconsidered from the ConvexLAR angle. Supplementary materials for this article are available online.}, number={3}, journal={JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS}, author={Xiao, Wei and Wu, Yichao and Zhou, Hua}, year={2015}, month={Jul}, pages={603–626} }
 @article{reeve_pang_ferguson_o'kelly_berry_xiao_2013, title={Rheumatoid Arthritis Disease Progression Modeling}, volume={47}, ISSN={["2168-4804"]}, DOI={10.1177/2168479013499571}, abstractNote={Time progression models provide a significant advantage in developing clinical trials and can also be used to elicit comparisons among therapeutic agents. The authors performed a meta-analysis to construct a time progression model for rheumatoid arthritis (RA), an area of significant interest for pharmaceutical development, using the ACR20 end point. Compounds studied were chiefly monoclonal antibodies that were used in conjunction with methotrexate. The study shows that an exponential time response model adequately fits the data. From the modeling, a distribution of effects for biological RA therapies can be provided.}, number={6}, journal={THERAPEUTIC INNOVATION & REGULATORY SCIENCE}, author={Reeve, Russell and Pang, Lei and Ferguson, Bradley and O'Kelly, Michael and Berry, Seth and Xiao, Wei}, year={2013}, month={Nov}, pages={641–650} }