@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{laber_lizotte_ferguson_2014, title={Set-Valued Dynamic Treatment Regimes for Competing Outcomes}, volume={70}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12132}, abstractNote={SummaryDynamic treatment regimes (DTRs) operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up‐to‐date patient information to a single recommended treatment. Current methods for estimating optimal DTRs, for example Q‐learning, require the specification of a single outcome by which the “goodness” of competing dynamic treatment regimes is measured. However, this is an over‐simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, for example, symptom relief and side‐effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing DTRs that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set‐valued functions that take as input up‐to‐date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non‐inferior outcome vectors. Constructing these set‐valued functions requires solving a non‐trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study.}, number={1}, journal={BIOMETRICS}, author={Laber, Eric B. and Lizotte, Daniel J. and Ferguson, Bradley}, year={2014}, month={Mar}, pages={53–61} }
@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} }