@article{guan_reich_laber_2021, title={A spatiotemporal recommendation engine for malaria control}, volume={4}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxab010}, abstractNote={Summary Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited resources in realtime for malaria control. We formalize a policy for the resource allocation as a sequence of decisions, one per intervention decision, that map up-to-date disease related information to a resource allocation. An optimal policy must control the spread of the disease while being interpretable and viewed as equitable to stakeholders. We construct an interpretable class of resource allocation policies that can accommodate allocation of resources residing in a continuous domain and combine a hierarchical Bayesian spatiotemporal model for disease transmission with a policy-search algorithm to estimate an optimal policy for resource allocation within the pre-specified class. The estimated optimal policy under the proposed framework improves the cumulative long-term outcome compared with naive approaches in both simulation experiments and application to malaria interventions in the Democratic Republic of the Congo.}, journal={BIOSTATISTICS}, author={Guan, Qian and Reich, Brian J. and Laber, Eric B.}, year={2021}, month={Apr} }
 @article{yang_zhang_liu_guan_2021, title={SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale}, volume={9}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13555}, abstractNote={Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the nonparametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinical trial.}, journal={BIOMETRICS}, author={Yang, Shu and Zhang, Yilong and Liu, Guanghan Frank and Guan, Qian}, year={2021}, month={Sep} }
 @article{guan_reich_laber_bandyopadhyay_2020, title={Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1660169}, abstractNote={Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine nonparametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.}, number={531}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Guan, Qian and Reich, Brian J. and Laber, Eric B. and Bandyopadhyay, Dipankar}, year={2020}, month={Jul}, pages={1066–1078} }
 @article{guan_laber_reich_2016, title={Bayesian nonparametric estimation for dynamic treatment regimes with sequential transition times comment}, volume={111}, number={515}, journal={Journal of the American Statistical Association}, author={Guan, Q. and Laber, E. B. and Reich, B. J.}, year={2016}, pages={936–942} }