@article{wu_gao_yang_reich_rappold_2024, title={Estimating spatially varying health effects of wildland fire smoke using mobile health data}, url={https://doi.org/10.1093/jrsssc/qlae034}, DOI={10.1093/jrsssc/qlae034}, abstractNote={Abstract Wildland fire smoke exposures are an increasing threat to public health, highlighting the need for studying the effects of protective behaviours on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals in real-time and subsequently study the effectiveness, but also pose methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality, and ways to record their own health symptoms and actions taken to reduce smoke exposure. We propose a doubly robust estimator of the structural nested mean model that accounts for spatially and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework also handles informative missingness by inverse probability weighting of estimating functions. We evaluate the method using extensive simulation studies and apply it to Smoke Sense data to increase the knowledge base about the relationship between health preventive measures and health-related outcomes. Our results show that the protective behaviours’ effects vary over space and time and find that protective behaviours have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the U.S.}, journal={Journal of the Royal Statistical Society Series C: Applied Statistics}, author={Wu, Lili and Gao, Chenyin and Yang, Shu and Reich, Brian J and Rappold, Ana G}, year={2024}, month={Jul} }
 @article{lee_gao_ghosh_yang_2024, title={Transporting survival of an HIV clinical trial to the external target populations}, volume={3}, ISSN={["1520-5711"]}, DOI={10.1080/10543406.2024.2330216}, abstractNote={Due to the heterogeneity of the randomized controlled trial (RCT) and external target populations, the estimated treatment effect from the RCT is not directly applicable to the target population. For example, the patient characteristics of the ACTG 175 HIV trial are significantly different from that of the three external target populations of interest: US early-stage HIV patients, Thailand HIV patients, and southern Ethiopia HIV patients. This paper considers several methods to transport the treatment effect from the ACTG 175 HIV trial to the target populations beyond the trial population. Most transport methods focus on continuous and binary outcomes; on the contrary, we derive and discuss several transport methods for survival outcomes: an outcome regression method based on a Cox proportional hazard (PH) model, an inverse probability weighting method based on the models for treatment assignment, sampling score, and censoring, and a doubly robust method that combines both methods, called the augmented calibration weighting (ACW) method. However, as the PH assumption was found to be incorrect for the ACTG 175 trial, the methods that depend on the PH assumption may lead to the biased quantification of the treatment effect. To account for the violation of the PH assumption, we extend the ACW method with the linear spline-based hazard regression model that does not require the PH assumption. Applying the aforementioned methods for transportability, we explore the effect of PH assumption, or the violation thereof, on transporting the survival results from the ACTG 175 trial to various external populations.}, journal={JOURNAL OF BIOPHARMACEUTICAL STATISTICS}, author={Lee, Dasom and Gao, Chenyin and Ghosh, Sujit and Yang, Shu}, year={2024}, month={Mar} }
 @article{yang_gao_zeng_wang_2023, title={Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation}, volume={4}, ISSN={["1467-9868"]}, url={https://doi.org/10.1093/jrsssb/qkad017}, DOI={10.1093/jrsssb/qkad017}, abstractNote={Abstract}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY}, author={Yang, Shu and Gao, Chenyin and Zeng, Donglin and Wang, Xiaofei}, year={2023}, month={Apr} }
 @article{gao_yang_2023, title={Pretest estimation in combining probability and non-probability samples}, volume={17}, ISSN={["1935-7524"]}, DOI={10.1214/23-EJS2137}, abstractNote={Multiple heterogeneous data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we develop a unified framework of the test-and-pool approach to general parameter estimation by combining gold-standard probability and non-probability samples. We focus on the case when the study variable is observed in both datasets for estimating the target parameters, and each contains other auxiliary variables. Utilizing the probability design, we conduct a pretest procedure to determine the comparability of the non-probability data with the probability data and decide whether or not to leverage the non-probability data in a pooled analysis. When the probability and non-probability data are comparable, our approach combines both data for efficient estimation. Otherwise, we retain only the probability data for estimation. We also characterize the asymptotic distribution of the proposed test-and-pool estimator under a local alternative and provide a data-adaptive procedure to select the critical tuning parameters that target the smallest mean square error of the test-and-pool estimator. Lastly, to deal with the non-regularity of the test-and-pool estimator, we construct a robust confidence interval that has a good finite-sample coverage property.}, number={1}, journal={ELECTRONIC JOURNAL OF STATISTICS}, author={Gao, Chenyin and Yang, Shu}, year={2023}, pages={1492–1546} }
 @article{gao_yang_kim_2023, title={Soft calibration for selection bias problems under mixed-effects models}, volume={3}, ISSN={["1464-3510"]}, url={https://doi.org/10.1093/biomet/asad016}, DOI={10.1093/biomet/asad016}, abstractNote={Abstract}, journal={BIOMETRIKA}, author={Gao, Chenyin and Yang, Shu and Kim, Jae Kwang}, year={2023}, month={Mar} }
 @article{deng_gao_2023, title={Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market}, volume={2}, ISSN={["1468-0106"]}, DOI={10.1111/1468-0106.12417}, abstractNote={Abstract}, journal={PACIFIC ECONOMIC REVIEW}, author={Deng, Yang and Gao, Chenyin}, year={2023}, month={Feb} }
 @article{gao_thompson_kim_yang_2022, title={Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling}, volume={5}, ISSN={["1467-985X"]}, url={https://doi.org/10.1111/rssa.12841}, DOI={10.1111/rssa.12841}, abstractNote={Abstract}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY}, author={Gao, Chenyin and Thompson, Katherine Jenny and Kim, Jae Kwang and Yang, Shu}, year={2022}, month={May} }
 @article{xie_du_zhao_gao_lyu_suo_kuang_2021, title={Advanced trophectoderm quality increases the risk of a large for gestational age baby in single frozen-thawed blastocyst transfer cycles}, volume={36}, ISSN={["1460-2350"]}, DOI={10.1093/humrep/deab088}, abstractNote={Abstract}, number={8}, journal={HUMAN REPRODUCTION}, author={Xie, Qin and Du, Tong and Zhao, Ming and Gao, Chenyin and Lyu, Qifeng and Suo, Lun and Kuang, Yanping}, year={2021}, month={Aug}, pages={2111–2120} }