Xiaoqin Wang

Refereegranskat

• Yin, L. & Wang, X. (2024). Estimating and testing sequential causal effects based on alternative G-formula: an observational study of the influence of early diagnosis on survival of cardia cancer. Taylor & Francis, Communications in statistics. Simulation and computation, 53 (4), (1917-1931). 10.1080/03610918.2022.2060511 [Mer information]

• Lan, Y., Yin, L. & Wang, X. (2022). Dynamics of COVID-19 progression and the long-term influences of measures on pandemic outcomes. BMC, Emerging Themes in Epidemiology, 19 (), 10.1186/s12982-022-00119-6 [Mer information]

• Wang, X., Blom, J., Ye, W. & Yin, L. (2022). Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments. Sage, Statistical Methods in Medical Research, 31 (8), (1538-1548). 10.1177/09622802221098429 [Mer information]

• Wang, X., Wallentin, F. & Yin, L. (2022). The statistical evidence missing from the Swedish decision-making of COVID-19 strategy during the early period: A longitudinal observational analysis. Elsevier, SSM - Population Health, 18 (), 10.1016/j.ssmph.2022.101083 [Mer information]

• Wang, X. & Yin, L. (2020). New g-formula for the sequential causal effect and blip effect of treatment in sequential causal inference. Annals of Statistics, 48 (1), (138-160). 10.1214/18-AOS1795 [Mer information]

• Yin, L. & Wang, X. (2017). Estimating confidence regions of common measures of the baseline and treatment effect on dichotomous outcome of a population. Communications in statistics. Simulation and computation, 46 (4), (3034-3049). [Mer information]

• Yin, L., Wang, X. & Ye, W. (2017). Maximum-likelihood estimation and presentation for the interaction between treatments in observational studies with a dichotomous outcome. Communications in statistics. Simulation and computation, 46 (9), (7138-7153). [Mer information]

• Wang, X., Ye, W. & Yin, L. (2017). Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies. Journal of Applied Statistics, 44 (14), (2483-2498). [Mer information]

• Wang, X., Jin, Y. & Yin, L. (2016). Measuring and estimating treatment effect on dichotomous outcome of a population. Statistical Methods in Medical Research, 25 (5), (1779-1790). [Mer information]

• Wang, X. & Yin, L. (2015). Identifying and estimating net effects of treatments in sequential casual inference. Electronic Journal of Statistics, 9 (), (1608-1643). 10.1214/15-EJS1046 [Mer information]

• Yin, L. & Wang, X. (2015). Measuring and estimating treatment effect on count outcome in randomized trial and observational studies. Communications in Statistics - Theory and Methods, 44 (5), (1080-1095). [Mer information]

• Wang, X. & Yin, L. (2015). Point and interval estimation of baseline risk and treatment effect based on logistic model for observational studies. Biometrical Journal, 57 (3), (441-452). [Mer information]

• Wang, X., Yin, J. & Yin, L. (2015). Point and interval estimations of marginal risk difference by logistic model. Communications in Statistics - Theory and Methods, 44 (17), (3703-3722). [Mer information]

• Wang, X. & Yin, L. (2013). Identification of Confounding versus Dispersing Covariates by Confounding Influence. Communications in Statistics - Theory and Methods, 42 (24), (4540-4556). [Mer information]

• Yin, L., Sundberg, R., Wang, X. & Rubin, D. (2006). Control of confounding through secondary samples. Statistics in Medicine, 25 (22), (3814-3825). [Mer information]

• Wang, X. (1993). The finite part of singular integrals in several complex variables. Transactions of the American Mathematical Society, 337 (2), (771-793). [Mer information]

• Wang, X. (1991). Analyticity theorems for parameter-dependent currents. Mathematica Scandinavica, 69 (2), (179-198). [Mer information]