@article{liu_yang_zhang_liu_2024, title={Multiply robust estimators in longitudinal studies with missing data under control-based imputation}, volume={80}, ISSN={["1541-0420"]}, DOI={10.1093/biomtc/ujad036}, abstractNote={Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves n1/2-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than n-1/4 when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.}, number={1}, journal={BIOMETRICS}, author={Liu, Siyi and Yang, Shu and Zhang, Yilong and Liu, Guanghan}, year={2024}, month={Jan} }
 @article{liu_zhang_golm_liu_yang_2023, title={Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes}, volume={9}, ISSN={["2475-4277"]}, DOI={10.1080/24754269.2023.2261351}, abstractNote={Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.}, journal={STATISTICAL THEORY AND RELATED FIELDS}, author={Liu, Siyi and Zhang, Yilong and Golm, Gregory T. and Liu, Guanghan and Yang, Shu}, year={2023}, month={Sep} }
 @article{liu_yang_zhang_liu_2022, title={Sensitivity analyses in longitudinal clinical trials via distributional imputation}, volume={11}, ISSN={["1477-0334"]}, DOI={10.1177/09622802221135251}, abstractNote={Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analyses are critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies and the pharmaceutical industry use sensitivity models such as return-to-baseline, control-based, and washout imputation, following the ICH E9(R1) guidance. Multiple imputation is popular in sensitivity analyses; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin’s combining rule. We propose distributional imputation in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the distributional imputation estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The superiority of the distributional imputation framework is validated in the simulation study and an antidepressant longitudinal clinical trial.}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Liu, Siyi and Yang, Shu and Zhang, Yilong and Liu, Guanghan}, year={2022}, month={Nov} }