@article{liu_wang_gao_poteat_matsouaka_2025, title={A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption}, DOI={10.1002/sim.70329}, abstractNote={ABSTRACT Violations of the positivity assumption can render conventional causal estimands unidentifiable, including the average treatment effect (ATE), the average treatment effect on the treated (ATT), and the average treatment effect on the controls (ATC). Shifting the inferential focus to their alternative counterparts—the weighted ATE (WATE), the weighted ATT (WATT), and the weighted ATC (WATC)—offers valuable insights into treatment effects while preserving internal validity. In this tutorial, we provide a comprehensive review of recent advances in propensity score (PS) weighting methods, along with practical guidance on how to select a primary target estimand (while other estimands serve as supplementary analyses), implement the corresponding PS‐weighted estimators, and conduct post‐weighting diagnostic assessments. The tutorial is accompanied by a user‐friendly R package, ChiPS. We demonstrate the pertinence of various estimators through extensive simulation studies. We illustrate the flow of the tutorial on two real‐world case studies: (i) Effect of smoking on blood lead level using data from the 2007–2008 National Health and Nutrition Examination Survey (NHANES); and (ii) Impact of history of sex work on HIV status among transgender women in South Africa.}, journal={Statistics in Medicine}, author={Liu, Yi and Wang, Yuan and Gao, Ying and Poteat, Tonia and Matsouaka, Roland A.}, year={2025}, month={Nov} }
 @article{liu_liu_zhou_matsouaka_2025, title={Assessing racial disparities in healthcare expenditure using generalized propensity score weighting}, DOI={10.1186/s12874-025-02508-2}, abstractNote={We found that generalized propensity score weighting (GPSW) methods are valuable quantitative tools to standardize and compare characteristics as well as outcomes of non-manipulable groups. This helps assess disparities across multiple racial and ethnic groups, as demonstrated in this study. These methods offer flexible and semi-parametric analysis on the primary causal parameters of interest (such as the racial disparities), with straightforward and intuitive interpretations. In addition, when there is violation of the positivity assumption, OWATT serves as an excellent alternative due to its greater efficiency, evidenced by relatively smaller variance. More importantly, the OWATT uses the entire dataset by assigning weights to all participants, regardless of their propensity score values. This feature of OWATT circumvents the need to specify user-defined thresholds, as required in ATT trimming or truncation, and retains as much data information as possible, leading to more reliable estimation results.}, journal={BMC Medical Research Methodology}, author={Liu, Jiajun and Liu, Yi and Zhou, Yunji and Matsouaka, Roland A.}, year={2025}, month={Mar} }
 @article{liu_zhu_han_yang_2025, title={COADVISE: covariate adjustment with variable selection in randomized controlled trials}, ISSN={0964-1998 1467-985X}, url={http://dx.doi.org/10.1093/jrsssa/qnaf171}, DOI={10.1093/jrsssa/qnaf171}, abstractNote={Abstract Adjusting for covariates in randomized controlled trials can enhance the credibility and efficiency of treatment effect estimation. However, handling numerous covariates and their complex (nonlinear) transformations poses a challenge. Motivated by the case study of the Best Apnea Interventions for Research (BestAIR) trial data from the National Sleep Research Resource (NSRR), where the number of covariates (p=114) is comparable to the sample size (N=196), we propose a principled covariate adjustment with variable selection (COADVISE) framework. COADVISE enables variable selection for covariates most relevant to the outcome while accommodating both linear and nonlinear adjustments. This framework ensures consistent estimates with improved efficiency over unadjusted estimators and provides robust variance estimation, even under outcome model misspecification. We demonstrate efficiency gains through theoretical analysis, extensive simulations, and a re-analysis of the BestAIR trial data to compare alternative variable selection strategies, offering cautionary recommendations. A user-friendly R package, Coadvise, is available to facilitate practical implementation.}, journal={Journal of the Royal Statistical Society Series A: Statistics in Society}, publisher={Oxford University Press (OUP)}, author={Liu, Yi and Zhu, Ke and Han, Larry and Yang, Shu}, year={2025}, month={Nov} }
 @article{poteat_liu_adams_merwe_cloete_howard_mccarthy_2025, title={Social determinants of HIV status and viral load suppression among transgender women in South Africa: a cross-sectional analysis}, DOI={10.1080/09540121.2025.2535471}, abstractNote={Transgender women in South Africa face a heavy HIV burden, but data on key psychosocial and structural factors remain limited. This cross-sectional study examined associations between HIV outcomes and psychosocial (substance use, alcohol use, medical distrust, community connectedness) and structural (education, homelessness, income, sex work, violence) factors. We conducted interviewer-administered surveys with 213 transgender women in Cape Town, Johannesburg, and East London between June and November 2018. Of the 213 participants, 196 knew their HIV status and 67 reported living with HIV. Multivariable logistic regression found homelessness (aOR 4.50 [95%CI: 1.67, 12.23]), sex work (aOR 5.90 [95%CI: 2.14, 16.29]), and earning above the poverty level (aOR 3.08 [95%CI: 1.37, 6.94]) were significantly associated with living with HIV. Among participants with HIV, sex work (aOR 13.39 [95%CI: 1.17, 153.67]) was the only significant predictor of viral suppression. South Africa's provision of financial support specifically for PHIV may account for associations between income and HIV; while South Africa's sex-worker specific clinics, tailored to this population's needs, may account for their higher viral suppression. Study findings highlight the importance of context-specific HIV research with key populations to identify locally relevant strategies to improve HIV outcomes.}, journal={AIDS Care}, author={Poteat, Tonia and Liu, Yi and Adams, Darya and Merwe, L. Leigh-Ann and Cloete, Allanise and Howard, Lauren E. and McCarthy, Janice}, year={2025}, month={Jul} }
 @article{liu_li_zhou_matsouaka_2024, title={Average treatment effect on the treated, under lack of positivity}, DOI={10.1177/09622802241269646}, abstractNote={The use of propensity score methods has become ubiquitous in causal inference. At the heart of these methods is the positivity assumption. Violation of the positivity assumption leads to the presence of extreme propensity score weights when estimating average causal effects, which affects statistical inference. To circumvent this issue, trimming or truncating methods have been widely used. Unfortunately, these methods require that we pre-specify a threshold. There are a number of alternative methods to deal with the lack of positivity when we estimate the average treatment effect (ATE). However, no other methods exist beyond trimming and truncation to deal with the same issue when the goal is to estimate the average treatment effect on the treated (ATT). In this article, we propose a propensity score weight-based alternative for the ATT, called overlap weighted average treatment effect on the treated. The appeal of our proposed method lies in its ability to obtain similar or even better results than trimming and truncation while relaxing the constraint to choose an a priori threshold (or related measures). The performance of the proposed method is illustrated via a series of Monte Carlo simulations and a data analysis on racial disparities in health care expenditures.}, journal={Statistical Methods in Medical Research}, author={Liu, Yi and Li, Huiyue and Zhou, Yunji and Matsouaka, Roland A}, year={2024}, month={Sep} }
 @article{gao_liu_matsouaka_2024, title={When does adjusting covariate under randomization help? A comparative study on current practices}, DOI={10.1186/s12874-024-02375-3}, abstractNote={{"Label"=>"PURPOSE", "NlmCategory"=>"OBJECTIVE"} We aim to thoroughly compare past and current methods that leverage baseline covariate information to estimate the average treatment effect (ATE) using data from of randomized clinical trials (RCTs). We especially focus on their performance, efficiency gain, and power. {"Label"=>"METHODS", "NlmCategory"=>"METHODS"} We compared 6 different methods using extensive Monte-Carlo simulation studies: the unadjusted estimator, i.e., analysis of variance (ANOVA), the analysis of covariance (ANCOVA), the analysis of heterogeneous covariance (ANHECOVA), the inverse probability weighting (IPW), the augmented inverse probability weighting (AIPW), and the overlap weighting (OW) as well as the augmented overlap weighting (AOW) estimators. The performance of these methods is assessed using the relative bias (RB), the root mean square error (RMSE), the model-based standard error (SE) estimation, the coverage probability (CP), and the statistical power. {"Label"=>"RESULTS", "NlmCategory"=>"RESULTS"} Even with a well-executed randomization, adjusting for baseline covariates by an appropriate method can be a good practice. When the outcome model(s) used in a covariate-adjusted method is closer to the correctly specified model(s), the efficiency and power gained can be substantial. We also found that most covariate-adjusted methods can suffer from the high-dimensional curse, i.e., when the number of covariates is relatively high compared to the sample size, they can have poor performance (along with lower efficiency) in estimating ATE. Among the different methods we compared, the OW performs the best overall with smaller RMSEs and smaller model-based SEs, which also result in higher power when the true effect is non-zero. Furthermore, the OW is more robust when dealing with the high-dimensional issue. {"Label"=>"CONCLUSION", "NlmCategory"=>"CONCLUSIONS"} To effectively use covariate adjustment methods, understanding their nature is important for practical investigators. Our study shows that outcome model misspecification and high-dimension are two main burdens in a covariate adjustment method to gain higher efficiency and power. When these factors are appropriately considered, e.g., performing some variable selections if the data dimension is high before adjusting covariate, these methods are expected to be useful.}, journal={BMC Medical Research Methodology}, author={Gao, Ying and Liu, Yi and Matsouaka, Roland}, year={2024}, month={Oct} }