Tuesday, Aug 5: 8:30 AM - 10:20 AM
0725
Topic-Contributed Paper Session
Music City Center
Room: CC-202B
Health policy
Applied
Yes
Main Sponsor
Health Policy Statistics Section
Presentations
Respondent-driven sampling (RDS) is widely used to study hidden or hard-to-reach populations by incentivizing study participants to recruit their social connections. The success and efficiency of RDS can depend critically on the nature of the incentives including their number, value, call to action, etc. Standard RDS uses an incentive structure that is set a priori and held fixed throughout the study and thus does not make use of accumulating information on which incentives are effective and for whom. We propose a reinforcement learning (RL) based adaptive RDS study design in which the incentives are tailored over time to maximize cumulative utility during the study. We show that these designs are more efficient, cost-effective, and can generate new insights into the social structure of hidden populations. In addition, we develop methods for valid post-study inference which are non-trivial due to the adaptive sampling induced by RL as well as the complex dependencies among subjects due to latent (unobserved) social network structure. We provide asymptotic regret bounds and illustrate its finite sample behavior through a suite of simulation experiments.
Keywords
Respondent-driven sampling, Reinforcement learning, Branching processes
Estimating treatment effects using observation data often relies on the assumption of no unmeasured confounders. However, unmeasured confounding variables may exist in many real-world problems. It can lead to a biased estimation without incorporating the unmeasured confounding effect. To address this problem, this paper proposes a new mixed-effects joint modeling approach to identifying and estimating the outcome regression function and the propensity score function in the presence of unmeasured confounders in clustered data. As a result, we can obtain the estimators of the average treatment effect and heterogeneous treatment effects. In our proposed setting, we allow interaction effects of the treatment and unmeasured confounders on the outcome. We also allow the joint models for the outcome regression and propensity score functions to depend on the same unmeasured confounders. Moreover, we propose a new Laplacian-variant EM algorithm to estimate the parameters in the joint models. We apply the method to a real-world application from the CitieS-Health Barcelona Panel Study, in which we study the effect of short-term air pollution exposure on mental health.
Keywords
Treatment effect; Unmeasured confounding variable; No unmeasured confounding assumption; EM algorithm; Laplace Approximation; Metal health and air pollution
Co-Author(s)
Namhwa Lee, University of California- Riverside
Guanyu Hu, The University of Texas Health Science Center at Houston
Shujie Ma, UC Riverside
Speaker
Namhwa Lee, University of California- Riverside
Usual parametric and semi-parametric regression methods are inappropriate and inadequate for large clustered survival studies when the appropriate functional forms of the covariates and their interactions in hazard functions are unknown, and random cluster effects as well as some unknown cluster-level covariates are spatially correlated. We present a general nonparametric method for such studies using Soft Bayesian Additive Regression Trees (SBART). Our additional methodological and computational challenges include large number of clusters, variable cluster sizes, and proper statistical augmentation of the unobservable cluster-level covariate using a data registry different from the main survival study. We use an innovative 3-step tool based on latent variables to address our computational challenges.
We illustrate the practical implementation of our method by assessing the impacts of intervention in some cluster/county level and patient-level covariates to mitigate existing racial disparity in breast cancer survival in 67 Florida counties (clusters) using two different data resources.
We also compare our method with existing analysis methods through simulation studies.
Keywords
Spatial survival analysis; Multiple data sources integration; Soft Bayesian Additive Regression Trees (SBART); Racial disparities; Life-Years Saved.
Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of other units, i.e., there is interference. In some settings, units may be grouped into clusters such that it is reasonable to assume interference only occurs within clusters, i.e., there is clustered interference. In this paper, methods are developed which can accommodate confounding, censored outcomes, and clustered interference. The approach avoids parametric assumptions and permits inference about counterfactual scenarios corresponding to any stochastic policy which modifies the propensity score distribution, and thus may have application across diverse settings. The proposed nonparametric sample splitting estimators allow for flexible data-adaptive estimation of nuisance functions and are consistent and asymptotically normal with parametric convergence rates. Simulation studies demonstrate the finite sample performance of the proposed estimators, and the methods are applied to a cholera vaccine study in Bangladesh.
Keywords
Causal inference
Observational study
Partial interference
Right censoring
Stochastic policy
Treatment effect
Causal mediation analysis provides insights into the mechanisms through which treatments affect outcomes. While mediation effects often vary across individuals, most existing methods focus solely on population-average effects, overlooking individual-level heterogeneity. To address this limitation, we propose a Bayesian regression tree ensemble method that flexibly models non-linear relationships and captures treatment-by-mediator interactions in the mediation process. Using hierarchical posterior sampling, our approach provides credible intervals with nominal coverage rates for testing heterogeneous mediation effects. Additionally, we leverage regression tree summaries to identify subgroups with distinct mediation effects and employ SHapley Additive exPlanation (SHAP) values to highlight key moderators and their influence on the mediation process. Comprehensive simulations demonstrate the method's accuracy in estimating and inferring heterogeneous mediation effects. Finally, we apply our method to investigate the heterogeneous mediation of Alzheimer's disease pathology burden on the effect of apolipoprotein E (APOE) genotype on late-life cognition.
Keywords
Causal Mediation Analysis; Bayesian Tree Ensembles; Heterogeneous Effects; Non-linear Interactions; Moderation Mechanisms.