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Semiparametric causal mediation analysis in cluster-randomized experiments

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arXiv 2024年

作者： Cheng, Chao Li, Fan Department of Biostatistics Yale School of Public Health United States Center for Methods in Implementation and Prevention Science Yale School of Public Health United States

In cluster-randomized experiments, there is emerging interest in exploring the causal mechanism in which a cluster-level treatment affects the outcome through an intermediate outcome. Despite an extensive development of causal mediation methods in the past decade, only a few exceptions have been considered in assessing causal mediation in cluster-randomized studies, all of which depend on parametric model-based estimators. In this article, we develop the formal semiparametric efficiency theory to motivate several doubly-robust methods for addressing several mediation effect estimands corresponding to both the cluster-average and the individual-level treatment effects in cluster-randomized experiments—the natural indirect effect, natural direct effect, and spillover mediation effect. We derive the efficient influence function for each mediation effect, and carefully parameterize each efficient influence function to motivate practical strategies for operationalizing each estimator. We consider both parametric working models and data-adaptive machine learners to estimate the nuisance functions, and obtain semiparametric efficient causal mediation estimators in the latter case. Our methods are illustrated via extensive simulations and two completed cluster-randomized experiments. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

Identification and multiply robust estimation in causal mediation analysis across principal strata

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arXiv 2023年

We consider assessing causal mediation in the presence of a post-treatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the post-treatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply robust estimators are consistent under four types of misspecifications and are efficient when all nuisance models are correctly specified. We also develop a nonparametric efficient estimator that leverages data-adaptive machine learners to achieve efficient inference and discuss sensitivity methods to address key identification assumptions. We illustrate our methods via simulations and two real data examples. © 2023, CC BY-NC-ND.

关键词： Adversarial machine learning

Model-assisted inference for dynamic causal effects in staggered rollout cluster randomized experiments

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arXiv 2025年

作者： Chen, Xinyuan Li, Fan Department of Mathematics and Statistics Mississippi State University MS United States Department of Biostatistics Yale School of Public Health CT United States Center for Methods in Implementation and Prevention Science Yale School of Public Health CT United States

Staggered rollout cluster randomized experiments (SR-CREs) are increasingly used for their practical feasibility and logistical convenience. These designs involve staggered treatment adoption across clusters, requiring analysis methods that account for an exhaustive class of dynamic causal effects, anticipation, and non-ignorable cluster-period sizes. Without imposing outcome modeling assumptions, we study regression estimators using individual data, cluster-period averages, and scaled cluster-period totals, with and without covariate adjustment from a design-based perspective, where only the treatment adoption time is random. We establish consistency and asymptotic normality of each regression estimator under a finite-population framework and formally prove that the associated variance estimators are asymptotically conservative in the Löwner ordering. Furthermore, we conduct a unified efficiency comparison of the estimators and provide practical recommendations. We highlight the efficiency advantage of using estimators based on scaled cluster-period totals with covariate adjustment over their counterparts using individual-level data and cluster-period averages. Our results rigorously justify linear regression estimators as model-assisted methods to address an entire class of dynamic causal effects in SR-CREs and significantly expand those developed for parallel-arm CREs by Su and Ding (2021) to accommodate a wider class of complex experimental settings with staggered randomization. © 2025, CC BY.

关键词： Cluster analysis

Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome

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arXiv 2023年

作者： Cheng, Chao Spiegelman, Donna Li, Fan Department of Biostatistics Yale University School of Public Health New HavenCT United States Center for Methods in Implementation and Prevention Science Yale University New HavenCT United States

Mediation analysis is widely used in health science research to evaluate the extent to which an intermediate variable explains an observed exposure-outcome relationship. However, the validity of analysis can be compromised when the exposure is measured with error. Motivated by the Health Professionals Follow-up Study (HPFS), we investigate the impact of exposure measurement error on assessing mediation with a survival outcome, based on the Cox proportional hazards outcome model. When the outcome is rare and there is no exposure-mediator interaction, we show that the uncorrected estimators of the natural indirect and direct effects can be biased into either direction, but the uncorrected estimator of the mediation proportion is approximately unbiased as long as the measurement error is not large or the mediator-exposure association is not strong. We develop ordinary regression calibration and risk set regression calibration approaches to correct the exposure measurement error-induced bias when estimating mediation effects and allowing for an exposure-mediator interaction in the Cox outcome model. The proposed approaches require a validation study to characterize the measurement error process. We apply the proposed approaches to the HPFS (1986–2016) to evaluate extent to which reduced body mass index mediates the protective effect of vigorous physical activity on the risk of cardiovascular diseases, and compare the finite-sample properties of the proposed estimators via simulations. © 2023, CC BY-NC-ND.

关键词： Calibration

Principal stratification with U-statistics under principal ignorability

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arXiv 2024年

Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects have traditionally been the default target of inference, it may not be sufficient when the interest lies in the relative favorability of one potential outcome over the other within the principal stratum. We thus introduce the principal generalized causal effect estimands, which extend the principal average causal effects to accommodate nonlinear contrast functions. Under principal ignorability, we expand the theoretical results in Jiang et al. (2022) to a much wider class of causal estimands in the presence of a binary intermediate variable. We develop identification formulas and derive the efficient influence functions of the generalized estimands for principal stratification analyses. These efficient influence functions motivate a set of multiply robust estimators and lay the ground for obtaining efficient debiased machine learning estimators via cross-fitting based on U-statistics. The proposed methods are illustrated through simulations and the analysis of a data example. © 2024, CC BY.

关键词： Statistics

Identifying Key Influencers using an Egocentric Network-based Randomized Design

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arXiv 2025年

作者： He, Zhibing Fan, Junhan Buchanan, Ashley Spiegelman, Donna Forastiere, Laura Department of Biostatistics Yale University New Haven United States Center for Methods in Implementation and Prevention Science Yale University New Haven United States Roche Canada Mississauga Canada Department of Pharmacy Practice and Clinical Research University of Rhode Island United States

Behavioral health interventions, such as trainings or incentives, are implemented in settings where individuals are interconnected, and the intervention assigned to some individuals may also affect others within their network. Evaluating such interventions requires assessing both the effect of the intervention on those who receive it and the spillover effect on those connected to the treated individuals. With behavioral interventions, spillover effects can be heterogeneous in that certain individuals, due to their social connectedness and individual characteristics, are more likely to respond to the intervention and influence their peers’ behaviors. Targeting these individuals can enhance the effectiveness of interventions in the population. In this paper, we focus on an Egocentric Network-based Randomized Trial (ENRT) design, wherein a set of index participants is recruited from the population and randomly assigned to the treatment group, while concurrently collecting outcome data on their nominated network members, who remina untreated. In such design, spillover effects on network members may vary depending on the characteristics of the index participant. Here, we develop a testing method, the Multiple Comparison with Best (MCB), to identify subgroups of index participants whose treatment exhibits the largest spillover effect on their network members. Power and sample size calculations are then provided to design ENRTs that can detect key influencers. The proposed methods are demonstrated in a study on network-based peer HIV prevention education program, providing insights into strategies for selecting peer educators in peer education interventions. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity

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arXiv 2023年

作者： Ryan, Mary M. Esserman, Denise Li, Fan Department of Biostatistics Yale School of Public Health Connecticut United States Yale Center for Analytical Sciences Yale School of Public Health Connecticut United States Center for Methods in Implementation and Prevention Science Yale School of Public Health Connecticut United States

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters. © 2023, CC BY-NC-ND.

关键词： Application programs

How should parallel cluster randomized trials with a baseline period be analyzed? A survey of estimands and common estimators

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arXiv 2024年

作者： Lee, Kenneth Menglin Li, Fan Centre for Quantitative Medicine Duke-NUS Medical School 169857 Singapore Department of Biostatistics Yale School of Public Health New HavenCT United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New HavenCT United States

The parallel cluster randomized trial with baseline (PB-CRT) is a common variant of the standard parallel cluster randomized trial (P-CRT). We define two natural estimands in the context of PB-CRTs with informative cluster sizes, the participant-average treatment effect (pATE) and cluster-average treatment effect (cATE), to address participant and cluster-level hypotheses. In this work, we theoretically derive the convergence of the unweighted and inverse cluster-period size weighted (i.) independence estimating equation, (ii.) fixed-effects model, (iii.) exchangeable mixed-effects model, and (iv.) nested-exchangeable mixed-effects model treatment effect estimators in a PB-CRT with continuous outcomes. Overall, we theoretically show that the unweighted and weighted independence estimating equation and fixed-effects model yield consistent estimators for the pATE and cATE estimands. Although mixed-effects models yield inconsistent estimators to these two natural estimands under informative cluster sizes, we empirically demonstrate that the exchangeable mixed-effects model is surprisingly robust to bias. This is in sharp contrast to the corresponding analyses in P-CRTs and the nested-exchangeable mixed-effects model in PB-CRTs, and may carry implications for practice. We report a simulation study and conclude with a re-analysis of a PB-CRT examining the effects of community youth teams on improving mental health among adolescent girls in rural eastern India. © 2024, CC BY.

关键词： Inverse problems

Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification

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arXiv 2023年

作者： Chao, Ariel Spiegelman, Donna Buchanan, Ashley Forastiere, Laura Department of Biostatistics Yale School of Public Health United States Center for Methods in Implementation and Prevention Science Yale School of Public Health United States Department of Pharmacy Practice College of Pharmacy the University of Rhode Island United States

To leverage peer influence and increase population behavioral changes, behavioral interventions often rely on peer-based strategies. A common study design that assesses such strategies is the egocentric-network randomized trial (ENRT), in which those receiving the intervention are encouraged to disseminate information to their peers. The Average Spillover Effect (ASpE) measures the impact of the intervention on participants who do not receive it, but whose outcomes may be affected by others who do. The assessment of the ASpE relies on assumptions about, and correct measurement of, interference sets within which individuals may influence one another’s outcomes. It can be challenging to properly specify interference sets, such as networks in ENRTs, and when mismeasured, intervention effects estimated by existing methods will be biased. In HIV prevention studies where social networks play an important role in disease transmission, correcting ASpE estimates for bias due to network misclassification is critical for accurately evaluating the full impact of interventions. We combined measurement error and causal inference methods to bias-correct the ASpE estimate for network misclassification in ENRTs, when surrogate networks are recorded in place of true ones, and validation data that relate the misclassified to the true networks are available. We investigated finite sample properties of our methods in an extensive simulation study, and illustrated our methods in the HIV prevention Trials Network (HPTN) 037 study. Copyright © 2023, The Authors. All rights reserved.

关键词： Information dissemination