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arXiv 2024年

作者： Isenberg, Dane Harhay, Michael Mitra, Nandita Li, Fan Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States 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

Patient-centered outcomes, such as quality of life and length of hospital stay, are the focus in a wide array of clinical studies. However, participants in randomized trials for elderly or critically and severely ill patient populations may have truncated or undefined non-mortality outcomes if they do not survive through the measurement time point. To address truncation by death, the survivor average causal effect (SACE) has been proposed as a causally interpretable subgroup treatment effect defined under the principal stratification framework. However, the majority of methods for estimating SACE have been developed in the context of individually-randomized trials. Only limited discussions have been centered around cluster-randomized trials (CRTs), where methods typically involve strong distributional assumptions for outcome modeling. In this paper, we propose two weighting methods to estimate SACE in CRTs that obviate the need for potentially complicated outcome distribution modeling. We establish the requisite assumptions that address latent clustering effects to enable point identification of SACE, and we provide computationally-efficient asymptotic variance estimators for each weighting estimator. In simulations, we evaluate our weighting estimators, demonstrating their finite-sample operating characteristics and robustness to certain departures from the identification assumptions. We illustrate our methods using data from a CRT to assess the impact of a sedation protocol on mechanical ventilation among children with acute respiratory failure. Copyright © 2024, The Authors. All rights reserved.

关键词： Sampling

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A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding

arXiv

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arXiv 2024年

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

In longitudinal observational studies with time-to-event outcomes, a common objective in causal analysis is to estimate the causal survival curve under hypothetical intervention scenarios. The g-formula is a useful tool for this analysis. To enhance the traditional parametric g-formula, we developed an alternative g-formula estimator, which incorporates the Bayesian Additive Regression Trees (BART) into the modeling of the time-evolving generative components, aiming to mitigate the bias due to model misspecification. We focus on binary time-varying treatments and introduce a general class of g-formulas for discrete survival data that can incorporate the longitudinal balancing scores. The minimum sufficient formulation of these longitudinal balancing scores is linked to the nature of treatment strategies, i.e., static or dynamic. For each type of treatment strategy, we provide posterior sampling algorithms. We conducted simulations to illustrate the empirical performance of the proposed method and demonstrate its practical utility using data from the Yale New Haven Health System’s electronic health records. © 2024, CC BY.

关键词： Electronic health record

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Asymptotic inference with flexible covariate adjustment under rerandomization and stratified rerandomization

arXiv

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arXiv 2024年

作者： Wang, Bingkai Li, Fan Department of Biostatistics School of Public Health University of Michigan Ann ArborMI United States 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

Rerandomization is an effective treatment allocation procedure to control for baseline covariate imbalance. For estimating the average treatment effect, rerandomization has been previously shown to improve the precision of the unadjusted and the linearly-adjusted estimators over simple randomization without compromising consistency. However, it remains unclear whether such results apply more generally to the class of M-estimators, including the g-computation formula with generalized linear regression and doubly-robust methods, and more broadly, to efficient estimators with data-adaptive machine learners. In this paper, using a super-population framework, we develop the asymptotic theory for a more general class of covariate-adjusted estimators under rerandomization and its stratified extension. We prove that the asymptotic linearity and the influence function remain identical for any M-estimator under simple randomization and rerandomization, but rerandomization may lead to a non-Gaussian asymptotic distribution. We further explain, drawing examples from several common M-estimators, that asymptotic normality can be achieved if rerandomization variables are appropriately adjusted for in the final estimator. These results are extended to stratified rerandomization. Finally, we study the asymptotic theory for efficient estimators based on data-adaptive machine learners, and prove their efficiency optimality under rerandomization and stratified rerandomization. Our results are demonstrated via simulations and re-analyses of a cluster-randomized experiment that used stratified rerandomization. © 2024, CC BY.

关键词： Machine learning

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Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations

arXiv

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arXiv 2024年

作者： Wang, Bingkai Li, Fan Yu, Mengxin Department of Biostatistics University of Michigan Ann ArborMI United States 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 Department of Statistics and Data Science University of Pennsylvania PhiladelphiaPA United States

Traditional statistical inference in cluster randomized trials typically invokes the asymptotic theory that requires the number of clusters to approach infinity. In this article, we propose an alternative conformal causal inference framework for analyzing cluster randomized trials that achieves the target inferential goal in finite samples without the need for asymptotic approximations. Different from traditional inference focusing on estimating the average treatment effect, our conformal causal inference aims to provide prediction intervals for the difference of counterfactual outcomes, thereby providing a new decision-making tool for clusters and individuals in the same target population. We prove that this framework is compatible with arbitrary working outcome models—including data-adaptive machine learning methods that maximally leverage information from baseline covariates, and enjoys robustness against misspecification of working outcome models. Under our conformal causal inference framework, we develop efficient computation algorithms to construct prediction intervals for treatment effects at both the cluster and individual levels, and further extend to address inferential targets defined based on pre-specified covariate subgroups. Finally, we demonstrate the properties of our methods via simulations and a real data application based on a completed cluster randomized trial for treating chronic pain. © 2024, CC BY.

关键词： Prediction models

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Model-robust and efficient covariate adjustment for cluster-randomized experiments

arXiv

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arXiv 2022年

作者： Wang, Bingkai Park, Chan Small, Dylan S. Li, Fan The Statistics and Data Science Department The Wharton School University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Center for Methods in Implementation and Prevention Science Yale University School of Public Health New HavenCT United States

Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment is unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two conventional model-based methods, generalized estimating equations and linear mixed models, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. For our proposed efficient covariate-adjusted estimator, we prove that when the nuisance functions are consistently estimated by machine learning algorithms, the estimator is consistent, asymptotically normal, and efficient. When the nuisance functions are estimated via parametric working models, the estimator is triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives. © 2022, CC BY.

关键词： Cluster analysis

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Moving toward best practice when using propensity score weighting in survey observational studies

arXiv

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arXiv 2025年

作者： Zeng, Yukang Li, Fan Tong, Guangyu Department of Biostatistics Yale School of Public Health New HavenCT United States Cardiovascular Medicine Analytics Center Yale School of Medicine New HavenCT United States Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New HavenCT United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New HavenCT United States

Propensity score weighting is a common method for estimating treatment effects with survey data. The method is applied to minimize confounding using measured covariates that are often different between individuals in treatment and control. However, existing literature does not reach a consensus on the optimal use of survey weights for population-level inference in the propensity score weighting analysis. Under the balancing weights framework, we provided a unified solution for incorporating survey weights in both the propensity score of estimation and the outcome regression model. We derived estimators for different target populations, including the combined, treated, controlled, and overlap populations. We provide a unified expression of the sandwich variance estimator and demonstrate that the survey-weighted estimator is asymptotically normal, as established through the theory of M-estimators. Through an extensive series of simulation studies, we examined the performance of our derived estimators and compared the results to those of alternative methods. We further carried out two case studies to illustrate the application of the different methods of propensity score analysis with complex survey data. We concluded with a discussion of our findings and provided practical guidelines for propensity score weighting analysis of observational data from complex surveys. © 2025, CC BY.

关键词： Regression analysis

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Power and Sample Size Calculations for Cluster Randomized Hybrid Type 2 Effectiveness-implementation Studies

arXiv

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arXiv 2024年

作者： Owen, Melody A. Curran, Geoffrey M. Smith, Justin D. Tedla, Yacob Cheng, Chao Spiegelman, Donna Center for Methods in Implementation and Prevention Science Yale University New HavenCT United States Department of Psychiatry University of Arkansas for Medical Sciences Little RockAR United States Population Health Sciences University of Utah School of Medicine Salt Lake CityUT United States Division of Epidemiology Department of Medicine Vanderbilt University Medical Center NashvilleTN United States

Hybrid studies allow investigators to simultaneously study an intervention effectiveness outcome and an implementation research outcome. In particular, type 2 hybrid studies support research that places equal importance on both outcomes rather than focusing on one and secondarily on the other (i.e., type 1 and type 3 studies). Hybrid 2 studies introduce the statistical issue of multiple testing, complicated by the fact that they are typically also cluster randomized trials. Standard statistical methods do not apply in this scenario. Here, we describe the design methodologies available for validly powering hybrid type 2 studies and producing reliable sample size calculations in a cluster-randomized design with a focus on binary outcomes. Through a literature search, 18 publications were identified that included methods relevant to the design of hybrid 2 studies. Five methods were identified, two of which did not account for clustering but are extended in this article to do so, namely the combined outcomes approach and the single 1-degree of freedom combined test. Procedures for powering hybrid 2 studies using these five methods are described and illustrated using input parameters inspired by a study from the Community Intervention to Reduce CardiovascuLar Disease in Chicago (CIRCL-Chicago) implementation Research center. In this illustrative example, the intervention effectiveness outcome was controlled blood pressure, and the implementation outcome was reach. The conjunctive test resulted in higher power than the popular p-value adjustment methods, and the newly extended combined outcomes and single 1-DF test were found to be the most powerful among all of the tests. © 2024, CC BY-NC-ND.

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Causal Covariate Selection for the Imputation-based Regression Calibration Method for Exposure Measurement Error Bias Correction

arXiv

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arXiv 2023年

作者： Tang, Wenze Spiegelman, Donna Wu, Yujie Wang, Molin Department of Epidemiology Harvard School of Public Health MA United States Department of Biostatistics Yale School of Public Health CT United States Center on Methods for Implementation and Prevention Science Yale School of Public Health CT United States Department of Biostatistics Harvard School of Public Health MA United States

In this paper, we investigated the selection of the minimal and most efficient covariate adjustment sets for regression calibration method developed by Carroll, Rupert and Stefanski (CRS, 1992), correcting for bias due to continuous exposure measurement error. We utilized directed acyclic graph to illustrate how subject matter knowledge can aid in the selection of such adjustment sets. Unbiased measurement error correction requires the collection of the following variable sets: (I) common causes of true exposure and outcome and (II) common causes of measurement error and outcome, in both the main study and validation study. For CRS regression calibration method under linear models, researchers need to minimally adjust for covariate set (I) in both measurement error model (MEM) and outcome model and adjust for covariate set (II) at least in MEM. In contrast to the regression calibration method developed by Rosner, Spiegelman and Willet, adjusting for non-risk factors that are correlates of the true exposure or of measurement error in the MEM only is valid and increases efficiency under CRS method. We applied the proposed covariate selection approach to the Health Professional Follow-up Study of the effect of fiber intake on cardiovascular disease. In this study, we also demonstrated the potential issue with data-driven approach to build the MEM that is agnostic to the structural assumptions. We extend the originally proposed estimators to settings where effect modification by a covariate is allowed. Finally, we caution against the use of the regression calibration method to calibrate the true nutrition intake using biomarkers. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Design of egocentric network-based studies to estimate causal effects under interference

arXiv

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arXiv 2023年

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

Many public health interventions are conducted in settings where individuals are connected to one another and the intervention assigned to randomly selected individuals may spill over to other individuals they are connected to. In these spillover settings, the effects of such interventions can be quantified in several ways. The average individual effect measures the intervention effect among those directly treated, while the spillover effect measures the effect among those connected to those directly treated. In addition, the overall effect measures the average intervention effect across the study population, over those directly treated along with those to whom the intervention spills over but who are not directly treated. Here, we develop methods for study design with the aim of estimating individual, spillover, and overall effects. In particular, we consider an egocentric network-based randomized design in which a set of index participants is recruited from the population and randomly assigned to treatment, while data are also collected from their untreated network members. We use the potential outcomes framework to define two clustered regression modeling approaches and clarify the underlying assumptions required to identify and estimate causal effects. We then develop sample size formulas for detecting individual, spillover, and overall effects. We investigate the roles of the intra-class correlation coefficient and the probability of treatment allocation on the required number of egocentric networks with a fixed number of network members for each egocentric network and vice-versa. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics

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Semiparametric principal stratification analysis beyond monotonicity

arXiv

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arXiv 2025年

作者： Tong, Jiaqi Kahan, Brennan Harhay, Michael O. Li, Fan Department of Biostatistics Yale University of Public Health New HavenCT United States MRC Clinical Trials Unit at UCL Institute of Clinical Trials and Methodology London United Kingdom Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New HavenCT United States

Intercurrent events, common in clinical trials and observational studies, affect the existence or interpretation of final outcomes. Principal stratification addresses these challenges by defining local average treatment effects within latent subpopulations, but often relies on restrictive assumptions such as monotonicity and counterfactual intermediate independence. To address these limitations, we propose a unified semiparametric framework for principal stratification analysis leveraging a margin-free, conditional odds ratio sensitivity parameter. Under principal ignorability, we derive nonparametric identification formulas and develop efficient estimation methods, including a conditionally doubly robust parametric estimator and a de-biased machine learning estimator with data-adaptive nuisance estimators. Simulations show that incorrectly assuming monotonicity can often lead to suboptimal inference, while specifying non-trivial odds ratio sensitivity parameter can enable approximately valid inference under monotonicity. We apply our methods to a critical care trial and further suggest a semiparametric sensitivity analysis approach under violation of principal ignorability. © 2025, CC BY-NC-SA.

关键词： Sensitivity analysis

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