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Assessing mediation in cross-sectional stepped wedge cluster randomized trials

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arXiv 2024年

作者： Cao, Zhiqiang Li, Fan Department of Mathematics College of Big Data and Internet Shenzhen Technology University Guangdong China 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 Clinical and Translational Research Accelerator Department of Medicine Yale School of Medicine New HavenCT United States

Mediation analysis has been comprehensively studied for independent data but relatively little work has been done for correlated data, especially for the increasingly adopted stepped wedge cluster randomized trials (SW-CRTs). Motivated by challenges in underlying the effect mechanisms in pragmatic and implementation science clinical trials, we develop new methods for mediation analysis in SW-CRTs. Specifically, based on a linear and generalized linear mixed models, we demonstrate how to estimate the natural indirect effect and mediation proportion in typical SW-CRTs with four data types, including both continuous and binary mediators and outcomes. Furthermore, to address the emerging challenges in exposure-time treatment effect heterogeneity, we derive the mediation expressions in SW-CRTs when the total effect varies as a function of the exposure time. The cluster jackknife approach is considered for inference across all data types and treatment effect structures. We conduct extensive simulations to evaluate the finite-sample performances of proposed mediation estimators and demonstrate the proposed approach in a real data example. A user-friendly R package mediateSWCRT has been developed to facilitate the practical implementation of the estimators. © 2024, CC BY-NC-ND.

关键词： Random processes

Doubly robust estimation and sensitivity analysis with outcomes truncated by death in multi-arm clinical trials

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arXiv 2024年

作者： Tong, Jiaqi Cheng, Chao Tong, Guangyu Harhay, Michael O. Li, Fan 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 Internal Medicine Section of Cardiovascular Medicine Yale School of Medicine New HavenCT United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

In clinical trials, the observation of participant outcomes may frequently be hindered by death, leading to ambiguity in defining a scientifically meaningful final outcome for those who die. Principal stratification methods are valuable tools for addressing the average causal effect among always-survivors, i.e., the average treatment effect among a subpopulation in the principal strata of those who would survive regardless of treatment assignment. Although robust methods for the truncation-by-death problem in two-arm clinical trials have been previously studied, its expansion to multi-arm clinical trials remains unknown. In this article, we study the identification of a class of survivor average causal effect estimands with multiple treatments under monotonicity and principal ignorability, and first propose simple weighting and regression approaches. As a further improvement, we then derive the efficient influence function to motivate doubly robust estimators for the survivor average causal effects in multi-arm clinical trials. We also articulate sensitivity methods under violations of key causal assumptions. Extensive simulations are conducted to investigate the finite-sample performance of the proposed methods, and a real data example is used to illustrate how to operationalize the proposed estimators and the sensitivity methods in practice. © 2024, CC BY-NC-ND.

关键词： Sensitivity analysis

Doubly robust estimation and sensitivity analysis for marginal structural quantile models

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arXiv 2022年

作者： Cheng, Chao Hu, Liangyuan Li, Fan 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 Biostatistics and Epidemiology Rutgers University New BrunswickNJ United States

The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of findings to unmeasured baseline and time-varying confounding. © 2022, CC BY-NC-ND.

关键词： Sensitivity analysis

Estimating the natural indirect effect and the mediation proportion via the product method

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arXiv 2021年

作者： Cheng, Chao Spiegelman, Donna Li, Fan 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 natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for estimating NIE and MP is through the product method, which involves an model for the outcome conditional on the mediator and exposure and another model describing the exposure-mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. With four common data types, we propose closed-form interval estimators via the theory of estimating equations and multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Based upon these theoretical developments and empirical studies, we offer several practical recommendations to inform practice. An R package mediateP is developed to implement the methods for point and variance estimation discussed in this paper. © 2021, CC BY-NC-ND.

关键词： Sampling

Analysis of cohort stepped wedge cluster-randomized trials with non-ignorable dropout via joint modeling

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arXiv 2024年

作者： Gasparini, Alessandro Crowther, Michael J. Hoogendijk, Emiel O. Li, Fan Harhay, Michael O. Red Door Analytics AB Stockholm Sweden Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden MRC Clinical Trials Unit University College London London United Kingdom Department of Epidemiology and Data Science Amsterdam Public Health Research Institute Amsterdam UMC – Location VU University Medical Center Amsterdam Netherlands Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine the University of Pennsylvania PA United States Department of Biostatistics Yale School of Public Health Connecticut United States Center for Methods in Implementation and Prevention Science Yale School of Public Health Connecticut United States

Stepped wedge cluster-randomized trial (CRTs) designs randomize clusters of individuals to intervention sequences, ensuring that every cluster eventually transitions from a control period to receive the intervention under study by the end of the study period. The analysis of stepped wedge CRTs is usually more complex than parallel-arm CRTs due to more complex intra-cluster correlation structures. A further challenge in the analysis of closed-cohort stepped wedge CRTs, which follow groups of individuals enrolled in each period longitudinally, is the occurrence of dropout. This is particularly problematic in studies of individuals at high risk for mortality, which causes non-ignorable missing outcomes. If not appropriately addressed, missing outcomes from death will erode statistical power, at best, and bias treatment effect estimates, at worst. Joint longitudinal-survival models can accommodate informative dropout and missingness patterns in longitudinal studies. Specifically, within the joint longitudinal-survival modeling framework, one directly models the dropout process via a time-to-event submodel together with the longitudinal outcome of interest. The two submodels are then linked using a variety of possible association structures. This work extends linear mixed-effects models by jointly modeling the dropout process to accommodate informative missing outcome data in closed-cohort stepped wedge CRTs. We focus on constant intervention and general time-on-treatment effect parametrizations for the longitudinal submodel and study the performance of the proposed methodology using Monte Carlo simulation under several data-generating scenarios. We illustrate the joint modeling methodology in practice by reanalyzing data from the ‘Frail Older Adults: Care in Transition’ (ACT) trial, a stepped wedge CRT of a multifaceted geriatric care model versus usual care in 35 primary care practices in the Netherlands. Copyright © 2024, The Authors. All rights reserved.

关键词： Monte Carlo methods

A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL

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arXiv 2022年

作者： Chen, Xinyuan Harhay, Michael O. Tong, Guangyu Li, Fan Department of Mathematics and Statistics Mississippi State University United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania United States 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

MSC Codes 62P10Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants, in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home, but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and source of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field. © 2022, CC BY.

关键词： Decision making

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures

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arXiv 2023年

作者： Ouyang, Yongdong Taljaard, Monica Forbes, Andrew B. Li, Fan Clinical Epidemiology Program Ottawa Hospital Research Institute 1053 Carling Ave OttawaON Canada School of Epidemiology and Public Health University of Ottawa 600 Peter Morand Crescent OttawaON Canada School of Public Health and Preventive Medicine Monash University Melbourne Australia 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

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials (SW-CRTs). A key consideration for analyzing a SW-CRT is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators (RVE) may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of RVE for SW-CRT. In this paper, we review six RVEs (both standard and small-sample bias-corrected RVEs) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these RVEs for SW-CRTs with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a RVE with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with RVEs performed adequately. The CR3 RVE (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for linear mixed model analysis

关键词： Random errors

Handling incomplete outcomes and covariates in cluster-randomized trials: Doubly-robust estimation, efficiency considerations, and sensitivity analysis

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arXiv 2024年

作者： Wang, Bingkai Li, Fan Wang, Rui 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 Department of Population Medicine Harvard Pilgrim Health Care Institute Harvard Medical School BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

In cluster-randomized trials (CRTs), missing data can occur in various ways, including missing values in outcomes and baseline covariates at the individual or cluster level, or completely missing information for non-participants. Among the various types of missing data in CRTs, missing outcomes have attracted the most attention. However, no existing methods can simultaneously address all aforementioned types of missing data in CRTs. To fill in this gap, we propose a new doubly-robust estimator for the average treatment effect on a variety of scales. The proposed estimator simultaneously handles missing outcomes under missingness at random, missing covariates without constraining the missingness mechanism, and missing cluster-population sizes via a uniform sampling mechanism. Furthermore, we detail key considerations to improve precision by specifying the optimal weights, leveraging machine learning, and modeling the treatment assignment mechanism. Finally, to evaluate the impact of violating missing data assumptions, we contribute a new sensitivity analysis framework tailored to CRTs. Simulation studies and a real data application both demonstrate that our proposed methods are effective in handling missing data in CRTs and superior to the existing methods. © 2024, CC BY.

关键词： Sensitivity analysis

A critical analysis of health system in Nepal: Perspectives based on COVID-19 response

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Dialogues in Health 2023年 3卷 100142-100142页

作者： Kuikel, Bihari Sharan Shrestha, Archana Xu, Dong Roman Shahi, Brish Bahadur Bhandari, Bakhat Mishra, Ravi Kanta Bhattrai, Navaraj Acharya, Kiran Timalsina, Ashish Dangaura, Nripa Raj Adhikari, Bikram Dhital, Rabin Karmacharya, Biraj Man Department of Public Health and Community Programs Kathmandu University School of Medical Sciences Dhulikhel Nepal Institute for Implementation Science and Health Kathmandu Nepal Center of Methods for Implementation and Prevention Science Yale School of Public Health New Haven CT United States Southern Medical University Guangzhou China Ministry of Social Development Karnali Province Nepal Ministry of Health and Population Kathmandu Nepal Nepal Public Health Foundation Kathmandu Nepal New ERA Kathmandu Nepal Family Welfare Division Department of Health Services Kathmandu Nepal HERD International Lalitpur Nepal Rural Development and Empowerment Center Nuwakot Nepal

Background: Nepal moved from a unitary government to a federal system of government in 2015 under its constitution. Nepal is a federal democratic republic governed by three levels of government: a federal, provincial, and local level. The response to COVID-19 in Nepal has been majorly led and controlled by the federal government. All three levels of government are performing their responsibilities;however, they face various challenges in responding to COVID-19. This study aimed to critically analyze Nepal's health system in the context of the COVID-19 response. methods: We conducted semi-structured in-depth interviews by telephone among the policymakers, health workers, and stakeholders at the federal, provincial, and local levels (n = 41) between January to July 2021. The interviews were audio recorded, transcribed into English, and coded using inductive-deductive approaches. Results: COVID-19 considerably impacted routine health care, mainly maternity services and immunization. Inadequate financial resources, inadequate human resources, and the lack of ventilators, ICUs, and X-ray services were the significant challenges in tackling and managing COVID-19 effectively. Conclusion: The study found that all three levels of government perform their roles and responsibilities and effectively manage the pandemic. The federal and provincial governments focused more on the plans and policy development, while the local government demonstrated greater accountability in implementing those plans and policies. Therefore, all three tiers of government need to coordinate together for preparing and communicating information in times of emergency. Besides, it is imperative to empower local governments to maintain Nepal's federal health system. © 2023 The Authors

关键词： COVID-19 Health system Nepal WHO health system building blocks