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arXiv 2023年

作者： Wu, Wenbo Li, Fan Liu, Richard Li, Yiting McAdams-DeMarco, Mara Geras, Krzysztof J. Schaubel, Douglas E. Díaz, Iván Division of Biostatistics Department of Population Health NYU Grossman School of Medicine United States Division of Nephrology Department of Medicine NYU Grossman School of Medicine United States Center for Data Science New York University United States 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 Surgery NYU Grossman School of Medicine United States Division of Epidemiology Department of Population Health NYU Grossman School of Medicine United States Department of Radiology NYU Grossman School of Medicine United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine United States

Encompassing numerous nationwide, statewide, and institutional initiatives in the United States, provider profiling has evolved into a major health care undertaking with ubiquitous applications, profound implications, and high-stakes consequences. In line with such a significant profile, the literature has accumulated a number of developments dedicated to enhancing the statistical paradigm of provider profiling. Tackling wide-ranging profiling issues, these methods typically adjust for risk factors using linear predictors. While this approach is simple, it can be too restrictive to characterize complex and dynamic factor-outcome associations in certain contexts. One such example arises from evaluating dialysis facilities treating Medicare beneficiaries with end-stage renal disease. It is of primary interest to consider how the coronavirus disease (COVID-19) affected 30-day unplanned readmissions in 2020. The impact of COVID-19 on the risk of readmission varied dramatically across pandemic phases. To efficiently capture the variation while profiling facilities, we develop a generalized partially linear model (GPLM) that incorporates a neural network. Considering provider-level clustering, we implement the GPLM as a stratified sampling-based stochastic optimization algorithm that features accelerated convergence. Furthermore, an exact test is designed to identify under- and over-performing facilities, with an accompanying funnel plot to visualize profiles. The advantages of the proposed methods are demonstrated through simulation experiments and profiling dialysis facilities using 2020 Medicare claims from the United States Renal Data System. © 2023, CC BY.

关键词： Optimization

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Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome

arXiv

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arXiv 2022年

作者： Zhu, Angela Y. Mitra, Nandita Hemming, Karla Harhay, Michael O. Li, Fan Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA19104 United States Department of Public Health Epidemiology and Biostatistics University of Birmingham Institute of Applied Health Research BirminghamB15 2TT United Kingdom Department of Biostatistics Yale School of Public Health New HavenCT06510 United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New HavenCT06510 United States

Cluster-randomized trials (CRTs) involve randomizing entire groups of participants—called clusters—to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually-randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units. Copyright © 2022, The Authors. All rights reserved.

关键词： Intensive care units

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Constrained randomization and statistical inference for multi-arm parallel cluster randomized controlled trials

arXiv

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arXiv 2021年

作者： Zhou, Yunji Turner, Elizabeth L. Simmons, Ryan A. Li, Fan Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Duke Global Health Institute Duke University DurhamNC 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

A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi-arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. Randomization-based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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A Bayesian Approach to the G-Formula via Iterative Conditional Regression

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Statistics in medicine 2025年第13-14期44卷 e70123页

作者： Ruyi Liu Liangyuan Hu Francis Perry Wilson Joshua L Warren Fan Li Department of Biostatistics Yale School of Public Health New Haven CT USA. Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven CT USA. Department of Biostatistics and Epidemiology Rutgers University New Brunswick NJ USA. Clinical and Translational Research Accelerator Yale School of Medicine New Haven CT USA.

In longitudinal observational studies with time-varying confounders, the generalized computation algorithm formula (g-formula) is a principled tool to estimate the average causal effect of a treatment regimen. However, the standard non-iterative g-formula implementation requires specifying both the conditional distribution of the outcomes and the joint distribution of all time-varying covariates. This process can be cumbersome to implement and is prone to model misspecification bias. As an alternative, the iterative conditional expectation (ICE) g-formula estimator solely depends on a series of nested outcome regressions and avoids the need for specifying the full distribution of all time-varying covariates. This simplicity lends itself to the natural integration of flexible machine learning techniques to develop more robust average causal effect estimators with time-varying treatments. In this work, we introduce a Bayesian approach that includes parametric regressions and Bayesian Additive Regression Trees to flexibly model a series of outcome surfaces. We fit the ICE g-formula and develop a sampling algorithm to obtain samples from the posterior distribution of the final causal effect estimator. We illustrate the performance characteristics of the Bayesian ICE estimator and the associated variations via simulation studies and applications to two real world data examples.

关键词： Bayesian additive regression trees causal inference g‐formula longitudinal propensity score observational studies time‐varying confounding

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Correction: Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity

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BMC medical research methodology 2023年第1期23卷 135页

作者： Jiaqi Tong Fan Li Michael O Harhay Guangyu Tong Department of Biostatistics Yale School of Public Health 135 College Street New Haven CT 06510 USA. Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven CT USA. Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Biostatistics Yale School of Public Health 135 College Street New Haven CT 06510 USA. guangyu.tong@yale.edu. Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven CT USA. guangyu.tong@yale.edu.

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Prevalence and factors associated with depression, anxiety, and stress symptoms among home isolated COVID-19 patients in Western Nepal

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Dialogues in Health 2023年 2卷 100090-100090页

作者： Adhikari, Bikram Poudel, Lisasha Thapa, Tek Bahadur Neupane, Deekshya Maharjan, Pranita Hagaman, Ashley Bhandari, Niroj Katuwal, Nishan Shrestha, Bhawana Maharjan, Rashmi Shrestha, Sudip Shrestha, Akina Tamrakar, Dipesh Rajbhandari, Bibek Shahi, Brish Bahadur Shrestha, Rajeev Karmacharya, Biraj Man Shrestha, Archana Research and Development Division Dhulikhel Hospital Kathmandu University Hospital Dhulikhel Nepal Social and Behavioral Sciences Department Yale School of Public Health New Haven United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven CT United States Institute for Implementation Science and Health Nepal Department of Community Programs Dhulikhel Hospital-Kathmandu University Hospital Dhulikhel Nepal Department of Nursing and Midwifery Kathmandu University School of Medical Sciences Dhulikhel Nepal Department of Public Health Kathmandu University School of Medical Sciences Dhulikhel Nepal Department of Emergency Medicine and General Practice Nepal police Hospital Kathmandu Nepal Ministry of Social Development Karnali Province Nepal Department of Pharmacology Kathmandu University School of Medical Sciences Dhulikhel Nepal Department of Chronic Disease Epidemiology Yale School of Public Health New Haven United States

Introduction: Globally, COVID-19 pandemic has a significant impact on mental health. In Nepal, COVID-19 positive cases have to self-isolate at home in multi-generational and multi-family households. This could be strongly associated with depression, anxiety, and stress-related health outcomes. Additionally, COVID-19 related stigma and fear of transmission may intensify depression, anxiety, and stress symptoms. This study determined the prevalence of depression, anxiety, and stress symptoms and their association with presence of COVID-19 symptoms and comorbid conditions among home isolated COVID-19 positives in the Karnali province, Nepal. methods: We conducted a cross-sectional study to assess depression, anxiety, and stress symptoms among 402 home isolated COVID-19 patients of Karnali province from January to May 2021 using “Depression, Anxiety and Stress Scale-21 (DASS-21)”. We interviewed patients to collect socio-demographic, DASS-21, COVID-19 symptoms, comorbid conditions, and self-treatment. We conducted a telephonic interview using a standardized questionnaire using Kobotoolbox. We calculated the prevalence of depression, anxiety, and stress symptoms. We utilized univariate and multivariate logistic regression to determine their association with the presence of COVID-19 symptoms and comorbid conditions. In multivariate logistic regression, we adjusted sociodemographic factors (age, gender, ethnicity, marital status, monthly family income, education level), smoking status and history of self-treatment. We reported adjusted odds ratios (aOR) with 95% confidence intervals. All analyses were conducted in R (version: 4.0.3). Results: The prevalence of depression, anxiety and stress symptoms among home isolated COVID-19 patients were 8.0% (95% CI: 5.5 to 11.1), 11.2% (95% CI: 8.3 to 14.7), and 4.0% (95% CI: 2.3 to 6.4) respectively. Higher odds of depression symptoms (aOR: 2.86;95% CI: 1.10–7.44, p = 0.03), anxiety symptoms (aOR: 3.81;95% CI: 1.62 to 8.93;p = <0.01

关键词： Anxiety symptoms COVID-19 Depression symptoms Home isolation Nepal Stress symptoms

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Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights

arXiv

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arXiv 2021年

作者： Cheng, Chao Li, Fan Thomas, Laine E. 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 Statistical Science Duke University DurhamNC United States Duke Clinical Research Institute DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States

The inverse probability weighting approach is popular for evaluating treatment effects in observational studies, but extreme propensity scores could bias the estimator and induce excessive variance. Recently, the overlap weighting approach has been proposed to alleviate this problem, which smoothly down-weighs the subjects with extreme propensity scores. Although advantages of overlap weighting have been extensively demonstrated in literature with continuous and binary outcomes, research on its performance with time-to-event or survival outcomes is limited. In this article, we propose two weighting estimators that combine propensity score weighting and inverse probability of censoring weighting to estimate the counterfactual survival functions. These estimators are applicable to the general class of balancing weights, which includes inverse probability weighting, trimming, and overlap weighting as special cases. We conduct simulations to examine the empirical performance of these estimators with different weighting schemes in terms of bias, variance, and 95% confidence interval coverage, under various degree of covariate overlap between treatment groups and censoring rate. We demonstrate that overlap weighting consistently outperforms inverse probability weighting and associated trimming methods in bias, variance, and coverage for time-to-event outcomes, and the advantages increase as the degree of covariate overlap between the treatment groups decreases. © 2021, CC BY-NC-ND.

关键词： Trimming

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A tutorial on conducting sample size and power calculations for detecting treatment effect heterogeneity in cluster randomized trials

arXiv

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arXiv 2025年

作者： Baumann, Mary Ryan Taljaard, Monica Heagerty, Patrick J. Harhay, Michael O. Tong, Guangyu Wang, Rui Li, Fan Department of Population Health Sciences University of Wisconsin-Madison MadisonWI United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Methodological and Implementation Research Ottawa Hospital Research Institute OttawaON Canada School of Epidemiology and Public Health University of Ottawa OttawaON Canada Department of Biostatistics School of Public Health University of Washington SeattleWA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New HavenCT 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 BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Yale Center of Analytical Sciences Yale University New HavenCT United States Department of Population Health Sciences University of Wisconsin-Madison WARF Office Building 610 Walnut Street MadisonWI53726 United States

Cluster-randomized trials (CRTs) are a well-established class of designs for evaluating large-scale, community-based research questions. An essential task in planning these trials is determining the required number of clusters and cluster sizes to achieve sufficient statistical power for detecting a clinically relevant effect size. Compared to methods for evaluating the average treatment effect (ATE) for the entire study population, there is more recent development of sample size methods for testing the heterogeneity of treatment effects (HTEs), i.e., modification of treatment effects by subpopulation characteristics, in CRTs. For confirmatory analyses of HTEs in CRTs, effect modifiers must be pre-specified, and ideally, accompanied by sample size or power calculations to ensure the trial has adequate power for the planned analyses. Power analysis for HTE analyses is more complex than for ATEs due to the additional design parameters that must be specified. Power and sample size formulas for HTE analyses have been separately derived under several cluster-randomized designs, including single and multi-period parallel designs, crossover designs, and stepped-wedge designs, as well as under continuous and binary outcomes. This tutorial provides a consolidated reference guide for these methods and enhances their accessibility through the development of an online R Shiny calculator. We further discuss key considerations for researchers conducting sample size and power calculations for testing pre-specified HTE hypotheses in CRTs, including the essential role of advance estimates of intracluster correlation coefficients for both outcomes and covariates on power. The sample size methodology and calculator functionality are demonstrated through real CRT examples. © 2025, CC BY-NC-ND.

关键词： Population statistics

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Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials

arXiv

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arXiv 2021年

作者： Li, Fan Buchanan, Ashley L. Cole, Stephen R. 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 Department of Pharmacy Practice College of Pharmacy University of Rhode Island KingstonRI United States Department of Epidemiology Gillings School of Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States

Comparative effectiveness evidence from randomized trials may not be directly generalizable to a target population of substantive interest when, as in most cases, trial participants are not randomly sampled from the target population. Motivated by the need to generalize evidence from two trials conducted in the AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly robust estimators to estimate the causal effects of HIV interventions in a specified population of people living with HIV in the USA. We focus on a non-nested trial design and discuss strategies for both point and variance estimation of the target population average treatment effect. Specifically in the generalizability context, we demonstrate both analytically and empirically that estimating the known propensity score in trials does not increase the variance for each of the weighting, regression and doubly robust estimators. We apply these methods to generalize the average treatment effects from two ACTG trials to specified target populations and operationalize key practical considerations. Finally, we report on a simulation study that investigates the finite-sample operating characteristics of the generalizability estimators and their sandwich variance estimators. © 2021, CC BY.

关键词： Sampling

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Estimating heterogeneous survival treatment effect in observational data using machine learning

arXiv

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arXiv 2020年

作者： Hu, Liangyuan Ji, Jiayi Li, Fan Department of Population Health Science and Policy Icahn School of Medicine New YorkNY United States Department of Biostatistics Yale University School of Public Health CT United States Center for Methods in Implementation and Prevention Science Yale University School of Public Health CT United States

methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of treatment effect heterogeneity and inform better practice, we carry out a comprehensive simulation study presenting a wide range of settings describing confounded heterogeneous survival treatment effects and varying degrees of covariate overlap. Our results suggest that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently yields the best performance, in terms of bias, precision and expected regret. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Including a non-parametrically estimated propensity score as an additional fixed covariate in the AFT-BART-NP model formulation can further improve its efficiency and frequentist coverage. Finally, we demonstrate the application of flexible causal machine learning estimators through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer. © 2020, CC BY.

关键词： Machine learning

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