arXiv subjects

Guanbo Wang

Publications and source records attributed to Guanbo Wang.

52 records · Page 3Linked to original sources

arXiv2024

CausalMetaR: An R package for performing causally interpretable meta-analyses

Researchers would often like to leverage data from a collection of sources (e.g., primary studies in a meta-analysis) to estimate causal effects in a target population of interest. However, traditional meta-analytic methods do not produce causally interpretable estimates for a well-defined target population. In this paper, we present the CausalMetaR R package, which implements efficient and robust methods to estimate causal effects in a given internal or external target population using multi-source data. The package includes estimators of average and subgroup treatment effects for the entire target population. To produce efficient and robust estimates of causal effects, the package implements doubly robust and non-parametric efficient estimators and supports using flexible data-adaptive (e.g., machine learning techniques) methods and cross-fitting techniques to estimate the nuisance models (e.g., the treatment model, the outcome model). We describe the key features of the package and demonstrate how to use the package through an example.

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arXiv2024

Extending Inferences from Randomized Clinical Trials to Target Populations: A Scoping Review of Transportability Methods

Objective: Randomized controlled trial (RCT) results often inform clinical decision-making, but the highly curated populations of trials and the care provided during the trial are often not reflective of real-world practice. The objective of this scoping review is to identify the ability of methods to transport findings from RCTs to target populations. Study design: A scoping review was conducted on the literature focusing on the transportability of the results from RCTs to observational cohorts. Each study was assessed based on the methodology used for transportability and the extent to which the treatment effect from the RCT was estimated in the target population in observational data. Results: A total of 15 published papers were included. The research topics include cardiovascular diseases, infectious diseases, psychiatry, oncology, orthopedics, anesthesiology, and hematology. These studies show that the findings from RCTs could be translated to real-world settings, with varying degrees of effect size and precision. In some cases, the estimated treatment effect for the target population were statistically significantly different from those in RCTs. Conclusion: Despite variations in the magnitude of effects between RCTs and real-world studies, transportability methods play an important role in effectively bridging the RCTs and real-world care delivery, offering valuable insights for evidence-based medicine.

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arXiv2024

Association between Sitting Time and Urinary Incontinence in the US Population: data from the National Health and Nutrition Examination Survey (NHANES) 2007 to 2018

Background Urinary incontinence (UI) is a common health problem that affects the life and health quality of millions of people in the US. We aimed to investigate the association between sitting time and UI. Methods Across-sectional survey of adult participants of National Health and Nutrition Examination Survey 2007-2018 was performed. Weighted multivariable logistic and regression models were conducted to assess the association between sitting time and UI. Results A total of 22916 participants were enrolled. Prolonged sitting time was associated with urgent UI (UUI, Odds ratio [OR] = 1.184, 95% Confidence interval [CI] = 1.076 to 1.302, P = 0.001). Compared with patients with sitting time shorter than 7 hours, moderate activity increased the risk of prolonged sitting time over 7 hours in the fully-adjusted model (OR = 2.537, 95% CI = 1.419 to 4.536, P = 0.002). Sitting time over 7 hours was related to male mixed UI (MUI, OR = 1.581, 95% CI = 1.129 to 2.213, P = 0.010), and female stress UI (SUI, OR = 0.884, 95% CI = 0.795 to 0.983, P = 0.026) in the fully-adjusted model. Conclusions Prolonged sedentary sitting time (> 7 hours) indicated a high risk of UUI in all populations, female SUI and male MUI. Compared with sitting time shorter than 7 hours, the moderate activity could not reverse the risk of prolonged sitting, which warranted further studies for confirmation.

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arXiv2024

Efficient estimation of subgroup treatment effects using multi-source data

Investigators often use multi-source data (e.g., multi-center trials, meta-analyses of randomized trials, pooled analyses of observational cohorts) to learn about the effects of interventions in subgroups of some well-defined target population. Such a target population can correspond to one of the data sources of the multi-source data or an external population in which the treatment and outcome information may not be available. We develop and evaluate methods for using multi-source data to estimate subgroup potential outcome means and treatment effects in a target population. We consider identifiability conditions and propose doubly robust estimators that, under mild conditions, are non-parametrically efficient and allow for nuisance functions to be estimated using flexible data-adaptive methods (e.g., machine learning techniques). We also show how to construct confidence intervals and simultaneous confidence bands for the estimated subgroup treatment effects. We examine the properties of the proposed estimators in simulation studies and compare performance against alternative estimators. We also conclude that our methods work well when the sample size of the target population is much larger than the sample size of the multi-source data. We illustrate the proposed methods in a meta-analysis of randomized trials for schizophrenia.

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arXiv2024

Continuous-time structural failure time model for intermittent treatment

The intermittent intake of treatment is commonly seen in patients with chronic disease. For example, patients with atrial fibrillation may need to discontinue the oral anticoagulants when they experience a certain surgery and re-initiate the treatment after the surgery. As another example, patients may skip a few days before they refill a treatment as planned. This treatment dispensation information (i.e., the time at which a patient initiates and refills a treatment) is recorded in the electronic healthcare records or claims database, and each patient has a different treatment dispensation. Current methods to estimate the effects of such treatments censor the patients who re-initiate the treatment, which results in information loss or biased estimation. In this work, we present methods to estimate the effect of treatments on failure time outcomes by taking all the treatment dispensation information. The developed methods are based on the continuous-time structural failure time model, where the dependent censoring is tackled by inverse probability of censoring weighting. The estimators are doubly robust and locally efficient.

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arXiv2024

Integrating complex selection rules into the latent overlapping group Lasso for constructing coherent prediction models

The construction of coherent prediction models holds great importance in medical research as such models enable health researchers to gain deeper insights into disease epidemiology and clinicians to identify patients at higher risk of adverse outcomes. One commonly employed approach to developing prediction models is variable selection through penalized regression techniques. Integrating natural variable structures into this process not only enhances model interpretability but can also %increase the likelihood of recovering the true underlying model and boost prediction accuracy. However, a challenge lies in determining how to effectively integrate potentially complex selection dependencies into the penalized regression. In this work, we demonstrate how to represent selection dependencies mathematically, provide algorithms for deriving the complete set of potential models, and offer a structured approach for integrating complex rules into variable selection through the latent overlapping group Lasso. To illustrate our methodology, we applied these techniques to construct a coherent prediction model for major bleeding in hypertensive patients recently hospitalized for atrial fibrillation and subsequently prescribed oral anticoagulants. In this application, we account for a proxy of anticoagulant adherence and its interaction with dosage and the type of oral anticoagulants in addition to drug-drug interactions.

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arXiv2024

A general framework for formulating structured variable selection

In variable selection, a selection rule that prescribes the permissible sets of selected variables (called a "selection dictionary") is desirable due to the inherent structural constraints among the candidate variables. Such selection rules can be complex in real-world data analyses, and failing to incorporate such restrictions could not only compromise the interpretability of the model but also lead to decreased prediction accuracy. However, no general framework has been proposed to formalize selection rules and their applications, which poses a significant challenge for practitioners seeking to integrate these rules into their analyses. In this work, we establish a framework for structured variable selection that can incorporate universal structural constraints. We develop a mathematical language for constructing arbitrary selection rules, where the selection dictionary is formally defined. We demonstrate that all selection rules can be expressed as combinations of operations on constructs, facilitating the identification of the corresponding selection dictionary. Once this selection dictionary is derived, practitioners can apply their own user-defined criteria to select the optimal model. Additionally, our framework enhances existing penalized regression methods for variable selection by providing guidance on how to appropriately group variables to achieve the desired selection rule. Furthermore, our innovative framework opens the door to establishing new l0 norm-based penalized regression techniques that can be tailored to respect arbitrary selection rules, thereby expanding the possibilities for more robust and tailored model development.

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arXiv2024

Non-collapsibility and Built-in Selection Bias of Hazard Ratio in Randomized Controlled Trials

Background: The hazard ratio of the Cox proportional hazards model is widely used in randomized controlled trials to assess treatment effects. However, two properties of the hazard ratio including the non-collapsibility and built-in selection bias need to be further investigated. Methods: We conduct simulations to differentiate the non-collapsibility effect and built-in selection bias from the difference between the marginal and the conditional hazard ratio. Meanwhile, we explore the performance of the Cox model with inverse probability of treatment weighting for covariate adjustment when estimating the marginal hazard ratio. The built-in selection bias is further assessed in the period-specific hazard ratio. Results: The conditional hazard ratio is a biased estimate of the marginal effect due to the non-collapsibility property. In contrast, the hazard ratio estimated from the inverse probability of treatment weighting Cox model provides an unbiased estimate of the true marginal hazard ratio. The built-in selection bias only manifests in the period-specific hazard ratios even when the proportional hazards assumption is satisfied. The Cox model with inverse probability of treatment weighting can be used to account for confounding bias and provide an unbiased effect under the randomized controlled trials setting when the parameter of interest is the marginal effect. Conclusions: We propose that the period-specific hazard ratios should always be avoided due to the profound effects of built-in selection bias.

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arXiv2024

Structured Learning in Time-dependent Cox Models

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (i.e., covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.

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arXiv2024

A Fault Location Method Based on Electromagnetic Transient Convolution Considering Frequency-Dependent Parameters and Lossy Ground

As the capacity of power systems grows, the need for quick and precise short-circuit fault location becomes increasingly vital for ensuring the safe and continuous supply of power. In this paper, we propose a fault location method that utilizes electromagnetic transient convolution (EMTC). We assess the performance of a naive EMTC implementation in multi-phase power lines by using frequency-dependent parameters in real fault simulation, while using constant parameters in pre-calculation. Our results show that the location error increases as the distance between the fault location and the measurement location increases. Therefore, we adopt the aerial mode transients after phase-mode transformation to perform the convolution, which reduces the influence of frequency-dependence and ground loss. We conduct numerical experiments in a 3-phase 100-km transmission line, a radial distribution network and IEEE 9-bus system under different fault conditions. Our results show that the proposed method achieves tolerable location errors and operates efficiently through direct convolution of the real fault-generated transient signals and the pre-stored calculated transient signals.

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arXiv2023

Evaluating hybrid controls methodology in early-phase oncology trials: a simulation study based on the MORPHEUS-UC trial

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase the statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings we recommend more simulation studies.

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arXiv2023

CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift. Leveraging both expert data and lower-quality diverse data, we devise a principled algorithm (namely CLARE) that solves offline IRL efficiently via integrating "conservatism" into a learned reward function and utilizing an estimated dynamics model. Our theoretical analysis provides an upper bound on the return gap between the learned policy and the expert policy, based on which we characterize the impact of covariate shift by examining subtle two-tier tradeoffs between the exploitation (on both expert and diverse data) and exploration (on the estimated dynamics model). We show that CLARE can provably alleviate the reward extrapolation error by striking the right exploitation-exploration balance therein. Extensive experiments corroborate the significant performance gains of CLARE over existing state-of-the-art algorithms on MuJoCo continuous control tasks (especially with a small offline dataset), and the learned reward is highly instructive for further learning.

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arXiv2022

A Fault Location Method Using Direct Convolution: Electromagnetic Time Reversal or Not Reversal

Electromagnetic time reversal (EMTR) is drawing increasing interest in short-circuit fault location. In this letter, we investigate the classic EMTR fault location methods and find that it is not necessary to reverse the obtained signal in time which is a standard operation in these methods before injecting it into the network. The effectiveness of EMTR fault location method results from the specific similarity of the transfer functions in the forward and reverse processes. Therefore, we can inject an arbitrary type and length of source in the reverse process to locate the fault. Based on this observation, we propose a new EMTR fault location method using direct convolution. This method is different from the traditional methods, and it only needs to pre-calculate the assumed fault transients for a given network, which can be stored in embedded hardware. The faults can be located efficiently via direct convolution of the signal collected from a fault and the pre-stored calculated transients, even using a fraction of the fault signal.

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arXiv2021

An Optimization-Accelerated Electromagnetic Time Reversal-based Fault Location Method for Power Lines with Branches

It is very important to locate the short-circuit fault in a power system quickly and accurately. Electromagnetic time reversal (EMTR) has drawn increasing attention because of its clear physical background and excellent performance. This paper studies the EMTR method for locating the short-circuit fault of transmission and distribution lines with or without branches, and introduces a simulated annealing algorithm to accelerate the calculation of an EMTR fault location. This algorithm is different from the traditional exhaustive method in that it solves the corresponding optimization problem, thus improving the location speed by up to an order of magnitude. With the help of graph theory, a method is proposed that automatically splits a complex line topology with branches into several one-dimensional lines. The problem of short-circuit fault location in the branching lines is then transformed into several one-dimensional optimization problems, which are then solved by the optimization algorithm. This solves the problem of realizing rapid location in a power network with branches. Numerical experiments are carried out in a distribution network model to demonstrate the effectiveness of the method. Results under different conditions show the method works reliably and efficiently.

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arXiv2021

GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable for speech recognition training, and to filter out segments with low-quality transcription. For system training, GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, are re-processed by professional human transcribers to ensure high transcription quality. Baseline systems are provided for popular speech recognition toolkits, namely Athena, ESPnet, Kaldi and Pika.

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arXiv2021

Modeling Treatment Effect Modification in Multidrug-Resistant Tuberculosis in an Individual Patient Data Meta-Analysis

Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data (IPD) from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis (MDR-TB), where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model (MSM) for effect modification by different patient characteristics and co-medications in a meta-analysis of observational IPD. We develop, evaluate, and apply a targeted maximum likelihood estimator (TMLE) for the doubly robust estimation of the parameters of the proposed MSM in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study.

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