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Propensity score matching for estimating a marginal hazard ratio Stat. Med. (IF 2.0) Pub Date : 2024-05-06 Tongrong Wang, Honghe Zhao, Shu Yang, Shuhan Tang, Zhanglin Cui, Li Li, Douglas E. Faries
Propensity score matching is commonly used to draw causal inference from observational survival data. However, its asymptotic properties have yet to be established, and variance estimation is still open to debate. We derive the statistical properties of the propensity score matching estimator of the marginal causal hazard ratio based on matching with replacement and a fixed number of matches. We also
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Learning optimal biomarker‐guided treatment policy for chronic disorders Stat. Med. (IF 2.0) Pub Date : 2024-05-03 Bin Yang, Xingche Guo, Ji Meng Loh, Qinxia Wang, Yuanjia Wang
Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre‐treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well‐known to have a low response rate. We aim to design an integrated pipeline that improves the response
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Dynamic hierarchical state space forecasting Stat. Med. (IF 2.0) Pub Date : 2024-05-02 Ziyue Liu, Wensheng Guo
In this paper, we aim to both borrow information from existing units and incorporate the target unit's history data in time series forecasting. We consider a situation when we have time series data from multiple units that share similar patterns when aligned in terms of an internal time. The internal time is defined as an index according to evolving features of interest. When mapped back to the calendar
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Online causal inference with application to near real‐time post‐market vaccine safety surveillance Stat. Med. (IF 2.0) Pub Date : 2024-05-02 Lan Luo, Malcolm Risk, Xu Shi
Streaming data routinely generated by social networks, mobile or web applications, e‐commerce, and electronic health records present new opportunities to monitor the impact of an intervention on an outcome via causal inference methods. However, most existing causal inference methods have been focused on and applied to static data, that is, a fixed data set in which observations are pooled and stored
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Cumulative incidence of cardiac surgery associated with exposure to benfluorex: A retrospective analysis based on compensation claims data Stat. Med. (IF 2.0) Pub Date : 2024-05-02 Paddy Farrington, Solène Lellinger
Data on retrospective compensation claims for injuries caused by pharmaceutical drugs are prone to selection and reporting biases. Nevertheless, this case study of the antidiabetic drug benfluorex shows that such data can be used to estimate the cumulative incidence of drug‐related injury, and to provide insights into its epidemiology. To this end, we develop a modelling framework for under‐reporting
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Negative variance components and intercept‐slope correlations greater than one in magnitude: How do such “non‐regular” random intercept and slope models arise, and what should be done when they do? Stat. Med. (IF 2.0) Pub Date : 2024-05-02 Helen Bridge, Katy E. Morgan, Chris Frost
Statistical models with random intercepts and slopes (RIAS models) are commonly used to analyze longitudinal data. Fitting such models sometimes results in negative estimates of variance components or estimates on parameter space boundaries. This can be an unlucky chance occurrence, but can also occur because certain marginal distributions are mathematically identical to those from RIAS models with
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Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing Stat. Med. (IF 2.0) Pub Date : 2024-05-01 Lizhe Sun, Aiying Zhang, Faming Liang
This article presents a novel method for learning time‐varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time‐varying network
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Distributional imputation for the analysis of censored recurrent events Stat. Med. (IF 2.0) Pub Date : 2024-04-30 Sarah R. Fairfax, Shu Yang
Longitudinal clinical trials for which recurrent events endpoints are of interest are commonly subject to missing event data. Primary analyses in such trials are often performed assuming events are missing at random, and sensitivity analyses are necessary to assess robustness of primary analysis conclusions to missing data assumptions. Control‐based imputation is an attractive approach in superiority
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Flexible parametrization of graph‐theoretical features from individual‐specific networks for prediction Stat. Med. (IF 2.0) Pub Date : 2024-04-26 Mariella Gregorich, Sean L. Simpson, Georg Heinze
Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual‐specific networks, which capture dependencies in complex biological systems, are often summarized by graph‐theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions
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Enhancing long‐term survival prediction with two short‐term events: Landmarking with a flexible varying coefficient model Stat. Med. (IF 2.0) Pub Date : 2024-04-26 Wen Li, Qian Wang, Jing Ning, Jing Zhang, Zhouxuan Li, Sean I. Savitz, Amirali Tahanan, Mohammad H. Rahbar
Patients with cardiovascular diseases who experience disease‐related short‐term events, such as hospitalizations, often exhibit diverse long‐term survival outcomes compared to others. In this study, we aim to improve the prediction of long‐term survival probability by incorporating two short‐term events using a flexible varying coefficient landmark model. Our objective is to predict the long‐term survival
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Weighting estimation in the cause‐specific Cox regression with partially missing causes of failure Stat. Med. (IF 2.0) Pub Date : 2024-04-25 Jooyoung Lee, Shuji Ogino, Molin Wang
Complex diseases are often analyzed using disease subtypes classified by multiple biomarkers to study pathogenic heterogeneity. In such molecular pathological epidemiology research, we consider a weighted Cox proportional hazard model to evaluate the effect of exposures on various disease subtypes under competing‐risk settings in the presence of partially or completely missing biomarkers. The asymptotic
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Population‐average mediation analysis for zero‐inflated count outcomes Stat. Med. (IF 2.0) Pub Date : 2024-04-19 Andrew Sims, D. Leann Long, Hemant K. Tiwari, Jinhong Cui, Dustin M. Long, Todd M. Brown, Melissa J. Smith, Emily B. Levitan
Mediation analysis is an increasingly popular statistical method for explaining causal pathways to inform intervention. While methods have increased, there is still a dearth of robust mediation methods for count outcomes with excess zeroes. Current mediation methods addressing this issue are computationally intensive, biased, or challenging to interpret. To overcome these limitations, we propose a
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Variance‐components tests for genetic association with multiple interval‐censored outcomes Stat. Med. (IF 2.0) Pub Date : 2024-04-18 Jaihee Choi, Zhichao Xu, Ryan Sun
Massive genetic compendiums such as the UK Biobank have become an invaluable resource for identifying genetic variants that are associated with complex diseases. Due to the difficulties of massive data collection, a common practice of these compendiums is to collect interval‐censored data. One challenge in analyzing such data is the lack of methodology available for genetic association studies with
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On variance estimation of the inverse probability‐of‐treatment weighting estimator: A tutorial for different types of propensity score weights Stat. Med. (IF 2.0) Pub Date : 2024-04-16 Andriana Kostouraki, David Hajage, Bernard Rachet, Elizabeth J. Williamson, Guillaume Chauvet, Aurélien Belot, Clémence Leyrat
Propensity score methods, such as inverse probability‐of‐treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by
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Finding the best subgroup with differential treatment effect with multiple outcomes Stat. Med. (IF 2.0) Pub Date : 2024-04-16 Beibo Zhao, Jason Fine, Anastasia Ivanova
Precision medicine aims to identify specific patient subgroups that may benefit the most from a particular treatment than the whole population. Existing definitions for the best subgroup in subgroup analysis are based on a single outcome and do not consider multiple outcomes; specifically, outcomes of different types. In this article, we introduce a definition for the best subgroup under a multiple‐outcome
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Multiblock partial least squares and rank aggregation: Applications to detection of bacteriophages associated with antimicrobial resistance in the presence of potential confounding factors Stat. Med. (IF 2.0) Pub Date : 2024-04-15 Shoumi Sarkar, Samuel Anyaso‐Samuel, Peihua Qiu, Somnath Datta
Urban environments, characterized by bustling mass transit systems and high population density, host a complex web of microorganisms that impact microbial interactions. These urban microbiomes, influenced by diverse demographics and constant human movement, are vital for understanding microbial dynamics. We explore urban metagenomics, utilizing an extensive dataset from the Metagenomics & Metadesign
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Order selection for heterogeneous semiparametric hidden Markov models Stat. Med. (IF 2.0) Pub Date : 2024-04-15 Yudan Zou, Xinyuan Song, Qian Zhao
Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion‐based methods become
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Mediation analysis using incomplete information from publicly available data sources Stat. Med. (IF 2.0) Pub Date : 2024-04-12 Andriy Derkach, Elizabeth D. Kantor, Joshua N. Sampson, Ruth M. Pfeiffer
Our work was motivated by the question whether, and to what extent, well‐established risk factors mediate the racial disparity observed for colorectal cancer (CRC) incidence in the United States. Mediation analysis examines the relationships between an exposure, a mediator and an outcome. All available methods require access to a single complete data set with these three variables. However, because
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Statistical considerations in model‐based dose finding for binary responses under model uncertainty Stat. Med. (IF 2.0) Pub Date : 2024-04-12 Zhiwu Yan, Min Yang
The statistical methodology for model‐based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore
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A discrete approximation method for modeling interval-censored multistate data Stat. Med. (IF 2.0) Pub Date : 2024-04-10 Lu You, Xiang Liu, Jeffrey Krischer
Many longitudinal studies are designed to monitor participants for major events related to the progression of diseases. Data arising from such longitudinal studies are usually subject to interval censoring since the events are only known to occur between two monitoring visits. In this work, we propose a new method to handle interval-censored multistate data within a proportional hazards model framework
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A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling Stat. Med. (IF 2.0) Pub Date : 2024-04-10 Wei Wei, Ondrej Blaha, Denise Esserman, Daniel Zelterman, Michael Kane, Rachael Liu, Jianchang Lin
Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the
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Bayesian federated inference for estimating statistical models based on non‐shared multicenter data sets Stat. Med. (IF 2.0) Pub Date : 2024-04-09 Marianne A Jonker, Hassan Pazira, Anthony CC Coolen
Identifying predictive factors for an outcome of interest via a multivariable analysis is often difficult when the data set is small. Combining data from different medical centers into a single (larger) database would alleviate this problem, but is in practice challenging due to regulatory and logistic problems. Federated learning (FL) is a machine learning approach that aims to construct from local
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Individualized empirical null estimation for exact tests of healthcare quality Stat. Med. (IF 2.0) Pub Date : 2024-04-09 Nicholas Hartman, Kevin He
United States federal agencies evaluate healthcare providers to identify, flag, and potentially penalize those that deliver low‐quality care compared to national expectations. In practice, evaluation metrics are inevitably impacted by unobserved confounding factors, which reduce flagging accuracy and cause the statistics to be overdispersed relative to the theoretical null distributions. In response
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Beyond the two‐trials rule Stat. Med. (IF 2.0) Pub Date : 2024-04-04 Leonhard Held
The two‐trials rule for drug approval requires “at least two adequate and well‐controlled studies, each convincing on its own, to establish effectiveness.” This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of
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Confidence intervals for odds ratio from multistage randomized phase II trials Stat. Med. (IF 2.0) Pub Date : 2024-04-03 Shiwei Cao, Sin‐Ho Jung
A multi‐stage randomized trial design can significantly improve efficiency by allowing early termination of the trial when the experimental arm exhibits either low or high efficacy compared to the control arm during the study. However, proper inference methods are necessary because the underlying distribution of the target statistic changes due to the multi‐stage structure. This article focuses on
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A joint frailty model for recurrent and competing terminal events: Application to delirium in the ICU Stat. Med. (IF 2.0) Pub Date : 2024-04-02 Lacey H. Etzkorn, Quentin Le Coënt, Mark van den Boogaard, Virginie Rondeau, Elizabeth Colantuoni
Joint models linking longitudinal biomarkers or recurrent event processes with a terminal event, for example, mortality, have been studied extensively. Motivated by studies of recurrent delirium events in patients receiving care in an intensive care unit (ICU), we devise a joint model for a recurrent event process and multiple terminal events. Being discharged alive from the ICU or experiencing mortality
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Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose? Stat. Med. (IF 2.0) Pub Date : 2024-04-02 Matthew Dodd, James Carpenter, Jennifer A. Thompson, Elizabeth Williamson, Katherine Fielding, Diana Elbourne
BackgroundNon‐inferiority trials comparing different active drugs are often subject to treatment non‐adherence. Intention‐to‐treat (ITT) and per‐protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non‐adherence.MethodsThe REMoxTB trial evaluated two 4‐month experimental regimens compared with a 6‐month control regimen for
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A two‐stage group‐sequential design for delayed treatment responses with the possibility of trial restart Stat. Med. (IF 2.0) Pub Date : 2024-04-02 Stephen Schüürhuis, Frank Konietschke, Cornelia Ursula Kunz
Common statistical theory applicable to confirmatory phase III trial designs usually assumes that patients are enrolled simultaneously and there is no time gap between enrollment and outcome observation. However, in practice, patients are enrolled successively and there is a lag between the enrollment of a patient and the measurement of the primary outcome. For single‐stage designs, the difference
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Approximate balancing weights for clustered observational study designs Stat. Med. (IF 2.0) Pub Date : 2024-04-01 Eli Ben‐Michael, Lindsay Page, Luke Keele
In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure
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Causal mediation analysis with mediator values below an assay limit Stat. Med. (IF 2.0) Pub Date : 2024-04-01 Ariel Chernofsky, Ronald J. Bosch, Judith J. Lok
Causal indirect and direct effects provide an interpretable method for decomposing the total effect of an exposure on an outcome into the indirect effect through a mediator and the direct effect through all other pathways. A natural choice for a mediator in a randomized clinical trial is the treatment's targeted biomarker. However, when the mediator is a biomarker, values can be subject to an assay
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Information‐incorporated sparse hierarchical cancer heterogeneity analysis Stat. Med. (IF 2.0) Pub Date : 2024-03-30 Wei Han, Sanguo Zhang, Shuangge Ma, Mingyang Ren
Cancer heterogeneity analysis is essential for precision medicine. Most of the existing heterogeneity analyses only consider a single type of data and ignore the possible sparsity of important features. In cancer clinical practice, it has been suggested that two types of data, pathological imaging and omics data, are commonly collected and can produce hierarchical heterogeneous structures, in which
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Collaborative inference for treatment effect with distributed data‐sharing management in multicenter studies Stat. Med. (IF 2.0) Pub Date : 2024-03-29 Mengtong Hu, Xu Shi, Peter X.‐K. Song
Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time‐consuming. Data merging may become more burdensome when propensity
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A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes Stat. Med. (IF 2.0) Pub Date : 2024-03-28 Dennis Dobler, Kathrin Möllenhoff
A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover
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How could a pooled testing policy have performed in managing the early stages of the COVID‐19 pandemic? Results from a simulation study Stat. Med. (IF 2.0) Pub Date : 2024-03-28 Bethany Heath, Sofía S. Villar, David S. Robertson
A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID‐19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimizing cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analyzing this with a single diagnostic
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Sample size adaptation designs and efficiency comparison with group sequential designs Stat. Med. (IF 2.0) Pub Date : 2024-03-28 Lu Cui
This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two‐stage adaptive clinical trial setting, theorems are developed to describe
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Estimating generalized propensity scores with survey and attrition weighted data Stat. Med. (IF 2.0) Pub Date : 2024-03-26 Daniel F. McCaffrey, Beth Ann Griffin, Michael Robbins, Yajnaseni Chakraborti, Donna L. Coffman, Brian Vegetabile
Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to
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Model‐agnostic explanations for survival prediction models Stat. Med. (IF 2.0) Pub Date : 2024-03-26 Krithika Suresh, Carsten Görg, Debashis Ghosh
Advanced machine learning methods capable of capturing complex and nonlinear relationships can be used in biomedical research to accurately predict time‐to‐event outcomes. However, these methods have been criticized as “black boxes” that are not interpretable and thus are difficult to trust in making important clinical decisions. Explainable machine learning proposes the use of model‐agnostic explainers
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Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss Stat. Med. (IF 2.0) Pub Date : 2024-03-16 Yin Liu, Ziwen Gao
Excessive zeros in multivariate count data are often observed in scenarios of biomedicine and public health. To provide a better analysis on this type of data, we first develop a marginalized multivariate zero‐inflated Poisson (MZIP) regression model to directly interpret the overall exposure effects on marginal means. Then, we define a multiple Pearson residual for our newly developed MZIP regression
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Monitoring epidemic processes under political measures Stat. Med. (IF 2.0) Pub Date : 2024-03-15 Nataliya Chukhrova, Oskar Plate, Arne Johannssen
Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring
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Categorisation of continuous covariates for stratified randomisation: How should we adjust? Stat. Med. (IF 2.0) Pub Date : 2024-03-15 Thomas R. Sullivan, Tim P. Morris, Brennan C. Kahan, Alana R. Cuthbert, Lisa N. Yelland
To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories
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A new and unified method for regression analysis of interval‐censored failure time data under semiparametric transformation models with missing covariates Stat. Med. (IF 2.0) Pub Date : 2024-03-13 Yichen Lou, Yuqing Ma, Mingyue Du
This paper discusses regression analysis of interval‐censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two‐step inference procedure that can
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Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data Stat. Med. (IF 2.0) Pub Date : 2024-03-13 Florie Bouvier, Etienne Peyrot, Alan Balendran, Corentin Ségalas, Ian Roberts, François Petit, Raphaël Porcher
Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of
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Penalized weighted smoothed quantile regression for high-dimensional longitudinal data Stat. Med. (IF 2.0) Pub Date : 2024-03-08 Yanan Song, Haohui Han, Liya Fu, Ting Wang
Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed
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On the distribution of the power function for the scale parameter of exponential families Stat. Med. (IF 2.0) Pub Date : 2024-03-05 Fulvio De Santis, Stefania Gubbiotti
The expected value of the standard power function of a test, computed with respect to a design prior distribution, is often used to evaluate the probability of success of an experiment. However, looking only at the expected value might be reductive. Instead, the whole probability distribution of the power function induced by the design prior can be exploited. In this article we consider one-sided testing
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Estimation and reduction of bias in self-controlled case series with non-rare event dependent outcomes and heterogeneous populations Stat. Med. (IF 2.0) Pub Date : 2024-03-04 Kenneth Menglin Lee, Yin Bun Cheung
The self-controlled case series (SCCS) is a commonly adopted study design in the assessment of vaccine and drug safety. Recurrent event data collected from SCCS studies are typically analyzed using the conditional Poisson model which assumes event times are independent within-cases. This assumption is violated in the presence of event dependence, where the occurrence of an event influences the probability
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Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany Stat. Med. (IF 2.0) Pub Date : 2024-02-29 Patrick W. Schmidt
Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for
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Use of win time for ordered composite endpoints in clinical trials Stat. Med. (IF 2.0) Pub Date : 2024-02-28 James F. Troendle, Eric S. Leifer, Song Yang, Neal Jeffries, Dong‐Yun Kim, Jungnam Joo, Christopher M. O'Connor
Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow‐up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that
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Detecting changes in the transmission rate of a stochastic epidemic model Stat. Med. (IF 2.0) Pub Date : 2024-02-27 Jenny Huang, Raphaël Morsomme, David Dunson, Jason Xu
Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for
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Doubly adaptive biased coin design to improve Bayesian clinical trials with time-to-event endpoints Stat. Med. (IF 2.0) Pub Date : 2024-02-22 Wenhao Cao, Hongjian Zhu, Li Wang, Lixin Zhang, Jun Yu
Clinical trialists often face the challenge of balancing scientific questions with other design features, such as improving efficiency, minimizing exposure to inferior treatments, and simultaneously comparing multiple treatments. While Bayesian response adaptive randomization (RAR) is a popular and effective method for achieving these objectives, it is known to have large variability and a lack of
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Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers Stat. Med. (IF 2.0) Pub Date : 2024-02-26 Yanping Chen, Yong Lin, Shou-En Lu, Weichung J. Shih, Hui Quan
Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive
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A Bayesian nonparametric meta-analysis model for estimating the reference interval Stat. Med. (IF 2.0) Pub Date : 2024-02-26 Wenhao Cao, Haitao Chu, Timothy Hanson, Lianne Siegel
A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta-analysis can provide a more generalizable reference interval based on the combined population
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RMST-based multiple contrast tests in general factorial designs Stat. Med. (IF 2.0) Pub Date : 2024-02-25 Merle Munko, Marc Ditzhaus, Dennis Dobler, Jon Genuneit
Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area
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Robust best linear weighted estimator with missing covariates in survival analysis Stat. Med. (IF 2.0) Pub Date : 2024-02-25 Ching‐Yun Wang, Li Hsu, Tabitha Harrison
Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood‐based estimators, but in general
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Estimation of trajectory of protective efficacy in infectious disease prevention trials using recurrent event times Stat. Med. (IF 2.0) Pub Date : 2024-02-24 Yin Bun Cheung, Xiangmei Ma, K. F. Lam, Chee Fu Yung, Paul Milligan
In studies of infectious disease prevention, the level of protective efficacy of medicinal products such as vaccines and prophylactic drugs tends to vary over time. Many products require administration of multiple doses at scheduled times, as opposed to one‐off or continual intervention. Accurate information on the trajectory of the level of protective efficacy over time facilitates informed clinical
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Optimal ensemble construction for multistudy prediction with applications to mortality estimation Stat. Med. (IF 2.0) Pub Date : 2024-02-24 Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder, Giovanni Parmigiani
It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling datasets before model fitting can produce poor out‐of‐study prediction performance when datasets are heterogeneous. Theoretical and applied work has shown multistudy ensembling to be a viable alternative that leverages the
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