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A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case‐Crossover Design Stat. Med. (IF 1.8) Pub Date : 2024-09-20 Yi Shi, Michael T. Eadon, Yao Chen, Anna Sun, Yuedi Yang, Chienwei Chiang, Macarius Donneyong, Jing Su, Pengyue Zhang
Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real‐world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case‐crossover design has been implemented to leverage large‐scale administrative claims data for ADE detection, while controlling both observed confounding effects
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Transportability of model‐based estimands in evidence synthesis Stat. Med. (IF 1.8) Pub Date : 2024-09-18 Antonio Remiro‐Azócar
In evidence synthesis, effect modifiers are typically described as variables that induce treatment effect heterogeneity at the individual level, through treatment‐covariate interactions in an outcome model parametrized at such level. As such, effect modification is defined with respect to a conditional measure, but marginal effect estimates are required for population‐level decisions in health technology
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Nonparametric Biomarker Based Treatment Selection With Reproducibility Data Stat. Med. (IF 1.8) Pub Date : 2024-09-18 Sara Byers, Xiao Song
We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms
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Group Response‐Adaptive Randomization With Delayed and Missing Responses Stat. Med. (IF 1.8) Pub Date : 2024-09-17 Guannan Zhai, Yang Li, Lixin Zhang, Feifang Hu
Response‐adaptive randomization (RAR) procedures have been extensively studied in the literature, but most of the procedures rely on updating the randomization after each response, which is impractical in many clinical trials. In this article, we propose a new family of RAR procedures that dynamically update based on the responses of a group of individuals, either when available or at fixed time intervals
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Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra‐Class Correlation Coefficient in Inter‐Rater Reliability Studies Stat. Med. (IF 1.8) Pub Date : 2024-09-17 Abderrahmane Bourredjem, Hervé Cardot, Hervé Devilliers
The agreement intra‐class correlation coefficient (ICCa) is a suitable statistical index for inter‐rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined
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Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach Stat. Med. (IF 1.8) Pub Date : 2024-09-12 Tianyi Sun, Allison B. McCoy, Alan B. Storrow, Dandan Liu
Clinical prediction models have been widely acknowledged as informative tools providing evidence‐based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real‐time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical
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The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies Stat. Med. (IF 1.8) Pub Date : 2024-09-11 Yuwen Liu, Jie Ren, Shuangge Ma, Cen Wu
Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy‐tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable
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Mediation Analysis with Multiple Exposures and Multiple Mediators Stat. Med. (IF 1.8) Pub Date : 2024-09-09 Yi Zhao
A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood‐based estimators are introduced for simultaneous
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Functional Principal Component Analysis as an Alternative to Mixed‐Effect Models for Describing Sparse Repeated Measures in Presence of Missing Data Stat. Med. (IF 1.8) Pub Date : 2024-09-09 Corentin Ségalas, Catherine Helmer, Robin Genuer, Cécile Proust‐Lima
Analyzing longitudinal data in health studies is challenging due to sparse and error‐prone measurements, strong within‐individual correlation, missing data and various trajectory shapes. While mixed‐effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non‐parametric
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Selection of number of clusters and warping penalty in clustering functional electrocardiogram Stat. Med. (IF 1.8) Pub Date : 2024-09-09 Wei Yang, Harold I. Feldman, Wensheng Guo
Clustering functional data aims to identify unique functional patterns in the entire domain, but this can be challenging due to phase variability that distorts the observed patterns. Curve registration can be used to remove this variability, but determining the appropriate level of warping flexibility can be complicated. Curve registration also requires a target to which a functional object is aligned
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High‐Dimensional Overdispersed Generalized Factor Model With Application to Single‐Cell Sequencing Data Analysis Stat. Med. (IF 1.8) Pub Date : 2024-09-06 Jinyu Nie, Zhilong Qin, Wei Liu
The current high‐dimensional linear factor models fail to account for the different types of variables, while high‐dimensional nonlinear factor models often overlook the overdispersion present in mixed‐type data. However, overdispersion is prevalent in practical applications, particularly in fields like biomedical and genomics studies. To address this practical demand, we propose an overdispersed generalized
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A Causal Mediation Approach to Account for Interaction of Treatment and Intercurrent Events: Using Hypothetical Strategy Stat. Med. (IF 1.8) Pub Date : 2024-09-05 Kunpeng Wu, Xiangliang Zhang, Meng Zheng, Jianghui Zhang, Wen Chen
Hypothetical strategy is a common strategy for handling intercurrent events (IEs). No current guideline or study considers treatment–IE interaction to target the estimand in any one IE‐handling strategy. Based on the hypothetical strategy, we aimed to (1) assess the performance of three estimators with different considerations for the treatment–IE interaction in a simulation and (2) compare the estimation
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Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID‐19 Stat. Med. (IF 1.8) Pub Date : 2024-09-05 Davide Pigoli, Kieran Baker, Jobie Budd, Lorraine Butler, Harry Coppock, Sabrina Egglestone, Steven G. Gilmour, Chris Holmes, David Hurley, Radka Jersakova, Ivan Kiskin, Vasiliki Koutra, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel, Richard Payne, Stephen J. Roberts, Björn W. Schuller, Ana Tendero‐Cañadas, Tracey Thornley, Alexander Titcomb
From early in the coronavirus disease 2019 (COVID‐19) pandemic, there was interest in using machine learning methods to predict COVID‐19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations
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Approximate maximum likelihood estimation in cure models using aggregated data, with application to HPV vaccine completion Stat. Med. (IF 1.8) Pub Date : 2024-09-05 John D. Rice, Allison Kempe
Research into vaccine hesitancy is a critical component of the public health enterprise, as rates of communicable diseases preventable by routine childhood immunization have been increasing in recent years. It is therefore important to estimate proportions of “never‐vaccinators” in various subgroups of the population in order to successfully target interventions to improve childhood vaccination rates
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Performance of mixed effects models and generalized estimating equations for continuous outcomes in partially clustered trials including both independent and paired data Stat. Med. (IF 1.8) Pub Date : 2024-09-05 Kylie M. Lange, Thomas R. Sullivan, Jessica Kasza, Lisa N. Yelland
Many clinical trials involve partially clustered data, where some observations belong to a cluster and others can be considered independent. For example, neonatal trials may include infants from single or multiple births. Sample size and analysis methods for these trials have received limited attention. A simulation study was conducted to (1) assess whether existing power formulas based on generalized
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Multilevel Longitudinal Functional Principal Component Model Stat. Med. (IF 1.8) Pub Date : 2024-09-03 Wenyi Lin, Jingjing Zou, Chongzhi Di, Cheryl L. Rock, Loki Natarajan
Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10–100 Hz or minute‐level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the
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Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs Stat. Med. (IF 1.8) Pub Date : 2024-09-03 Jonathan C. Moyer, Fan Li, Andrea J. Cook, Patrick J. Heagerty, Sherri L. Pals, Elizabeth L. Turner, Rui Wang, Yunji Zhou, Qilu Yu, Xueqi Wang, David M. Murray
Many individually randomized group treatment (IRGT) trials randomly assign individuals to study arms but deliver treatments via shared agents, such as therapists, surgeons, or trainers. Post‐randomization interactions induce correlations in outcome measures between participants sharing the same agent. Agents can be nested in or crossed with trial arm, and participants may interact with a single agent
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Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records Stat. Med. (IF 1.8) Pub Date : 2024-09-03 Daren Kuwaye, Hyunkeun Ryan Cho
In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection
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A hybrid approach to sample size re‐estimation in cluster randomized trials with continuous outcomes Stat. Med. (IF 1.8) Pub Date : 2024-08-28 Samuel K Sarkodie, James MS Wason, Michael J Grayling
This study presents a hybrid (Bayesian‐frequentist) approach to sample size re‐estimation (SSRE) for cluster randomised trials with continuous outcome data, allowing for uncertainty in the intra‐cluster correlation (ICC). In the hybrid framework, pre‐trial knowledge about the ICC is captured by placing a Truncated Normal prior on it, which is then updated at an interim analysis using the study data
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A simulation study of the performance of statistical models for count outcomes with excessive zeros Stat. Med. (IF 1.8) Pub Date : 2024-08-28 Zhengyang Zhou, Dateng Li, David Huh, Minge Xie, Eun‐Young Mun
Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol‐related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero‐inflated, particularly
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Estimating causes of maternal death in data‐sparse contexts Stat. Med. (IF 1.8) Pub Date : 2024-08-27 Michael Y. C. Chong, Marija Pejchinovska, Monica Alexander
Understanding the underlying causes of maternal death across all regions of the world is essential to inform policies and resource allocation to reduce the mortality burden. However, in many countries there exists very little data on the causes of maternal death, and data that do exist do not capture the entire population at risk. In this article, we present a Bayesian hierarchical multinomial model
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Modeling multiple‐criterion diagnoses by heterogeneous‐instance logistic regression Stat. Med. (IF 1.8) Pub Date : 2024-08-27 Chun‐Hao Yang, Ming‐Han Li, Shu‐Fang Wen, Sheng‐Mao Chang
Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD) that causes a significant burden in caregiving and medical costs. Clinically, the diagnosis of MCI is determined by the impairment statuses of five cognitive domains. If one of these cognitive domains is impaired, the patient is diagnosed with MCI, and if two out of the five domains are impaired, the patient is diagnosed
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Calibrating machine learning approaches for probability estimation: A short expansion Stat. Med. (IF 1.8) Pub Date : 2024-08-22 Francisco M. Ojeda, Stuart G. Baker, Andreas Ziegler
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Group sequential designs for clinical trials when the maximum sample size is uncertain Stat. Med. (IF 1.8) Pub Date : 2024-08-22 Amin Yarahmadi, Lori E. Dodd, Thomas Jaki, Peter Horby, Nigel Stallard
Motivated by the experience of COVID‐19 trials, we consider clinical trials in the setting of an emerging disease in which the uncertainty of natural disease course and potential treatment effects makes advance specification of a sample size challenging. One approach to such a challenge is to use a group sequential design to allow the trial to stop on the basis of interim analysis results as soon as
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Optimal subsampling for semi-parametric accelerated failure time models with massive survival data using a rank-based approach. Stat. Med. (IF 1.8) Pub Date : 2024-08-20 Zehan Yang,HaiYing Wang,Jun Yan
Subsampling is a practical strategy for analyzing vast survival data, which are progressively encountered across diverse research domains. While the optimal subsampling method has been applied to inferences for Cox models and parametric accelerated failure time (AFT) models, its application to semi-parametric AFT models with rank-based estimation have received limited attention. The challenges arise
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Principal quantile treatment effect estimation using principal scores Stat. Med. (IF 1.8) Pub Date : 2024-08-19 Kotaro Mizuma, Takamasa Hashimoto, Sho Sakui, Shingo Kuroda
Intercurrent events and estimands play a key role in defining the treatment effects of interest precisely. Sometimes the median or other quantiles of outcomes in a principal stratum according to potential occurrence of intercurrent events are of interest in randomized clinical trials. Naïve analyses such as those based on the observed occurrence of the intercurrent events lead to biased results. Therefore
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Comparing methods for assessing the reliability of health care quality measures Stat. Med. (IF 1.8) Pub Date : 2024-08-15 Kenneth J. Nieser, Alex H. S. Harris
Quality measurement plays an increasing role in U.S. health care. Measures inform quality improvement efforts, public reporting of variations in quality of care across providers and hospitals, and high‐stakes financial decisions. To be meaningful in these contexts, measures should be reliable and not heavily impacted by chance variations in sampling or measurement. Several different methods are used
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A novel block‐coordinate gradient descent algorithm for simultaneous grouped selection of fixed and random effects in joint modeling Stat. Med. (IF 1.8) Pub Date : 2024-08-15 Shuyan Chen, Zhiqing Fang, Zhong Li, Xin Liu
Joint models for longitudinal and time‐to‐event data are receiving increasing attention owing to its capability of capturing the possible association between these two types of data. Typically, a joint model consists of a longitudinal submodel for longitudinal processes and a survival submodel for the time‐to‐event response, and links two submodels by common covariates that may carry both fixed and
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Scalar‐on‐function regression: Estimation and inference under complex survey designs Stat. Med. (IF 1.8) Pub Date : 2024-08-13 Ekaterina Smirnova, Erjia Ciu, Lucia Tabacu, Andrew Leroux
Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of
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A modeling framework for the analysis of the SARS‐CoV2 transmission dynamics Stat. Med. (IF 1.8) Pub Date : 2024-08-09 Anastasia Chatzilena, Nikolaos Demiris, Konstantinos Kalogeropoulos
Despite the progress in medical data collection the actual burden of SARS‐CoV‐2 remains unknown due to under‐ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under‐reporting. Since daily deaths occur from past infections weighted by their probability of death
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Path‐specific causal decomposition analysis with multiple correlated mediator variables Stat. Med. (IF 1.8) Pub Date : 2024-08-07 Melissa J. Smith, Leslie A. McClure, D. Leann Long
A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods for causal decomposition
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Dynamic path analysis for exploring treatment effect mediation processes in clinical trials with time‐to‐event endpoints Stat. Med. (IF 1.8) Pub Date : 2024-08-07 Matthias Kormaksson, Markus Reiner Lange, David Demanse, Susanne Strohmaier, Jiawei Duan, Qing Xie, Mariana Carbini, Claudia Bossen, Achim Guettner, Antonella Maniero
Why does a beneficial treatment effect on a longitudinal biomarker not translate into overall treatment benefit on survival, when the biomarker is in fact a prognostic factor of survival? In a recent exploratory data analysis in oncology, we were faced with this seemingly paradoxical result. To address this problem, we applied a theoretically principled methodology called dynamic path analysis, which
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Exact test and exact confidence interval for the Cox model Stat. Med. (IF 1.8) Pub Date : 2024-08-07 Yongwu Shao, Zhishen Ye, Zhiwei Zhang
The Cox proportional hazards model is commonly used to analyze time‐to‐event data in clinical trials. Standard inference procedures for the Cox model are based on asymptotic approximations and may perform poorly when there are few events in one or both treatment groups, as may be the case when the event of interest is rare or when the experimental treatment is highly efficacious. In this article, we
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Evaluating individualized treatment effect predictions: A model‐based perspective on discrimination and calibration assessment Stat. Med. (IF 1.8) Pub Date : 2024-08-02 J. Hoogland, O. Efthimiou, T. L. Nguyen, T. P. A. Debray
In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their performance. In this paper, we aim to facilitate the validation of prediction models for individualized treatment effects. The estimands of interest are defined based
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How estimating nuisance parameters can reduce the variance (with consistent variance estimation) Stat. Med. (IF 1.8) Pub Date : 2024-07-31 Judith J. Lok
We often estimate a parameter of interest when the identifying conditions involve a finite‐dimensional nuisance parameter . Examples from causal inference are inverse probability weighting, marginal structural models and structural nested models, which all lead to unbiased estimating equations. This article presents a consistent sandwich estimator for the variance of estimators that solve unbiased
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Evaluation and comparison of covariate balance metrics in studies with time‐dependent confounding Stat. Med. (IF 1.8) Pub Date : 2024-07-31 David Adenyo, Jason R. Guertin, Bernard Candas, Caroline Sirois, Denis Talbot
Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time‐varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in
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Validation of predicted individual treatment effects in out of sample respondents Stat. Med. (IF 1.8) Pub Date : 2024-07-30 Alena Kuhlemeier, Thomas Jaki, Katie Witkiewitz, Elizabeth A. Stuart, M. Lee Van Horn
Personalized medicine promises the ability to improve patient outcomes by tailoring treatment recommendations to the likelihood that any given patient will respond well to a given treatment. It is important that predictions of treatment response be validated and replicated in independent data to support their use in clinical practice. In this paper, we propose and test an approach for validating predictions
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Identification and estimation of causal effects in the presence of confounded principal strata Stat. Med. (IF 1.8) Pub Date : 2024-07-30 Shanshan Luo, Wei Li, Wang Miao, Yangbo He
Principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non‐compliance and truncation by death problems. The causal effects within principal strata, which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. The analysis
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Erratum to “Joint modeling for censored predictors due to detection limits with applications to metabolites data” Stat. Med. (IF 1.8) Pub Date : 2024-07-30
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Dual‐agent dose‐finding in Phase I clinical trial—An extension of rapid enrollment design Stat. Med. (IF 1.8) Pub Date : 2024-07-30 Yunfei Wang
Dual‐agent treatment has become more and more popular in clinical trials. We have developed an approach called rapid enrollment dual‐agent design (REDD) for dose‐finding in Phase I clinical trials. This approach aims to administer treatment to patients using a dose combination that is highly probable to be the target dose combination. Unlike other non‐model‐based designs, rapid enrollment designs (RED
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Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data Stat. Med. (IF 1.8) Pub Date : 2024-07-26 Ilya Lipkovich, David Svensson, Bohdana Ratitch, Alex Dmitrienko
In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136‐196) that distinguish
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Optimal model averaging for partially linear models with missing response variables and error‐prone covariates Stat. Med. (IF 1.8) Pub Date : 2024-07-26 Zhongqi Liang, Suojin Wang, Li Cai
We consider the problem of optimal model averaging for partially linear models when the responses are missing at random and some covariates are measured with error. A novel weight choice criterion based on the Mallows‐type criterion is proposed for the weight vector to be used in the model averaging. The resulting model averaging estimator for the partially linear models is shown to be asymptotically
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Separable and controlled direct effects for competing events: Estimation of component specific effects on sickness absence Stat. Med. (IF 1.8) Pub Date : 2024-07-25 Niklas N. Maltzahn, Ingrid Sivesind Mehlum, Jon Michael Gran
In many settings, it is reasonable to think of treatment as consisting of a number of components, either because this is the case in practice or because it is conceptually possible to decompose treatment into separate components due to the way in which it exerts effects on the outcome of interest. For competing events, the treatment decomposition idea has recently been suggested to separate effects
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Shortest path or random walks? A framework for path weights in network meta‐analysis Stat. Med. (IF 1.8) Pub Date : 2024-07-24 Gerta Rücker, Theodoros Papakonstantinou, Adriani Nikolakopoulou, Guido Schwarzer, Tobias Galla, Annabel L. Davies
Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta‐analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path‐design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have
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Using a monotonic density ratio model to increase the power of the goodness‐of‐fit test for logistic regression models with case‐control data Stat. Med. (IF 1.8) Pub Date : 2024-07-24 Chunlin Wang, Zheyu Liu, Xinyu Wang
Logistic regression models are widely used in case‐control data analysis, and testing the goodness‐of‐fit of their parametric model assumption is a fundamental research problem. In this article, we propose to enhance the power of the goodness‐of‐fit test by exploiting a monotonic density ratio model, in which the ratio of case and control densities is assumed to be a monotone function. We show that
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An augmented illness‐death model for semi‐competing risks with clinically immediate terminal events Stat. Med. (IF 1.8) Pub Date : 2024-07-23 Harrison T. Reeder, Kyu Ha Lee, Stefania I. Papatheodorou, Sebastien Haneuse
Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of “semi‐competing risks” in which a non‐terminal event of interest is subject to
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Using instruments for selection to adjust for selection bias in Mendelian randomization Stat. Med. (IF 1.8) Pub Date : 2024-07-23 Apostolos Gkatzionis, Eric J. Tchetgen Tchetgen, Jon Heron, Kate Northstone, Kate Tilling
Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing
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A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two‐stage method Stat. Med. (IF 1.8) Pub Date : 2024-07-19 Madeline R. Abbott, Inbal Nahum‐Shani, Cho Y. Lam, Lindsey N. Potter, David W. Wetter, Walter H. Dempsey
Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real‐time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated
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Bayesian modeling of spatial ordinal data from health surveys Stat. Med. (IF 1.8) Pub Date : 2024-07-18 Miguel Ángel Beltrán‐Sánchez, Miguel‐Angel Martinez‐Beneito, Ana Corberán‐Vallet
Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual‐level model for small‐area estimation of survey‐based health
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Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data Stat. Med. (IF 1.8) Pub Date : 2024-07-16 Lily Zou, Yun‐Hee Choi, Leonardo Guizzetti, Di Shu, Joshua Zou, Guangyong Zou
A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate
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Dynamic undirected graphical models for time‐varying clinical symptom and neuroimaging networks Stat. Med. (IF 1.8) Pub Date : 2024-07-15 Erin I. McDonnell, Shanghong Xie, Karen Marder, Fanyu Cui, Yuanjia Wang
In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near‐certainty of disease. We propose a time‐dependent undirected graphical model that ensures temporal and structural smoothness across time‐specific networks to
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Prefiltered component‐based greedy (PreCoG) scan method Stat. Med. (IF 1.8) Pub Date : 2024-07-12 Joshua P. French, Mohammad Meysami, Ettie M. Lipner
The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component‐based Greedy (PreCoG) scan method
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Exploiting relationship directionality to enhance statistical modeling of peer‐influence across social networks Stat. Med. (IF 1.8) Pub Date : 2024-07-09 Xin Ran, Nancy E. Morden, Ellen Meara, Erika L. Moen, Daniel N. Rockmore, A. James O'Malley
Risky‐prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other “risky” drugs is a national public health concern. We use a novel data framework—a directed network connecting physicians who encounter the same patients in a sequence of visits—to investigate
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A Bayesian semi‐parametric scalar‐on‐function regression with measurement error using instrumental variables Stat. Med. (IF 1.8) Pub Date : 2024-07-09 Roger S Zoh, Yuanyuan Luan, Lan Xue, David B Allison, Carmen D Tekwe
Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device‐based physical activity measures are best treated as functions when assessing their associations with scalar‐valued outcomes such as