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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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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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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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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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Two‐stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers Stat. Med. (IF 2.0) Pub Date : 2024-02-27 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 : 2024-02-27 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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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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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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Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial Stat. Med. (IF 2.0) Pub Date : 2024-02-20 Gerardo Chowell, Amanda Bleichrodt, Ruiyan Luo
Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy-to-use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters
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Handling missing disease information due to death in diseases that need two visits to diagnose Stat. Med. (IF 2.0) Pub Date : 2024-02-21 Le Thi Phuong Thao, Rory Wolfe, Stephane Heritier, Ronald Geskus
In studies that assess disease status periodically, time of disease onset is interval censored between visits. Participants who die between two visits may have unknown disease status after their last visit. In this work, we consider an additional scenario where diagnosis requires two consecutive positive tests, such that disease status can also be unknown at the last visit preceding death. We show
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Martingale‐residual‐based greedy model averaging for high‐dimensional current status data Stat. Med. (IF 2.0) Pub Date : 2024-02-21 Chang Wang, Mingyue Du
Current status data are a type of failure time data that arise when the failure time of study subject cannot be determined precisely but is known only to occur before or after a random monitoring time. Variable selection methods for the failure time data have been discussed extensively in the literature. However, the statistical inference of the model selected based on the variable selection method
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Are the tests overpowered or underpowered? A unified solution to correctly specify type I errors in design of clinical trials for two sample proportions Stat. Med. (IF 2.0) Pub Date : 2024-02-19 Peiran Liu, Ming-Hui Chen, Susie Sinks, Peng Sun
As one of the most commonly used data types, methods in testing or designing a trial for binary endpoints from two independent populations are still being developed until recently. However, the power and the minimum required sample size comparisons between different tests may not be valid if their type I errors are not controlled at the same level. In this article, we unify all related testing procedures
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Statistical plasmode simulations–Potentials, challenges and recommendations Stat. Med. (IF 2.0) Pub Date : 2024-02-14 Nicholas Schreck, Alla Slynko, Maral Saadati, Axel Benner
Statistical data simulation is essential in the development of statistical models and methods as well as in their performance evaluation. To capture complex data structures, in particular for high-dimensional data, a variety of simulation approaches have been introduced including parametric and the so-called plasmode simulations. While there are concerns about the realism of parametrically simulated
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Shape restricted additive hazards models: Monotone, unimodal, and U-shape hazard functions Stat. Med. (IF 2.0) Pub Date : 2024-02-14 Yunro Chung, Anastasia Ivanova, Jason P. Fine
We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards
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Methods for the estimation of direct and indirect vaccination effects by combining data from individual- and cluster-randomized trials Stat. Med. (IF 2.0) Pub Date : 2024-02-13 Rui Wang, Mengqi Cen, Yunda Huang, George Qian, Natalie E. Dean, Susan S. Ellenberg, Thomas R. Fleming, Wenbin Lu, Ira M. Longini
Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease
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Utilizing local likelihood in regression discontinuity design: Investigating the impact of antiretroviral therapy eligibility on retention in clinical HIV care in South Africa Stat. Med. (IF 2.0) Pub Date : 2024-02-13 Jaehyun Seo, Chanmin Kim
The regression discontinuity (RD) design is a widely utilized approach for assessing treatment effects. It involves assigning treatment based on the value of an observed covariate in relation to a fixed threshold. Although the RD design has been widely employed across various problems, its application to specific data types has received limited attention. For instance, there has been little research
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Modeling correlated pairs of mammogram images Stat. Med. (IF 2.0) Pub Date : 2024-02-13 Shu Jiang, Graham A. Colditz
Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however
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Distribution–free hyperrectangular tolerance regions for setting multivariate reference regions in laboratory medicine Stat. Med. (IF 2.0) Pub Date : 2024-02-11 Wei Liu, Frank Bretz, Mario Cortina–Borja
Reference regions are important in laboratory medicine to interpret the test results of patients, and usually given by tolerance regions. Tolerance regions of p (≥2)$$ p\;\left(\ge 2\right) $$ dimensions are highly desirable when the test results contains p$$ p $$ outcome measures. Nonparametric hyperrectangular tolerance regions are attractive in real problems due to their robustness with respect
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A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design Stat. Med. (IF 2.0) Pub Date : 2024-02-12 Joshua L. Warren, Qi Wang, Maria M. Ciarleglio
Incorporating historical data into a current data analysis can improve estimation of parameters shared across both datasets and increase the power to detect associations of interest while reducing the time and cost of new data collection. Several methods for prior distribution elicitation have been introduced to allow for the data-driven borrowing of historical information within a Bayesian analysis
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Point estimation, confidence intervals, and P-values for optimal adaptive two-stage designs with normal endpoints Stat. Med. (IF 2.0) Pub Date : 2024-02-10 Jan Meis, Maximilian Pilz, Björn Bokelmann, Carolin Herrmann, Geraldine Rauch, Meinhard Kieser
Due to the dependency structure in the sampling process, adaptive trial designs create challenges in point and interval estimation and in the calculation of P-values. Optimal adaptive designs, which are designs where the parameters governing the adaptivity are chosen to maximize some performance criterion, suffer from the same problem. Various analysis methods which are able to handle this dependency
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Correction to “Using principal stratification in analysis of clinical trials” Stat. Med. (IF 2.0) Pub Date : 2024-02-08 Ilya Lipkovich, Bohdana Ratitch, Yongming Qu, Xiang Zhang, Mingyang Shan, Craig Mallinckrodt
We would like to make a correction to the paper titled “Using principal stratification in analysis of clinical trials” in Statistics in Medicine. In Section 11.2, Equations (14) and (15) for expressions T3$$ {T}_3 $$ and T4$$ {T}_4 $$, respectively, have to be corrected as shown in the updated text below. Similarly to f1(y|I)$$ {f}_1\left(y|I\right) $$, we can express f0(y|I)$$ {f}_0\left(y|I\right)
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Generalized functional linear model with a point process predictor Stat. Med. (IF 2.0) Pub Date : 2024-02-08 Jiehuan Sun, Kuang-Yao Lee
Point process data have become increasingly popular these days. For example, many of the data captured in electronic health records (EHR) are in the format of point process data. It is of great interest to study the association between a point process predictor and a scalar response using generalized functional linear regression models. Various generalized functional linear regression models have been
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Scaled average bioequivalence methods for highly variable drugs: Leveling-off soft limits and the EMA's 2010 guideline (some ways to improve its type I error control) Stat. Med. (IF 2.0) Pub Date : 2024-02-05 Joel Muñoz, Jordi Ocaña, Rolando Suárez, Carolina Millapán
The regulatory EMA's reference scaled average bioequivalence (RSABE) approach for highly variable drugs suffers from some type I error control problems at the neighborhood of the 30% coefficient of variation (CV), where the bioequivalence (BE) limits change from constant to linearly scaled. This paper analyses BE inference methods based on the “Leveling-off” (LO) soft sigmoid expanding BE limits that
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Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes Stat. Med. (IF 2.0) Pub Date : 2024-02-05 Dustin J. Rabideau, Fan Li, Rui Wang
Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate-conditional
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Multivariate probit linear mixed models for multivariate longitudinal binary data Stat. Med. (IF 2.0) Pub Date : 2024-02-06 Kuo-Jung Lee, Chanmin Kim, Jae Keun Yoo, Keunbaik Lee
When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross-correlations between different responses at different times, and correlations between different responses at each time point. The number
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Simultaneous variable selection and estimation for survival data via the Gaussian seamless-L0 penalty Stat. Med. (IF 2.0) Pub Date : 2024-02-06 Zili Liu, Hong Wang
We propose a new simultaneous variable selection and estimation procedure with the Gaussian seamless-L0$$ {L}_0 $$ (GSELO) penalty for Cox proportional hazard model and additive hazards model. The GSELO procedure shows good potential to improve the existing variable selection methods by taking strength from both best subset selection (BSS) and regularization. In addition, we develop an iterative algorithm
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Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses Stat. Med. (IF 2.0) Pub Date : 2024-02-06 Zhuo Meng, Jingshen Wang, Lifeng Lin, Chong Wu
Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid
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Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety Stat. Med. (IF 2.0) Pub Date : 2024-02-05 Jason Poulos, Marcela Horvitz-Lennon, Katya Zelevinsky, Tudor Cristea-Platon, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Sharon-Lise Normand
We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39 000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum
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Statistical inference on qualitative differences in the magnitude of an effect Stat. Med. (IF 2.0) Pub Date : 2024-02-02 Aaron Hudson, Ali Shojaie
Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is
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Effective sample size: A measure of individual uncertainty in predictions Stat. Med. (IF 2.0) Pub Date : 2024-01-31 Doranne Thomassen, Saskia le Cessie, Hans C. van Houwelingen, Ewout W. Steyerberg
Clinical prediction models are estimated using a sample of limited size from the target population, leading to uncertainty in predictions, even when the model is correctly specified. Generally, not all patient profiles are observed uniformly in model development. As a result, sampling uncertainty varies between individual patients' predictions. We aimed to develop an intuitive measure of individual
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Safety signal detection with control of latent factors Stat. Med. (IF 2.0) Pub Date : 2024-01-31 Xianming Tan, William Wang, Donglin Zeng, Guanghan F. Liu, Guoqing Diao, Niusha Jafari, Ethan M. Alt, Joseph G. Ibrahim
Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest
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Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities Stat. Med. (IF 2.0) Pub Date : 2024-01-30 Zhiyuan Du, Pang Du, Aiyi Liu
The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the
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Addressing subject heterogeneity in time-dependent discrimination for biomarker evaluation Stat. Med. (IF 2.0) Pub Date : 2024-01-29 Xinyang Jiang, Wen Li, Ruosha Li, Jing Ning
Accurate discrimination has been the central goal in identifying biomarkers for monitoring disease progression and early detection. Acknowledging the fact that discrimination accuracy of biomarkers for a time-to-event outcome often changes over time, local measures such as the time-dependent receiver operating characteristic curve and its area under the curve (AUC) are used to assess time-dependent
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Nonparametric estimation of linear personalized diagnostics rules via efficient grid algorithm Stat. Med. (IF 2.0) Pub Date : 2024-01-29 Yaliang Zhang, Yunro Chung
Many diseases are heterogeneous, comprised of multiple disease subgroups. It is of great interest but highly unlikely to find a single biomarker that can accurately detect such heterogeneous diseases across different subgroups. In this article, we propose to estimate a personalized diagnostic rule (PDR) to tailor more effective biomarkers to each individual according to a linear combination of his
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Classified functional mixed effects model prediction Stat. Med. (IF 2.0) Pub Date : 2024-01-27 Xiaoyan Liu, Jiming Jiang
In nowadays biomedical research, there has been a growing demand for making accurate prediction at subject levels. In many of these situations, data are collected as longitudinal curves and display distinct individual characteristics. Thus, prediction mechanisms accommodated with functional mixed effects models (FMEM) are useful. In this paper, we developed a classified functional mixed model prediction
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Optimizing dynamic predictions from joint models using super learning Stat. Med. (IF 2.0) Pub Date : 2024-01-25 Dimitris Rizopoulos, Jeremy M. G. Taylor
Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding
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Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects Stat. Med. (IF 2.0) Pub Date : 2024-01-25 Carly Lupton Brantner, Trang Quynh Nguyen, Tengjie Tang, Congwen Zhao, Hwanhee Hong, Elizabeth A. Stuart
Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric
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Multiple imputation strategies for missing event times in a multi-state model analysis Stat. Med. (IF 2.0) Pub Date : 2024-01-22 Elinor Curnow, Rachael A. Hughes, Kate Birnie, Kate Tilling, Michael J. Crowther
In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times
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Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity Stat. Med. (IF 2.0) Pub Date : 2024-01-23 Zhongying Xu, Tianzhou Ma, Lu Tang, Victor B. Talisa, Chung-Chou H. Chang
Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for
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Efficient estimation of Cox model with random change point Stat. Med. (IF 2.0) Pub Date : 2024-01-21 Xuerong Chen, Yalu Ping, Jianguo Sun
In clinical studies, the risk of a disease may dramatically change when some biological indexes of the human body exceed some thresholds. Furthermore, the differences in individual characteristics of patients such as physical and psychological experience may lead to subject-specific thresholds or change points. Although a large literature has been established for regression analysis of failure time
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Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI Stat. Med. (IF 2.0) Pub Date : 2024-01-21 Rengaraju Perumalraja, B. Felcia Logan's Deshna, N. Swetha
The growth of artificial intelligence (AI) in the healthcare industry tremendously increases the patient outcomes by reshaping the way we diagnose, treat and monitor patients. AI-based innovation in healthcare include exploration of drugs, personalized medicine, clinical diagnosis investigations, robotic-assisted surgery, verified prescriptions, pregnancy care for women, radiology, and reviewed patient
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Steady-state statistical properties and implementation of randomization designs with maximum tolerated imbalance restriction for two-arm equal allocation clinical trials Stat. Med. (IF 2.0) Pub Date : 2024-01-20 Wenle Zhao, Kerstine Carter, Oleksandr Sverdlov, Annika Scheffold, Yevgen Ryeznik, Christy Cassarly, Vance W. Berger
In recent decades, several randomization designs have been proposed in the literature as better alternatives to the traditional permuted block design (PBD), providing higher allocation randomness under the same restriction of the maximum tolerated imbalance (MTI). However, PBD remains the most frequently used method for randomizing subjects in clinical trials. This status quo may reflect an inadequate
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τ-Inflated beta regression model for censored recurrent events Stat. Med. (IF 2.0) Pub Date : 2024-01-20 Yizhuo Wang, Susan Murray
This research introduces a multivariate τ$$ \tau $$-inflated beta regression (τ$$ \tau $$-IBR) modeling approach for the analysis of censored recurrent event data that is particularly useful when there is a mixture of (a) individuals who are generally less susceptible to recurrent events and (b) heterogeneity in duration of event-free periods amongst those who experience events. The modeling approach
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A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater Stat. Med. (IF 2.0) Pub Date : 2024-01-14 Xiaotian Dai, Nicole Acosta, Xuewen Lu, Casey R. J. Hubert, Jangwoo Lee, Kevin Frankowski, Maria A. Bautista, Barbara J. Waddell, Kristine Du, Janine McCalder, Jon Meddings, Norma Ruecker, Tyler Williamson, Danielle A. Southern, Jordan Hollman, Gopal Achari, M. Cathryn Ryan, Steve E. Hrudey, Bonita E. Lee, Xiaoli Pang, Rhonda G. Clark, Michael D. Parkins, Thierry Chekouo
Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for
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Confidence distributions for treatment effects in clinical trials: Posteriors without priors Stat. Med. (IF 2.0) Pub Date : 2024-01-11 Ian C. Marschner
An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify
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Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity Stat. Med. (IF 2.0) Pub Date : 2024-01-10 Andrea Cremaschi, Maria De Iorio, Narasimhan Kothandaraman, Fabian Yap, Mya Thway Tint, Johan Eriksson
The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose