Statistics in Medicine最新文献

{"title":"Semiparametric Inference for a Two-Phase Failure-Time-Auxiliary-Dependent Sampling Design.","authors":"Xu Cao, Qingning Zhou, Jianwen Cai, Haibo Zhou","doi":"10.1002/sim.70239","DOIUrl":"https://doi.org/10.1002/sim.70239","url":null,"abstract":"<p><p>Large cohort studies under simple random sampling could be prohibitive to conduct for epidemiological studies with a limited budget, especially when exposure variables are expensive or hard to obtain. Failure-time-dependent sampling (FDS) is a commonly used cost-effective sampling strategy for studies with failure times as outcomes. To further enhance study efficiency upon FDS, we propose a two-phase failure-time-auxiliary-dependent sampling (FADS) design that allows the probability of obtaining the expensive exposures to depend on both the failure time and some cheaply available auxiliary variables to the main exposure of interest. To account for the sampling bias, we develop a semiparametric maximum pseudo-likelihood approach for inference and a nonparametric bootstrap procedure for variance estimation. The proposed estimator of regression coefficients is shown to be consistent and asymptotically normally distributed. The simulation studies indicate that our proposed method works well in practical settings and is more efficient than other competing sampling schemes or methods. We illustrate our method with the analysis of two real data sets, the ARIC Study and the National Wilms' Tumor Study.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 18-19","pages":"e70239"},"PeriodicalIF":1.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Dirichlet-Multinomial Mixture Regression Model for the Analysis of Microbiome Data.","authors":"Roberto Ascari, Sonia Migliorati, Andrea Ongaro","doi":"10.1002/sim.70220","DOIUrl":"10.1002/sim.70220","url":null,"abstract":"<p><p>Motivated by the challenges in analyzing gut microbiome and metagenomic data, this paper introduces a novel mixture distribution for multivariate counts and a regression model built upon it. The flexibility and interpretability of the proposed distribution accommodate both negative and positive dependence among taxa and are accompanied by numerous theoretical properties, including explicit expressions for inter- and intraclass correlations, thereby providing a powerful tool for understanding complex microbiome interactions. Furthermore, the regression model based on this distribution facilitates the clear identification and interpretation of relationships between taxa and covariates by modeling the marginal mean of the multivariate response (i.e., taxa counts). Inference is performed using a tailored Hamiltonian Monte Carlo estimation method combined with a spike-and-slab variable selection procedure. Extensive simulation studies and an application to a human gut microbiome dataset highlight the proposed model's substantial improvements over competing models in terms of fit, interpretability, and predictive performance.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 18-19","pages":"e70220"},"PeriodicalIF":1.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12330782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144795516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure Measurement Error Correction in Longitudinal Studies With Discrete Outcomes.","authors":"Ce Yang, Ning Zhang, Jiaxuan Li, Unnati V Mehta, Jaime E Hart, Donna L Spiegelman, Molin Wang","doi":"10.1002/sim.70191","DOIUrl":"10.1002/sim.70191","url":null,"abstract":"<p><p>Environmental epidemiologists are often interested in estimating the effect of time-varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main study/validation study design is developed. Various estimation procedures are explored. Simulation studies were conducted to assess its performance compared to standard analysis, and we found that the proposed method had good performance in terms of finite sample bias reduction and nominal coverage probability improvement. As an illustrative example, we applied the new method to a study of long-term exposure to <math> <semantics> <mrow> <msub><mrow><mtext>PM</mtext></mrow> <mrow><mn>2</mn> <mo>.</mo> <mn>5</mn></mrow> </msub> </mrow> <annotation>$$ {mathrm{PM}}_{2.5} $$</annotation></semantics> </math> , in relation to the occurrence of anxiety disorders in the Nurses' Health Study II. Failing to correct the error-prone exposure can lead to an underestimation of the chronic exposure effect of <math> <semantics> <mrow> <msub><mrow><mtext>PM</mtext></mrow> <mrow><mn>2</mn> <mo>.</mo> <mn>5</mn></mrow> </msub> </mrow> <annotation>$$ {mathrm{PM}}_{2.5} $$</annotation></semantics> </math> .</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70191"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agreement Between Two Quantitative Measurement Methods When the Underlying Latent Trait Is Not Constant.","authors":"Patrick Taffé","doi":"10.1002/sim.70164","DOIUrl":"https://doi.org/10.1002/sim.70164","url":null,"abstract":"<p><p>Most statistical methods that have been developed to assess the agreement between two quantitative measurement methods have (implicitly) relied on the assumption of a constant \"individual\" latent trait. This might be inappropriate when the \"individual\" is not an object but a person. Therefore, the goal of this study was to extend the standard measurement error model to cope with this limit. Four different settings were investigated: first, where the true individual latent trait was constant; second, where it was variable but without exhibiting a time trend; third, where it followed a linear time trend; and fourth, where it exhibited an approximate linear time trend. Two competing methods to estimate the parameters of the general measurement error model were assessed: the GLS estimator of Sprent and the two-stage method of Taffé. It was found that the latter generally performed better than the former to estimate the bias. In addition, it can be used when there is only a single measurement per individual by one of the two measurement methods, which is not the case with the former method.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70164"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Linear Combination of Biomarkers by Weighted Youden Index Maximization.","authors":"Sizhe Wang, Fang Fang, Jialiang Li","doi":"10.1002/sim.70182","DOIUrl":"https://doi.org/10.1002/sim.70182","url":null,"abstract":"<p><p>In medical research, it is common practice to combine various biomarkers to improve the accuracy of disease diagnosis. The weighted Youden index (WYI), which assigns unequal weights to sensitivity and specificity based on their relative importance, serves as an important and flexible evaluation metric of diagnostic tests. However, no existing methods have been designed specifically to identify the optimal linear combination of biomarkers that maximizes the WYI. In this paper, we propose a novel method to construct an optimal diagnosis score and determine the best cut-off point at the same time. The estimated combination coefficients and cut-off point are shown to have cube root asymptotics, and their joint limiting distribution is established rigorously. Further, the asymptotic normality of the optimal in-sample WYI is established, and out-of-sample inference for score distribution and comparison is investigated. These results provide deep theoretical insights for methods of Youden index maximization for the first time. Computationally, an iterative marginal optimization algorithm, different from the existing literature, is adopted to deal with the objective function that is neither continuous nor smooth. Simulation studies support the theoretical results and demonstrate the superiority of the proposed method. Two real-world examples-coronary disease and Alzheimer's disease diagnosis-are presented for illustration.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70182"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Bayesian Inference in the Multilevel Zero-Inflated Generalized Poisson Model.","authors":"Mekuanint Simeneh Workie, Xu Yi","doi":"10.1002/sim.70173","DOIUrl":"https://doi.org/10.1002/sim.70173","url":null,"abstract":"<p><p>Outliers, over-dispersion, and zero inflation are issues with count data. Traditional models like Poisson and negative binomial often fail to account for these issues, leading to biased estimates and poor model fit. These frameworks are extended by the Zero-Inflated Generalized Poisson (ZIGP) model, which takes into consideration not only zero inflation but also over-dispersion or under-dispersion. However, in the presence of outliers and hierarchical data structures. This study develops a robust Bayesian inference framework for the multilevel ZIGP model. Standard Bayesian methods often lack robustness under model misspecification and in the presence of outlier data. The framework uses a Robust expectation solution (RES) algorithm and generalized Bayesian inference (GBI) for robust estimation against outliers. These approaches improve estimation accuracy using robust loss functions and scaling parameters to minimize the influence of outliers. Simulation studies confirm that the Robust Expectation Solution (RES) algorithm significantly outperformed the Expectation-Maximization (EM) algorithm in reducing bias and mean squared error (MSE), especially in the presence of outliers. Regular Bayesian and EM algorithms were more sensitive to outliers, leading to potential bias and instability in parameter estimates. Our robust Bayesian framework, specifically the Generalized Bayesian Inference (GBI), demonstrated improved robustness and stability under model misspecification and outlier contamination. The main results show that tuning quantiles and optimizing scaling parameters improved parameter calibration and reduced bias and mean square error (MSE). We applied the framework to neonatal mortality data, identifying key risk factors such as maternal education, wealth status, rural residence, and age at first birth.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70173"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidence Intervals for AUC and pAUC by Empirical Likelihood.","authors":"Yumin Zhao, Xue Ding, Mai Zhou","doi":"10.1002/sim.70192","DOIUrl":"https://doi.org/10.1002/sim.70192","url":null,"abstract":"<p><p>The area under the receiver operating characteristic curve (AUC) and Partial AUC (pAUC) are often used to measure the performance of medical diagnostic tests. Under nonparametric settings, we propose and illustrate in this paper a two-sample empirical likelihood approach to test hypotheses and construct confidence intervals for AUC and pAUC. The empirical likelihood ratio test in our setup yields an asymptotic chi-square distribution under null hypothesis. Thus, there is no need to estimate the complicated scale factor or the variance of the nonparametric AUC/pAUC estimators like most other competing methods do. Simulations show our method is very competitive. In fact, our method tops competitors in every situation we simulated. Real data examples (with R code) are presented illustrating the statistical tests and confidence intervals for AUC and pAUC.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70192"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate Adjustments for Average Equivalence Testing.","authors":"Younes Boulaguiem, Luca Insolia, Maria-Pia Victoria-Feser, Dominique-Laurent Couturier, Stéphane Guerrier","doi":"10.1002/sim.10258","DOIUrl":"10.1002/sim.10258","url":null,"abstract":"<p><p>Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are \"equivalent\" for different outcomes simultaneously. In pharmacological research for example, many regulatory agencies require the generic product and its brand-name counterpart to have equivalent means both for the AUC and C_max pharmacokinetics parameters. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal <math> <semantics><mrow><mn>100</mn> <mrow><mo>(</mo> <mrow><mn>1</mn> <mo>-</mo> <mn>2</mn> <mi>α</mi></mrow> <mo>)</mo></mrow> <mo>%</mo></mrow> <annotation>$$ 100left(1-2alpha right)% $$</annotation></semantics> </math> confidence intervals for the difference in means between the two conditions of interest lie within predefined lower and upper equivalence limits. This procedure, already known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> -TOST, that consists in a correction of <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> , the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define <math> <semantics> <mrow><msup><mi>α</mi> <mo>*</mo></msup> </mrow> <annotation>$$ {alpha}^{ast } $$</annotation></semantics> </math> , the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between <math> <semantics> <mrow><msup><mi>α</mi> <mo>*</mo></msup> </mrow> <annotation>$$ {alpha}^{ast } $$</annotation></semantics> </math> and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> -TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses-that is, relatively small sample sizes, unknown and possibly heterogeneous variances as well as different correlation structures-and show the superior finite-sample properties of the multivariate <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> -TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e10258"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12258420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-Varying Bayesian Network Meta-Analysis.","authors":"Patrick M LeBlanc, David Banks","doi":"10.1002/sim.70160","DOIUrl":"10.1002/sim.70160","url":null,"abstract":"<p><p>The presence of methicillin-resistant Staphylococcus Aureus (MRSA) in complicated skin and soft structure infections (cSSSI) is associated with greater health risks and economic costs to patients. There is concern that MRSA is becoming resistant to other \"gold standard\" treatments such as vancomycin, and there is disagreement about the relative efficacy of vancomycin compared to linezolid. There are several review papers employing Bayesian Network Meta-Analyses (BNMAs) to investigate which treatments are best for MRSA-related cSSSIs, but none address time-based design inconsistencies. This paper proposes a time-varying BNMA (tBNMA), which models time-varying treatment effects across studies using a Gaussian Process kernel. A dataset is compiled from nine existing MRSA cSSSI NMA review papers containing 58 studies comparing 19 treatments over 19 years. The tBNMA finds evidence of a non-linear trend in the treatment effect of vancomycin-it became less effective than linezolid between 2002 and 2007, but has since recovered statistical equivalence.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70160"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sample Size Calculations for Partially Clustered Trials.","authors":"Kylie M Lange, Jessica Kasza, Thomas R Sullivan, Lisa N Yelland","doi":"10.1002/sim.70172","DOIUrl":"10.1002/sim.70172","url":null,"abstract":"<p><p>Partially clustered trials are defined as trials where some observations belong to a cluster and others are independent. For example, neonatal trials may include infants from a single, twin, or triplet birth. The clustering of observations in partially clustered trials should be accounted for when determining the target sample size to avoid being over or underpowered. However, sample size methods have only been developed for limited partially clustered trial designs (e.g., designs with maximum cluster sizes of 2). In this article, we present new design effects that can be used to determine the sample size for two-arm, parallel, partially clustered trials where clusters exist pre-randomization. Design effects are derived algebraically for continuous and binary outcomes, assuming a generalized estimating equations-based approach to estimation with either an independence or exchangeable working correlation structure. Both cluster and individual randomization are considered for the clustered observations. The design effects are shown to depend on the intracluster correlation coefficient, proportion of observations that belong to clusters of each size, method of randomization, type of outcome, and working correlation structure. The design effects are validated through a simulation study. Example sample size calculations are presented to illustrate how the design effects can be used to determine the target sample size for different partially clustered trial designs. The design effects depend on parameters that can be feasibly estimated when planning a trial and can be used to ensure that partially clustered trials are appropriately powered in the future.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70172"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}