Statistics in Medicine最新文献

{"title":"Accounting for Misclassification of Cause of Death in Weighted Cumulative Incidence Functions for Causal Analyses.","authors":"Jessie K Edwards, Bonnie E Shook-Sa, Giorgos Bakoyannis, Paul N Zivich, Michael E Herce, Stephen R Cole","doi":"10.1002/sim.70281","DOIUrl":"https://doi.org/10.1002/sim.70281","url":null,"abstract":"<p><p>Misclassification between causes of death can produce bias in estimated cumulative incidence functions. When estimating causal quantities, such as comparing the cumulative incidence of death due to specific causes under interventions, such bias can lead to suboptimal decision making. Here, a consistent semiparametric estimator of the cumulative incidence function under interventions in settings with misclassification between two event types is presented. The measurement parameters for this estimator can be informed by validation data or expert knowledge. Moreover, a modified bootstrap approach to variance estimation is proposed for confidence interval construction. The proposed estimator was applied to estimate the cumulative incidence of AIDS-related mortality in the Multicenter AIDS Cohort Study under single- versus combination-drug antiretroviral therapy regimens that may be subject to confounding. The proposed estimator is shown to be consistent and performed well in finite samples via a series of simulation experiments.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70281"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239714","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":"Deep Mixture of Linear Mixed Models for Complex Longitudinal Data.","authors":"Lucas Kock, Nadja Klein, David J Nott","doi":"10.1002/sim.70288","DOIUrl":"10.1002/sim.70288","url":null,"abstract":"<p><p>Mixtures of linear mixed models are widely used for modeling longitudinal data for which observation times differ between subjects. In typical applications, temporal trends are described using a basis expansion, with basis coefficients treated as random effects varying by subject. Additional random effects can describe variation between mixture components or other known sources of variation in complex designs. A key advantage of these models is that they provide a natural mechanism for clustering. Current versions of mixtures of linear mixed models are not specifically designed for the case where there are many observations per subject and complex temporal trends, which require a large number of basis functions to capture. In this case, the subject-specific basis coefficients are a high-dimensional random effects vector, for which the covariance matrix is hard to specify and estimate, especially if it varies between mixture components. To address this issue, we consider the use of deep mixture of factor analyzers models as a prior for the random effects. The resulting deep mixture of linear mixed models is well suited for high-dimensional settings, and we describe an efficient variational inference approach to posterior computation. The efficacy of the method is demonstrated in biomedical applications and on simulated data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70288"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239632","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":"Estimating Risk Factors for Pathogenic Dose Accrual From Longitudinal Data.","authors":"Daniel K Sewell, Kelly K Baker","doi":"10.1002/sim.70291","DOIUrl":"10.1002/sim.70291","url":null,"abstract":"<p><p>Estimating risk factors for the incidence of a disease is crucial for understanding its etiology. For diseases caused by enteric pathogens, off-the-shelf statistical model-based approaches do not consider the biological mechanisms through which infection occurs and thus can only be used to make comparatively weak statements about the association between risk factors and incidence. Building off of established work in quantitative microbiological risk assessment, we propose a new approach to determining the association between risk factors and dose accrual rates. Our more mechanistic approach achieves a higher degree of biological plausibility, incorporates currently ignored sources of variability, and provides regression parameters that are easily interpretable as the dose accrual rate ratio due to changes in the risk factors under study. We also describe a method for leveraging information across multiple pathogens. The proposed methods are available as an R package at https://github.com/dksewell/dare. Our simulation study shows unacceptable coverage rates from generalized linear models, while the proposed approach empirically maintains the nominal rate even when the model is misspecified. Finally, we demonstrated our proposed approach by applying our method to infant data obtained through the PATHOME study (https://reporter.nih.gov/project-details/10227256), discovering the impact of various environmental factors on infant enteric infections.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70291"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239741","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":"An Approach to Design Adaptive Clinical Trials With Time-to-Event Outcomes Based on a General Bayesian Posterior Distribution.","authors":"James M McGree, Antony M Overstall, Mark Jones, Robert K Mahar","doi":"10.1002/sim.70207","DOIUrl":"10.1002/sim.70207","url":null,"abstract":"<p><p>Clinical trials are an integral component of medical research. Trials require careful design to, for example, maintain the safety of participants and to use resources efficiently. Adaptive clinical trials are often more efficient and ethical than standard or non-adaptive trials because they can require fewer participants, target more promising treatments, and stop early with sufficient evidence of effectiveness or harm. The design of adaptive trials is usually undertaken via simulation, which requires assumptions about the data-generating process to be specified a priori. Unfortunately, if such assumptions are misspecified, then the resulting trial design may not perform as expected, leading to, for example, reduced statistical power or an increased Type I error. Motivated by a clinical trial of a vaccine to protect against gastroenteritis in infants, we propose an approach to design adaptive clinical trials with time-to-event outcomes without needing to explicitly define the data-generating process. To facilitate this, we consider trial design within a general Bayesian framework where inference about the treatment effect is based on the partial likelihood. As a result, inference is robust to the form of the baseline hazard function, and we exploit this property to undertake trial design when the data-generating process is only implicitly defined. The benefits of this approach are demonstrated via an illustrative example and via redesigning our motivating clinical trial.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70207"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145252732","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":"Estimating and Evaluating Counterfactual Prediction Models.","authors":"Christopher B Boyer, Issa J Dahabreh, Jon A Steingrimsson","doi":"10.1002/sim.70287","DOIUrl":"10.1002/sim.70287","url":null,"abstract":"<p><p>Counterfactual prediction methods are required when a model will be deployed in a setting where treatment policies differ from the setting where the model was developed, or when a model provides predictions under hypothetical interventions to support decision-making. However, estimating and evaluating counterfactual prediction models is challenging because, unlike traditional (factual) prediction, one does not observe the potential outcomes for all individuals under all treatment strategies of interest. Here, we discuss how to estimate a counterfactual prediction model, how to assess the model's performance, and how to perform model and tuning parameter selection. We provide identification and estimation results for counterfactual prediction models and for multiple measures of counterfactual model performance, including loss-based measures, the area under the receiver operating characteristic curve, and the calibration curve. Importantly, our results allow valid estimates of model performance under counterfactual intervention even if the candidate prediction model is misspecified, permitting a wider array of use cases. We illustrate these methods using simulation and apply them to the task of developing a statin-naïve risk prediction model for cardiovascular disease.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70287"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239796","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":"Robust Estimation of Additive Shared-Frailty Models for Recurrent Event Data With Dependent Censoring.","authors":"Xin Chen, Jieli Ding, Liuquan Sun","doi":"10.1002/sim.70286","DOIUrl":"https://doi.org/10.1002/sim.70286","url":null,"abstract":"<p><p>Recurrent event data with dependent censoring frequently arise in medical follow-up studies. In analyzing such data, one main challenge is addressing the complex dependencies among the recurrent events, failure events, and censoring events. In this paper, we focus on additive shared-frailty models for recurrent event processes and failure times, and propose a robust estimation procedure that accommodates censoring times dependent on both recurrent and failure events, even after conditioning on observed covariates. Notably, our method does not require specifying the exact dependence structure between censoring and recurrent/failure times, nor does it assume a particular frailty distribution. We show that the resulting estimates are consistent and asymptotically normal. We further assess the method's finite-sample performance through simulation studies, and illustrate its practical utility with a hospitalization dataset.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70286"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245280","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":"Sensitivity Analyses for Missing in Repeatedly Measured Outcome Data.","authors":"James F Troendle, Aparajita Sur, Eric S Leifer, Tiffany Powell-Wiley","doi":"10.1002/sim.70282","DOIUrl":"10.1002/sim.70282","url":null,"abstract":"<p><p>We discuss practical aspects of conducting sensitivity analyses for missing data with a repeatedly measured outcome. Our motivation is a SMART trial with a repeatedly measured outcome subject to missingness. We discuss and describe delta-based controlled imputation approaches to conducting sensitivity analyses for such trials that typically use linear mixed models for their primary analysis. We find that delta-based sensitivity analyses for trials with repeatedly measured outcome variables are enhanced by using MICE for the imputation. Further, including last-observed-before-time covariates is critical for a repeatedly observed outcome. We also develop some novel metrics for judging the adequacy of sensitivity analyses. Trial Registration: Tailoring Mobile Health Technology to Reduce Obesity and Improve Cardiovascular Health in Resource-Limited Neighborhood Environments: NCT03288207.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 23-24","pages":"e70282"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245265","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":"Multiplicity Control in Oncology Clinical Trials With a Binary Surrogate Endpoint-Based Drop-The-Losers Design.","authors":"Weibin Zhong, Jing-Ou Liu, Chenguang Wang","doi":"10.1002/sim.70209","DOIUrl":"https://doi.org/10.1002/sim.70209","url":null,"abstract":"<p><p>Typical phase 1 oncology studies identify the maximum tolerated dose as the \"optimal\" dose for subsequent phases. With the advancement of molecular targeted agents and immunotherapies, however, evaluating two or more doses has become increasingly critical for dose selection. Such evaluation is often done in phase 2 studies in a randomized manner. In this article, we evaluate the strategy of applying an adaptive phase 2/3 seamless design for dose selection in oncology studies. Specifically, we consider the \"drop-the-losers\" design, where multiple treatment arms and a control arm are administered during the initial stage, and a more effective arm is identified for later stages by a binary surrogate endpoint such as overall response. We derive the theoretical type I error inflation scale and conduct simulation studies to illustrate the impact of various factors on the type I error inflation in such designs. Furthermore, we demonstrate the findings through the design of a lung cancer trial and introduce a software that implements the proposed design.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70209"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006559","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":"Equivalency Between the Generalized Bivariate Bernoulli Model Dependency Test and a Logistic Regression Model With Interaction Effects.","authors":"Kazi Md Farhad Mahmud, Yanming Li, Devin C Koestler","doi":"10.1002/sim.70260","DOIUrl":"https://doi.org/10.1002/sim.70260","url":null,"abstract":"<p><strong>Background: </strong>Binary endpoints measured at two timepoints-such as pre- and post-treatment-are common in biomedical and healthcare research. The Generalized Bivariate Bernoulli Model (GBBM) provides a specialized framework for analyzing such bivariate binary data, allowing for formal tests of covariate-dependent associations conditional on baseline outcomes. Despite its potential utility, the GBBM remains underutilized due to the lack of direct implementation in standard statistical software. Moreover, we contend that the comparison made in the original publication between the GBBM dependency test and the regressive logistic regression model has shortcomings and does not provide an ideal basis for evaluating the model's performance.</p><p><strong>Methods: </strong>In this paper, we propose a standard logistic regression model with an interaction term and demonstrate that it yields an equivalent dependency test to the GBBM approach. This equivalence is established conceptually, theoretically, and empirically. Extensive simulations compared the power of the GBBM dependency test with: (a) dependency test from the regressive logistic model; (b) test derived from the logistic regression model with interaction; and (c) the Pearson Chi-square test. We also applied these methods to infant mortality data from the Bangladesh Demographic and Health Survey (BDHS).</p><p><strong>Results: </strong>The power of the GBBM dependency test differs from the regressive logistic regression model used as a benchmark in the original paper that introduced the GBBM methodology. In contrast, the power and type 1-error rate of the GBBM dependency test and the logistic regression model with interaction described herein are equivalent across varying effect sizes and sample sizes.</p><p><strong>Conclusion: </strong>Our work reveals that a widely available and flexible logistic regression model can serve as a practical alternative to the GBBM dependency test, enhancing accessibility for researchers. Moreover, this approach provides a foundation for extending dependency analyses to more complex longitudinal binary data structures, broadening its applicability in biomedical research.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70260"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016129","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 Algorithm for Sampling Parameters in a Structured Correlation Matrix With Application to Estimating Optimal Combinations of Muscles to Quantify Progression in Duchenne Muscular Dystrophy.","authors":"Michael K Kim, Michael J Daniels, William D Rooney, Rebecca J Willcocks, Glenn A Walter, Krista H Vandenborne","doi":"10.1002/sim.70252","DOIUrl":"https://doi.org/10.1002/sim.70252","url":null,"abstract":"<p><p>The goal of this paper is to estimate an optimal combination of biomarkers for individuals with Duchenne muscular dystrophy (DMD), which provides the most sensitive combinations of biomarkers to assess disease progression (in this case, optimal with respect to standardized response mean (SRM) for 4 muscle biomarkers). The biomarker data is incomplete (missing and irregular) multivariate longitudinal data. We propose a normal model with structured covariance designed for our setting. To sample from the posterior distribution of parameters, we develop a Markov Chain Monte Carlo (MCMC) algorithm to address the positive definiteness constraint on the structured correlation matrix. In particular, we propose a novel approach to compute the support of the parameters in the structured correlation matrix; we modify the approach from [1] on the set of the largest possible submatrices of the correlation matrix, where the correlation parameter is a unique element. For each posterior sample, we compute the optimal weights of our construct. We conduct data analysis and simulation studies to evaluate the algorithm and the frequentist properties of the posteriors of correlations and weights. We found that the lower extremities are the most responsive muscles at the early and late ambulatory disease stages, and the biceps brachii is the most responsive at the nonambulatory disease stage.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70252"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024172","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}