Statistics in Medicine最新文献

{"title":"A Novel Method for the Fair Assessment of Healthcare Quality Measures and Providers Care Consistency.","authors":"Cedric Neumann, Andrew Anthony Matas, Jeffrey Jamison Geppert","doi":"10.1002/sim.70722","DOIUrl":"10.1002/sim.70722","url":null,"abstract":"<p><p>The selection and use of quality measures in value-based payment rely on single-period methods that conflate sampling noise with genuine within-provider year-to-year execution variability, producing assessments that are unstable across years and dependent on patient volume. We introduce a Longitudinal Beta-Binomial model, which separates three sources of variability simultaneously and produces a year-invariant measure-level assessment <math><mrow><mi>τ</mi></mrow> </math> and a volume-independent provider-specific assessment <math> <mrow> <msub><mrow><mi>τ</mi></mrow> <mrow><mi>n</mi></mrow> </msub> </mrow> </math> . Applied to five years of data (program years 2021-2025) for 744 providers on two CMS mental health quality measures, the analysis yields a striking reversal: under the standard Beta-Binomial framework, FUH-30 appears far more discriminating than READM-30-IPF ( <math> <mrow> <msup><mrow><mi>ρ</mi></mrow> <mrow><mo>(</mo> <mi>j</mi> <mo>)</mo></mrow> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>78</mn></mrow> </math> -0.87 vs 0.51-0.63 per year); separating execution variability from sampling noise reverses this conclusion ( <math><mrow><mi>τ</mi> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>621</mn></mrow> </math> [0.598, 0.642] for FUH-30 vs <math><mrow><mi>τ</mi> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>758</mn></mrow> </math> [0.730, 0.784] for READM-30-IPF, 95% posterior credible intervals), because FUH-30's year-to-year provider instability is three times larger than its sampling noise. The framework proposes three requirements simultaneously: discriminability ( <math><mrow><mi>τ</mi></mrow> </math> can separate good from poor health-care providers and is year-invariant where <math> <mrow> <msup><mrow><mi>ρ</mi></mrow> <mrow><mo>(</mo> <mi>j</mi> <mo>)</mo></mrow> </msup> </mrow> </math> swings substantially across years); fairness (both <math><mrow><mi>τ</mi></mrow> </math> and <math> <mrow> <msub><mrow><mi>τ</mi></mrow> <mrow><mi>n</mi></mrow> </msub> </mrow> </math> are independent of patient volume, removing the structural disadvantage faced by small and rural providers); and actionability ( <math> <mrow> <msub><mrow><mi>τ</mi></mrow> <mrow><mi>n</mi></mrow> </msub> </mrow> </math> distinguishes consistently substandard from erratic providers, enabling targeted intervention and more honest measure adoption decisions).</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70722"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148865713","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":"ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.","authors":"Suvo Chatterjee, Siddhant Meshram, Ganesan Arunkumar, Fasil Tekola-Ayele, Arindam Fadikar","doi":"10.1002/sim.70716","DOIUrl":"10.1002/sim.70716","url":null,"abstract":"<p><p>Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70716"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819439","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":"Self-Adapting Priors for Dynamic Borrowing in Three-Arm Non-Inferiority Trials With Pre-Specified Margin.","authors":"Yuansong Zhao, Ying Yuan, Ram Tiwari, Samiran Ghosh","doi":"10.1002/sim.70706","DOIUrl":"10.1002/sim.70706","url":null,"abstract":"<p><p>Non-inferiority trials play a central role in clinical research by demonstrating that an experimental treatment is not unacceptably worse than an established reference treatment when superiority is unlikely, but the new treatment offers other advantages, such as reduced toxicity, easier administration, lower cost, or improved adherence. These trials are typically designed as active control studies, making historical data from previous clinical trials or real-world evidence readily available. A key challenge, however, is how to appropriately incorporate multiple heterogeneous historical datasets into the design and analysis of a non-inferiority trial, as each source may differ in relevance, quality, and similarity to the current study. Moreover, a conventional two-arm non-inferiority trial lacks a placebo or standard of care control, requiring the reference treatment effect to be justified using external evidence. When ethically and practically feasible, including a placebo arm allows assessment of assay sensitivity and provides internal validation of the reference treatment effect, thereby strengthening the credibility of the non-inferiority conclusion. In this article, we propose two novel Bayesian self-adapting priors, the Additive Self-Adapting Mixture (ASAM) prior and the Cumulative Self-Adapting Product (CSAP) prior, to incorporate information from multiple historical reference and placebo studies in three-arm non-inferiority trials. The ASAM prior constructs study-specific priors and combines them through an adaptively weighted mixture, whereas the CSAP prior uses a cumulative product formulation with adaptive study-specific weights. Both approaches dynamically borrow information by assigning greater weight to historical studies that are more compatible with the current trial while down-weighting less comparable studies, thereby mitigating the impact of between-study heterogeneity. Extensive simulation studies demonstrate that the proposed methods maintain appropriate control of the Type I error rate while achieving substantial gains in statistical power, providing a flexible and robust framework for Bayesian analysis of three-arm non-inferiority trials.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70706"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13531272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148866023","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":"Family-Wise Error Rate Control in Clinical Trials With Overlapping Populations.","authors":"Remi Luschei, Werner Brannath","doi":"10.1002/sim.70730","DOIUrl":"10.1002/sim.70730","url":null,"abstract":"<p><p>We consider clinical trials with multiple, overlapping patient populations that test multiple treatment policies specifically tailored to these populations. Such designs may lead to multiplicity issues, as false statements will affect several populations. For type I error control, often the family-wise error rate (FWER) is controlled, which is the probability to reject at least one true null hypothesis. If the joint distribution of the test statistics is known, the FWER level can be exhausted by determining critical values or adjusted <math><mrow><mi>α</mi></mrow> </math> -levels. The adjustment is typically done under the common ANOVA assumptions. However, the performed tests are then only valid under the rather strong assumption of homogeneous null effects, that is, when the null hypothesis applies to all subpopulations and their intersections. We show that under cancelling null effects, when heterogeneous effects cancel out in some or all subpopulations, this procedure does not provide FWER control. We also suggest different alternatives and compare them in terms of FWER control and their power.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70730"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819441","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":"Finite-Sample Adjustments in Estimating Equations for Correlated Overdispersed Count Outcomes With Application to Cluster Randomized Trials.","authors":"Ying Zhang, John S Preisser","doi":"10.1002/sim.70698","DOIUrl":"10.1002/sim.70698","url":null,"abstract":"<p><p>Generalized estimating equations (GEE) produce population-averaged estimates of treatment effects in marginal mean models for correlated count outcomes, when intra-cluster correlations are considered as nuisance parameters. For joint inference of marginal means and correlations, paired estimating equations have been proposed. With a small number of clusters, matrix-adjusted estimating equations (MAEE) were introduced to correct finite-sample bias in correlation parameter estimates. This article extends the GEE/MAEE method by adding a third estimating equation for the scale parameter (3EE/MAEE) and demonstrates its superior performance for correlated count outcomes. In simulations, 3EE/MAEE reduced bias of ICC estimates and better maintained the nominal coverage of confidence intervals compared to its uncorrected counterpart. The performance of different bias-corrected sandwich variance estimators as well as working negative binomial (NB) and Poisson models is also evaluated. The 3EE/MAEE method is applied to overdispersed correlated count outcomes from a real-world, stepped wedge cluster randomized trial.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70698"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148865806","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":"Context-Stratified Mendelian Randomization: Exploiting Regional Exposure Variation to Explore Causal Effect Heterogeneity and Nonlinearity.","authors":"Stephen Burgess, Benjamin A R Woolf, Amy M Mason","doi":"10.1002/sim.70727","DOIUrl":"10.1002/sim.70727","url":null,"abstract":"<p><p>Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to make causal claims. Standard MR approaches typically report a single population-averaged estimate, limiting their ability to explore effect heterogeneity or nonlinear dose-response relationships. Existing stratification methods, such as residual-based and doubly-ranked stratified MR, attempt to overcome this but rely on strong and unverifiable assumptions. We propose an alternative, context-stratified Mendelian randomization, which exploits exogenous variation in the exposure across subgroups-such as recruitment centers, geographic regions, or time periods-to investigate effect heterogeneity and nonlinearity. Separate MR analyses are performed within each context, and heterogeneity in the resulting estimates is assessed using Cochran's Q statistic and meta-regression. We demonstrate through simulations that the approach detects heterogeneity when present while maintaining nominal false positive rates under homogeneity when appropriate methods are used. In an applied example using UK Biobank data, we assess the effect of vitamin D levels on coronary artery disease risk across 20 recruitment centers. Despite some regional variation in vitamin D distributions, there is no evidence for a causal effect or heterogeneity in estimates. Compared to stratification methods requiring model-based assumptions, the context-stratified approach is simple to implement and unaffected by collider bias, provided the context variable is exogenous. However, the method's power and interpretability depend critically on meaningful exogenous variation in exposure distributions between contexts. In the example of vitamin D, subgroups from other stratification methods explored a much wider range of the exposure distribution.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70727"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148875723","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":"Quantile Tensor Regression for Integrative Genomic Analysis of Oesophageal Carcinoma.","authors":"Shuo Liu, Wolfgang Karl Härdle, Jianxin Pan, Jian Hou, Tan Meng, Maozai Tian","doi":"10.1002/sim.70732","DOIUrl":"10.1002/sim.70732","url":null,"abstract":"<p><p>Recent integrative genomic studies have increasingly exploited the tensor structure of multi-omics data to develop statistical methods that jointly model the relationship between clinical outcomes and multiple genomes. However, genomic measurements and clinical outcomes are frequently contaminated by outliers or heavy-tailed noise, necessitating robust tensor-based inference approaches. In this paper, we investigate the quantile tensor regression with an emphasis on the region selection problem. We introduce a novel estimator that integrates quantile regression for robustness with a nonconvex penalty to encourage sparsity in the tensor coefficient, thereby enabling the identification of localized genomic regions that significantly influence the clinical response. To solve the resulting optimization problem, we devise an effective algorithm tailored to the nonconvex objective and tensor architecture. We establish the asymptotic properties of the proposed nonconvex penalized estimator. Extensive simulations demonstrate the excellent finite-sample performance of the proposed estimator. We further illustrate the practical utility of the proposed estimator through an application to esophageal carcinoma data, providing empirical validation.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70732"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906806","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":"A Bayesian Multi-State Data Integration Approach for Estimating County-Level Prevalence of Opioid Misuse in the United States.","authors":"Zixuan Feng, Qiushi Chen, Paul Griffin, Le Bao","doi":"10.1002/sim.70707","DOIUrl":"10.1002/sim.70707","url":null,"abstract":"<p><p>Drug overdose deaths, including from opioids, remain a significant public health threat to the United States (US). To abate the harms of opioid misuse, understanding its prevalence at the local level is crucial for stakeholders in communities to develop response strategies that effectively use limited resources. Although there exist several state-specific studies that provide county-level prevalence estimates, such estimates are not widely available across the country, as the datasets used in these studies are not always readily available in other states, which, therefore, has limited the wider applications of existing models. To fill this gap, we propose a Bayesian multi-state data integration approach that fully utilizes publicly available data sources to estimate county-level opioid misuse prevalence for all counties in the US. The hierarchical structure jointly models opioid misuse prevalence and overdose death outcomes, leverages existing county-level prevalence estimates in limited states and state-level estimates from national surveys, and accounts for heterogeneity across counties and states with counties' covariates and mixed effects. Furthermore, our parsimonious and generalizable modeling framework employs horseshoe+ prior to flexibly shrink coefficients and prevent overfitting, ensuring adaptability as new county-level prevalence data in additional states become available. Using real-world data, our model shows high estimation accuracy through cross-validation and provides nationwide county-level estimates of opioid misuse for the first time.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70707"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881318","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":"Dynamic Predictions and Predictimands for Salvage Therapy in Recurrent Prostate Cancer Using Joint Models.","authors":"Lukas Owens, Dimitris Rizopoulos, Jonathan Fainberg, Sigrid Carlsson, Nicole Liso, Sean McBride, Vincent Laudone, Ruth Etzioni, Jeremy M G Taylor","doi":"10.1002/sim.70710","DOIUrl":"10.1002/sim.70710","url":null,"abstract":"<p><p>Prostate cancer patients with biochemical recurrence (BCR) face a decision of whether to start salvage therapy (ST), which may reduce the probability of metastatic progression at the cost of side effects. To inform the decision to start ST at or after BCR, models that make counterfactual predictions incorporating treatment are highly desirable. However, estimation of such models using observational data requires care due to time-varying confounding by the longitudinal biomarker prostate-specific antigen (PSA). Moreover, a careful definition of the estimands of interest, referred to as \"predictimands\", is required due to the possibility of delayed initiation of treatment after biochemical recurrence. In this study, we utilize the framework of joint longitudinal and survival models to tackle these issues, estimating a model for pre-ST PSA trajectories and risk of metastasis that incorporates the effect of ST, from a dataset of 2075 patients with BCR. We define relevant predictimands for a new patient after BCR under three scenarios: Immediately treated, never treated, and treatment under a dynamic regime, where ST is started when PSA is observed to exceed a pre-specified threshold. We propose a Monte Carlo scheme for computing these predictimands, adapting previous work on dynamic predictions from joint models to account for treatment timing. This methodology is applied to an example patient and validated in a simulation study. This methodology could be adapted to a wide variety of applications requiring counterfactual predictions in the presence of time-varying treatments and biomarkers. Code to implement such analyses is available in the R package JMbayes2.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70710"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814139","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":"Optimal Dynamic Treatment Regimes for High-Dimensional Accelerated Failure Time Model.","authors":"Shijie Zhao, Wei Zhao","doi":"10.1002/sim.70705","DOIUrl":"https://doi.org/10.1002/sim.70705","url":null,"abstract":"<p><p>The increasing prevalence of high-dimensional covariates presents a significant challenge to precision medicine. To overcome this, we propose a novel multi-stage optimal dynamic treatment regime method specifically designed for high-dimensional accelerated failure time models. Our approach, which aims to maximize individual survival time, employs backward induction with counterfactual survival times as the optimization objective at each stage. Parameter estimation leverages weighted counterfactual survival times to account for censoring, followed by the slope determination by analysis of residuals (SDAR) algorithm. Theoretical analysis guarantees exponential convergence with non-asymptotic error bounds under mild conditions. Simulation studies demonstrate a substantial improvement in survival time estimation, particularly under high censoring rates, compared to existing methods. We illustrate the practical utility of our method by applying it to a sepsis dataset, revealing clinically meaningful treatment recommendations.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70705"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13475189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766458","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}