Statistics in Medicine最新文献

{"title":"Microbial Diversity Estimation and Hill Number Calculation Using the Hierarchical Pitman-Yor Process.","authors":"Kevin McGregor, Todd Parsons, Elinor Simons, James Scott, Anita Kozyrskyj, Christopher Quince","doi":"10.1002/sim.70689","DOIUrl":"10.1002/sim.70689","url":null,"abstract":"<p><strong>Background: </strong>The human microbiome comprises the microorganisms that inhabit the various locales of the human body and plays a vital role in human health. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances into a single value. In a finite microbiome sample, there will be species missing from the target population, which will affect the diversity estimates.</p><p><strong>Methods: </strong>We employ a model based on the hierarchical Pitman-Yor (HPY) process to model the species abundance distributions over multiple populations. The model parameters are estimated using a Gibbs sampler. We also derive estimates of species diversity, conditional and unconditional on the observed data, as a function of the HPY parameters. Finally, we derive a general formula for the Hill numbers in the HPY context.</p><p><strong>Results: </strong>We show that the Gibbs sampler for the HPY model performs well in simulations. We also show that the conditional estimates of diversity from the HPY model improve over naïve estimates when species are missing. Similarly, the conditional HPY estimates tend to perform better than the naïve estimates especially when the number of individuals sampled from a population is small. Finally, we illustrate results of applying the HPY model in an infant gut microbiome dataset.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70689"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670619","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":"On the Statistical Limitations of Landmark Analysis for Addressing Immortal Time Bias.","authors":"Fei Wan","doi":"10.1002/sim.70679","DOIUrl":"10.1002/sim.70679","url":null,"abstract":"<p><p>Landmark analysis establishes a fixed \"landmark\" time, including only individuals who remain event-free up to that point and classifying survivors according to their treatment status at the landmark. By restarting follow-up from this time, the method aims to create comparable groups presumed free from immortal time bias. Despite its simplicity and widespread use, landmark analysis has notable limitations. Under potential outcomes and piecewise proportional hazards, we derived closed-form results for naive, exclusion, and landmark analyses and evaluated them via simulations. We found that landmark estimates fail to recover the target causal effect because the reference group mixes \"never-treated\" and \"late-treated\" subjects. This mixture attenuates the effect size, generally biasing estimates toward the null, with the magnitude of bias depending on the true effect, treatment rate, and event rate, except under the null hypothesis. In addition, landmark analysis suffers a substantial loss of statistical power due to the exclusion of early events and the attenuation of effect estimates. Indeed, the attenuation alone can make landmark analysis perform considerably worse in power than a randomized trial with equal group sizes. Without clinical context, a small treatment effect observed in a landmark analysis may reflect bias rather than a true null effect. Therefore, caution is warranted when using landmark analysis to address immortal time bias, and time-dependent regression models provide a more robust alternative.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70679"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580053","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":"Long Run Non-Transitivity of Win Ratio and How to Rule It Out.","authors":"Olga V Demler, Anna Győrffy-Kerekes","doi":"10.1002/sim.70581","DOIUrl":"https://doi.org/10.1002/sim.70581","url":null,"abstract":"<p><p>The win ratio (WR) has emerged as an increasingly used method for analyzing composite endpoints in randomized controlled trials (RCTs). Its growing popularity stems from its ability to accommodate a hierarchy of event types within a composite primary endpoint in an RCT. However, as a relatively new measure, the WR exhibits some surprising properties. Oakes and other authors have noted that for a given triplet of participants, there are realizations of event times and hierarchy of event types when \"wins\" form a loop. They term this property \"intransitivity.\" In this paper, we report and study a different phenomenon, a violation of long-run transitivity of WRs, for example, when in the long run <math><mrow><mtext>WR</mtext> <mfenced> <mrow><msub><mi>T</mi> <mi>A</mi></msub> <mo>></mo> <msub><mi>T</mi> <mi>B</mi></msub> </mrow> </mfenced> <mo>></mo> <mn>1</mn></mrow> </math> , <math><mrow><mtext>WR</mtext> <mfenced> <mrow><msub><mi>T</mi> <mi>B</mi></msub> <mo>></mo> <msub><mi>T</mi> <mi>C</mi></msub> </mrow> </mfenced> <mo>></mo> <mn>1</mn></mrow> </math> and paradoxically <math><mrow><mtext>WR</mtext> <mfenced> <mrow><msub><mi>T</mi> <mi>C</mi></msub> <mo>></mo> <msub><mi>T</mi> <mi>A</mi></msub> </mrow> </mfenced> <mo>></mo> <mn>1</mn></mrow> </math> , where <math> <mrow><msub><mi>T</mi> <mi>A</mi></msub> <mo>,</mo> <msub><mi>T</mi> <mi>B</mi></msub> <mo>,</mo> <msub><mi>T</mi> <mi>C</mi></msub> </mrow> </math> denote the time-to-event in trial arms A, B, and C (or placebo, standard of care and novel treatment, respectively). We refer to it as Efron-type \"non-transitivity.\" Unlike Oakes-type intransitivity, Efron-type non-transitivity is an inherent property of the underlying random variables. As such, it cannot be resolved by a longer observation period or larger sample size. Lumley and Gillen showed that transitivity can be guaranteed only for univariate statistics (e.g., mean, median, etc.), whereas head-to-head comparisons can be non-transitive. WR and win proportion (WP) are head-to-head comparisons and may be non-transitive, in which case the best treatment cannot be determined. Therefore, the non-transitivity property challenges the utility of all head-to-head comparisons as efficacy measures in RCTs. In this paper, we explore the scope of this problem, focusing on identifying conditions that rule out non-transitivity for WR, WP, and hazard ratio (HR). We apply our findings to establish transitivity in two case studies using published RCTs. This work highlights an important phenomenon that must be recognized when the WR is used as an efficacy measure in randomized controlled trials.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70581"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13487793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797516","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":"Pragmatic Two-Stage Design for Clinical Trials With Treatment Selection.","authors":"Paul Horton, Yajun Mei","doi":"10.1002/sim.70683","DOIUrl":"10.1002/sim.70683","url":null,"abstract":"<p><p>Clinical trials are inherently complex and become more challenging when selecting one treatment from many while also evaluating the efficacy. In this paper, we develop a pragmatic design that balances sample efficiency and practicality through a two-stage sequential approach. The first stage involves adaptive arm selection and early termination, while the second stage optimizes sample allocation. Inspired by the Kiefer-Weiss problem, the proposed method provides an efficient solution to an optimization framework that minimizes a weighted sum of the expected number of patients, probability of selecting a bad arm, and the probability of a global Type II error subject to constraints on the Family-Wise Error Rate and maximum sample size. The framework employs stochastic optimization to find the design parameters for the global minimum. We compare the performance against fixed and multi-arm multi-stage designs over a range of test conditions with varying error costs and treatment-response models. Our method improves the cost and practicality of a clinical trial for medical treatments or pharmaceutical development.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70683"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13408322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148607558","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":"Network Meta-Analysis of Survival Outcomes With Non-Proportional Hazards Using Flexible M-Splines.","authors":"David M Phillippo, Ayman Sadek, Hugo Pedder, Nicky J Welton","doi":"10.1002/sim.70695","DOIUrl":"10.1002/sim.70695","url":null,"abstract":"<p><p>Network meta-analysis (NMA) is widely used in healthcare decision-making, where estimates of the effect of multiple treatments on outcomes are required. For time-to-event outcomes such as survival or disease progression the most common approach is to model log hazard ratios; however, this relies on the proportional hazards assumption. Novel treatments such as immunotherapies are expected to display complex hazard functions that cannot be captured by standard parametric models, which results in non-proportional hazards when comparing treatments from different classes. As a result, alternative models such as fractional polynomials or restricted cubic splines are often used. These allow substantial flexibility on the shape of the baseline hazard, but require time-consuming model selection or are intractable for Bayesian analysis. We propose a flexible NMA model using M-splines on the baseline hazard, with a novel weighted random walk prior distribution that provides shrinkage to avoid overfitting and is invariant to the choice of knots and timescale. Non-proportional hazards are modeled either by stratifying by treatment or by introducing treatment effects on the spline coefficients, and covariates may be included on the log hazard rate and spline coefficients. Treatment and covariate effects on the spline coefficients are given random walk prior distributions to smoothly model departures from proportionality over time. The methods are implemented in the user-friendly R package multinma, which supports analyzes with aggregate data, individual participant data, or mixtures of both. We apply the methods to a NMA of progression-free survival with treatments for non-small cell lung cancer.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70695"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707598","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":"Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models.","authors":"Rahul Ghosal, Marcos Matabuena, Enakshi Saha","doi":"10.1002/sim.70694","DOIUrl":"10.1002/sim.70694","url":null,"abstract":"<p><p>We develop a new method for simultaneously selecting fixed and random effects in a multilevel functional regression model. The proposed method is motivated by accelerometer-derived physical activity data from the 2011 to 2012 cohort of the National Health and Nutrition Examination Survey (NHANES), with the aim of identifying age and race-specific heterogeneity in covariate effects on the diurnal pattern of physical activity across the lifespan. Existing methods for variable selection in function-on-scalar regression have primarily been designed for fixed effect selection and for single-level functional data. In high-dimensional multilevel functional regression, the presence of cluster-specific heterogeneity in covariate effects could be detected through sparsity in fixed and random effects, and for this purpose, we propose a multilevel functional mixed effects selection (MuFuMES) method. The fixed and random functional effects are modelled using splines, with spike-and-slab group lasso (SSGL) priors on the unknown parameters of interest, and a computationally efficient MAP estimation approach is employed for mixed effect selection through an Expectation Conditional Maximization (ECM) algorithm. Numerical analysis using simulation study illustrates the satisfactory selection accuracy of the variable selection method in having a negligible false-positive and false-negative rate. The proposed method is applied to the NHANES 2011-12 accelerometer data, where it effectively identifies age and race-specific heterogeneity in covariate effects on the diurnal pattern of physical activity, recovering biologically meaningful insights.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70694"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702116","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":"Bayesian Estimation of the Binomial Parameter in Adaptive Designs With Treatment Selection.","authors":"Pierre Bunouf, Jean-Marie Boher","doi":"10.1002/sim.70680","DOIUrl":"10.1002/sim.70680","url":null,"abstract":"<p><p>It is well-known that results from adaptive multi-arm experiments with treatment selection are subject to the bias of the selection process. In this article, we consider designs with a binary outcome that begin with randomized parallel arms. After each of the preplanned interim looks at data, researchers proceed to the next stage with treatments selected according to predetermined rules. The experiment ends with the best arm, and the binomial parameter is estimated from all the observations accrued throughout the experiment in this arm. We present a comprehensive and unified Bayesian approach to the point and interval estimations, which uses a class of design-dependent priors obtained from the reference prior theory (Bernardo 1979). The approach is applicable regardless of the treatment selection rule, and is not affected by the inclusion of a control arm if the treatment selection is based on direct comparisons of the response rates relative to the control arm. The frequentist characteristics of the posterior estimators are studied and compared with alternative methods, where available. To this end, we introduce specific tools that facilitate the evaluation of estimation methods in binary data analysis. We also study the influence of the treatment selection rule and the effect of the prior correction on estimates through various examples. The approach is easy to implement in the experimental practice, and can be used in the interim analyses to help researchers in the treatment selection process.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70680"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13449648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148685795","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":"Multiple Summary Knowledge-Based Disease Prevalence Estimation for Group Testing Data.","authors":"Wanshuo Yang, Juan Ding, Wenjun Xiong, Shuying Wang, Qizhai Li","doi":"10.1002/sim.70681","DOIUrl":"10.1002/sim.70681","url":null,"abstract":"<p><p>Group testing is widely recognized for its ability to reduce time and cost in screening for rare diseases. However, with limited samples, the parameter estimation from group-testing data might be inefficient. In some scenarios, the external summary statistics are available. To make full use of them, we propose a multiple-source knowledge transfer method. The resulting prevalence estimator is consistent and more efficient than the conventional estimator based on the target group-testing data alone. We establish the asymptotic normality of the proposed estimator. In addition, we develop a data-driven screening procedure to identify the transferable sources. We extend this framework to Dorfman's two-stage group testing design and show that integrating the retesting data yields efficiency gains. The asymptotic normality of the corresponding estimator is also established. Extensive simulations and a real-data application support the theoretical results and demonstrate the favorable finite-sample performance of the proposed method.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70681"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13403104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148593301","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 Cautionary Note for Plasmode Simulation Studies in the Setting of Causal Inference.","authors":"Pamela A Shaw, Susan Gruber, Brian D Williamson, Rishi Desai, Susan M Shortreed, Chloe Krakauer, Jennifer C Nelson, Mark J van der Laan","doi":"10.1002/sim.70676","DOIUrl":"10.1002/sim.70676","url":null,"abstract":"<p><p>Plasmode simulation has become an important tool for evaluating operating characteristics of different statistical methods in complex settings, such as pharmacoepidemiological studies of treatment effectiveness using electronic health records (EHR) data. These studies provide insight into how estimator performance is impacted by challenges including rare events, small sample size and so forth that can indicate which among a set of methods performs best in a real-world dataset. Plasmode simulation combines data resampled from a real-world dataset with data from a probabilistic model to generate a known truth for an estimand in realistic data. There are different potential plasmode strategies currently in use. We compare two popular plasmode simulation frameworks for the evaluation of methods estimating a point treatment estimand. We provide numerical evidence and a theoretical result that shows one of these frameworks can cause certain estimators to incorrectly appear overly biased. Detailed simulation studies using both model-generated and real-world EHR data demonstrate these pitfalls remain at large sample sizes and when analyzing data from a randomized controlled trial. We conclude with guidance for the choice of a plasmode framework that maintains good theoretical properties to allow a fair evaluation of statistical methods while also maintaining the desired similarity to real data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70676"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13392349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562965","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":"Cloud-Plots for Spherical Data With Applications to Glaucoma and Gait Cyclograms.","authors":"S Rao Jammalamadaka, Ali H Abuzaid","doi":"10.1002/sim.70704","DOIUrl":"10.1002/sim.70704","url":null,"abstract":"<p><p>In several biomedical contexts, the need for diagnostic analyses of data that is a set of directions in 2, 3, or higher dimensions arises, as for instance, when considering the relative movement of body parts like the limbs, or looking at anatomical orientations. Commonly used statistical tools, including graphical tools developed for data on the real line, or for dealing with multidimensional data taking values on Euclidean spaces, are quite inappropriate for analyzing such data, due to major differences in their underlying geometry. Taking clues from the Box-plot, which was introduced by Tukey [1977] for linear data, here we propose what we label a \"Cloud-plot\", which is a visualization tool and diagnostic framework for plotting data on 3-dimensional directions, which take values on the surface of a unit sphere, the <math> <mrow> <msup><mrow><mi>𝕊</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> . The analysis is based on \"geodesic distances\" between any 2 observations, which are the shortest paths between these two points on the surface of the sphere. Using these distances, we construct robust analogues of the spherical median and the quartiles. This is followed by a definition of the \"Spherical Median Absolute Deviation\" ( <math><mrow><mi>S</mi> <mi>M</mi> <mi>A</mi> <mi>D</mi></mrow> </math> ). We use these quantities to define outliers, and the performance of the proposed method is evaluated through a comprehensive simulation study that compares true and false positive rates given by several existing discordancy tests. Our extensive results indicate that the approach based on our Cloud-plot provides a very successful and competitive performance for detecting outliers, and remains stable across a range of sample sizes and contamination settings. The cloud-plot is illustrated using two interesting medical applications viz. (i) the identification of structural biomarkers of glaucoma, and (ii) the analysis of functional joint instability in gait cyclograms. These examples demonstrate the potential of the Cloud-plots to detect directional anomalies that may not be captured otherwise by tools commonly used on Euclidean spaces.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70704"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13477852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759731","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}