Statistical Methods in Medical Research最新文献_第2页

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-12 DOI: 10.1177/09622802241309750

Alexandra Blenkinsop, Lysandros Sofocleous, Francesco Di Lauro, Evangelia Georgia Kostaki, Ard van Sighem, Daniela Bezemer, Thijs van de Laar, Peter Reiss, Godelieve de Bree, Nikos Pantazis, Oliver Ratmann

{"title":"Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates.","authors":"Alexandra Blenkinsop, Lysandros Sofocleous, Francesco Di Lauro, Evangelia Georgia Kostaki, Ard van Sighem, Daniela Bezemer, Thijs van de Laar, Peter Reiss, Godelieve de Bree, Nikos Pantazis, Oliver Ratmann","doi":"10.1177/09622802241309750","DOIUrl":"https://doi.org/10.1177/09622802241309750","url":null,"abstract":"In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since the divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as a signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the proposed approach to estimate age-specific sources of HIV infection in Amsterdam tranamission networks among men who have sex with men between 2010 and 2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241309750"},"PeriodicalIF":1.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Bayesian bivariate spatial modeling of small area proportions with application to health survey data. 应用于健康调查数据的小面积比例的层次贝叶斯双变量空间建模。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-11 DOI: 10.1177/09622802251316968

Hanjun Yu, Xinyi Xu, Lichao Yu

引用次数: 0

Statistical considerations for evaluating treatment effect under various non-proportional hazard scenarios.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-11 DOI: 10.1177/09622802241313297

Xinyu Zhang, Erich J Greene, Ondrej Blaha, Wei Wei

{"title":"Statistical considerations for evaluating treatment effect under various non-proportional hazard scenarios.","authors":"Xinyu Zhang, Erich J Greene, Ondrej Blaha, Wei Wei","doi":"10.1177/09622802241313297","DOIUrl":"https://doi.org/10.1177/09622802241313297","url":null,"abstract":"We conducted a systematic comparison of statistical methods used for the analysis of time-to-event outcomes under various proportional and non-proportional hazard (NPH) scenarios. Our study used data from recently published oncology trials to compare the Log-rank test, still by far the most widely used option, against some available alternatives, including the MaxCombo test, the Restricted Mean Survival Time difference test, the Generalized Gamma model and the Generalized F model. Power, type I error rate, and time-dependent bias with respect to the survival probability and median survival time were used to evaluate and compare the performance of these methods. In addition to the real data, we simulated three hypothetical scenarios with crossing hazards chosen so that the early and late effects \"cancel out\" and used them to evaluate the ability of the aforementioned methods to detect time-specific and overall treatment effects. We implemented novel metrics for assessing the time-dependent bias in treatment effect estimates to provide a more comprehensive evaluation in NPH scenarios. Recommendations under each NPH scenario are provided by examining the type I error rate, power, and time-dependent bias associated with each statistical approach.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241313297"},"PeriodicalIF":1.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal survival embeddings: Non-parametric counterfactual inference under right-censoring.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-11 DOI: 10.1177/09622802241311455

Carlos García Meixide, Marcos Matabuena

引用次数: 0

Modeling the ratio of correlated biomarkers using copula regression.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-11 DOI: 10.1177/09622802241313293

Moritz Berger, Nadja Klein, Michael Wagner, Matthias Schmid

引用次数: 0

Distribution-free control charts for mixed-type data based on rank of interpoint distances.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-10 DOI: 10.1177/09622802251316964

Guojun Liu, Jyun-You Chiang, Yajie Bai, Zhengcheng Mou

{"title":"Distribution-free control charts for mixed-type data based on rank of interpoint distances.","authors":"Guojun Liu, Jyun-You Chiang, Yajie Bai, Zhengcheng Mou","doi":"10.1177/09622802251316964","DOIUrl":"https://doi.org/10.1177/09622802251316964","url":null,"abstract":"Multivariate control charts have found wide application in healthcare, yet they primarily cater to continuous or categorical variables. However, the emergence of mixed-type data has sparked interest in adapting traditional control charts to handle such complexity. Unfortunately, existing methods often struggle to effectively manage this complexity, particularly in scenarios with limited historical in-control data. In response, this article introduces three distribution-free control charts specifically designed for monitoring mixed-type processes. The proposed approach revolves around computing distances between observations and a specified point, thereby reducing the data to a single dimension. Subsequently, the ranks of these one-dimensional distances are leveraged to develop monitoring statistics. Furthermore, to facilitate dimensionality reduction, a novel distance measure tailored for mixed-type data is introduced. Extensive validation of our proposed method is conducted through comprehensive simulation experiments. Moreover, we demonstrate the practical applicability of the proposed method using an example related to heart disease.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251316964"},"PeriodicalIF":1.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling extensions for multi-location studies in environmental epidemiology.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-05 DOI: 10.1177/09622802241313284

Pierre Masselot, Antonio Gasparrini

引用次数: 0

Estimating target population treatment effects in meta-analysis with individual participant-level data. 在个体参与者水平数据的meta分析中估计目标人群的治疗效果。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241307642

Hwanhee Hong, Lu Liu, Elizabeth A Stuart

{"title":"Estimating target population treatment effects in meta-analysis with individual participant-level data.","authors":"Hwanhee Hong, Lu Liu, Elizabeth A Stuart","doi":"10.1177/09622802241307642","DOIUrl":"10.1177/09622802241307642","url":null,"abstract":"Meta-analysis of randomized controlled trials is commonly used to evaluate treatments and inform policy decisions because it provides comprehensive summaries of all available evidence. However, meta-analyses are limited to draw population inference of treatment effects because they usually do not define target populations of interest specifically, and results of the individual randomized controlled trials in those meta-analyses may not generalize to the target populations. To leverage evidence from multiple randomized controlled trials in the generalizability context, we bridge the ideas from meta-analysis and causal inference. We integrate meta-analysis with causal inference approaches estimating target population average treatment effect. We evaluate the performance of the methods via simulation studies and apply the methods to generalize meta-analysis results from randomized controlled trials of treatments on schizophrenia to adults with schizophrenia who present to usual care settings in the United States. Our simulation results show that all methods perform comparably and well across different settings. The data analysis results show that the treatment effect in the target population is meaningful, although the effect size is smaller than the sample average treatment effect. We recommend applying multiple methods and comparing the results to ensure robustness, rather than relying on a single method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"355-368"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized Bayesian kernel machine regression. 广义贝叶斯核机器回归

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-12-12 DOI: 10.1177/09622802241280784

Xichen Mou, Hongmei Zhang, S Hasan Arshad

引用次数: 0

Graphical methods to illustrate the nature of the relation between a continuous variable and the outcome when using restricted cubic splines with a Cox proportional hazards model. 在使用限制性三次样条和 Cox 比例危险模型时，用图形方法说明连续变量和结果之间关系的性质。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-10-21 DOI: 10.1177/09622802241287707

Peter C Austin

{"title":"Graphical methods to illustrate the nature of the relation between a continuous variable and the outcome when using restricted cubic splines with a Cox proportional hazards model.","authors":"Peter C Austin","doi":"10.1177/09622802241287707","DOIUrl":"10.1177/09622802241287707","url":null,"abstract":"Restricted cubic splines (RCS) allow analysts to model nonlinear relations between continuous covariates and the outcome in a regression model. When using RCS with the Cox proportional hazards model, there is no longer a single hazard ratio for the continuous variable. Instead, the hazard ratio depends on the values of the covariate for the two individuals being compared. Thus, using age as an example, when one assumes a linear relation between age and the log-hazard of the outcome there is a single hazard ratio comparing any two individuals whose age differs by 1 year. However, when allowing for a nonlinear relation between age and the log-hazard of the outcome, the hazard ratio comparing the hazard of the outcome between a 31- and a 30-year-old may differ from the hazard ratio comparing the hazard of the outcome between an 81- and an 80-year-old. We describe four methods to describe graphically the relation between a continuous variable and the outcome when using RCS with a Cox model. These graphical methods are based on plots of relative hazard ratios, cumulative incidence, hazards, and cumulative hazards against the continuous variable. Using a case study of patients presenting to hospital with heart failure and a series of mathematical derivations, we illustrate that the four methods will produce qualitatively similar conclusions about the nature of the relation between a continuous variable and the outcome. Use of these methods will allow for an intuitive communication of the nature of the relation between the variable and the outcome.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"277-285"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0