Statistical Methods in Medical Research最新文献_第8页

Optimal allocation strategies in platform trials with continuous endpoints. 连续终点平台试验的最佳分配策略。

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-05-01 Epub Date: 2024-03-20 DOI: 10.1177/09622802241239008

Marta Bofill Roig, Ekkehard Glimm, Tobias Mielke, Martin Posch

{"title":"Optimal allocation strategies in platform trials with continuous endpoints.","authors":"Marta Bofill Roig, Ekkehard Glimm, Tobias Mielke, Martin Posch","doi":"10.1177/09622802241239008","DOIUrl":"10.1177/09622802241239008","url":null,"abstract":"<p><p>Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the optimal allocation depends on the number of arms in the platform and the latter typically varies over time. In addition, the optimal allocation depends on the analysis strategy used and the optimality criteria considered. In this article, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and a testing procedure based on a regression model are used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods using concurrent and non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of <math><mi>k</mi></math> allocation rule used in classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of <math><mi>k</mi></math> allocations by means of a case study.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"858-874"},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176567","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

Bayesian analysis of joint quantile regression for multi-response longitudinal data with application to primary biliary cirrhosis sequential cohort study 应用于原发性胆汁性肝硬化序列队列研究的多反应纵向数据联合量子回归贝叶斯分析

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-27 DOI: 10.1177/09622802241247725

Yu-Zhu Tian, Man-Lai Tang, Catherine Wong, Mao-Zai Tian

引用次数: 0

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects 考虑预后效应的因果规则集合法估算异质性治疗效果

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-27 DOI: 10.1177/09622802241247728

Mayu Hiraishi, Ke Wan, Kensuke Tanioka, Hiroshi Yadohisa, Toshio Shimokawa

引用次数: 0

The “Why” behind including “Y” in your imputation model 将 "Y "纳入估算模型背后的 "原因"

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-16 DOI: 10.1177/09622802241244608

Lucy D’Agostino McGowan, Sarah C Lotspeich, Staci A Hepler

{"title":"The “Why” behind including “Y” in your imputation model","authors":"Lucy D’Agostino McGowan, Sarah C Lotspeich, Staci A Hepler","doi":"10.1177/09622802241244608","DOIUrl":"https://doi.org/10.1177/09622802241244608","url":null,"abstract":"Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"219 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616954","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

A simple and robust parametric shared frailty model for recurrent events with the competing risk of death: An application to the Carvedilol Prospective Randomized Cumulative Survival trial 针对具有死亡竞争风险的复发事件的简单稳健的参数共享虚弱模型：卡维地洛前瞻性随机累积生存试验的应用

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-16 DOI: 10.1177/09622802241236934

Jiren Sun, Thomas Cook

引用次数: 0

Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation 估计有家庭干扰的序数结果的动态治疗制度：在家庭戒烟中的应用

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-16 DOI: 10.1177/09622802241242313

Cong Jiang, Mary Thompson, Michael Wallace

{"title":"Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation","authors":"Cong Jiang, Mary Thompson, Michael Wallace","doi":"10.1177/09622802241242313","DOIUrl":"https://doi.org/10.1177/09622802241242313","url":null,"abstract":"The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes, which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient’s baseline characteristics, the information on treatments and responses accrued by that point, and the patient’s current health status, including symptom severity and other measures. However, dynamic treatment regime estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference – where one patient’s treatment may affect another’s outcome. In this paper, we introduce the weighted proportional odds model: a regression based, approximate doubly-robust approach to single-stage dynamic treatment regime estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of weighted proportional odds model with our adjusted weights via simulation studies. We further extend weighted proportional odds model to multi-stage dynamic treatment regime estimation with household interference, namely dynamic weighted proportional odds model. Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"4 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617021","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

Non-stationary Bayesian spatial model for disease mapping based on sub-regions 基于次区域的疾病绘图非稳态贝叶斯空间模型

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-10 DOI: 10.1177/09622802241244613

Esmail Abdul-Fattah, Elias Krainski, Janet Van Niekerk, Håvard Rue

{"title":"Non-stationary Bayesian spatial model for disease mapping based on sub-regions","authors":"Esmail Abdul-Fattah, Elias Krainski, Janet Van Niekerk, Håvard Rue","doi":"10.1177/09622802241244613","DOIUrl":"https://doi.org/10.1177/09622802241244613","url":null,"abstract":"This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model’s ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior to the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary. Additionally, we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"31 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140581004","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

Variable selection for latent class analysis in the presence of missing data with application to record linkage 缺失数据情况下的潜类分析变量选择与记录关联的应用

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-09 DOI: 10.1177/09622802241242317

Huiping Xu, Xiaochun Li, Zuoyi Zhang, Shaun Grannis

{"title":"Variable selection for latent class analysis in the presence of missing data with application to record linkage","authors":"Huiping Xu, Xiaochun Li, Zuoyi Zhang, Shaun Grannis","doi":"10.1177/09622802241242317","DOIUrl":"https://doi.org/10.1177/09622802241242317","url":null,"abstract":"The Fellegi-Sunter model is a latent class model widely used in probabilistic linkage to identify records that belong to the same entity. Record linkage practitioners typically employ all available matching fields in the model with the premise that more fields convey greater information about the true match status and hence result in improved match performance. In the context of model-based clustering, it is well known that such a premise is incorrect and the inclusion of noisy variables could compromise the clustering. Variable selection procedures have therefore been developed to remove noisy variables. Although these procedures have the potential to improve record matching, they cannot be applied directly due to the ubiquity of the missing data in record linkage applications. In this paper, we modify the stepwise variable selection procedure proposed by Fop, Smart, and Murphy and extend it to account for missing data common in record linkage. Through simulation studies, our proposed method is shown to select the correct set of matching fields across various settings, leading to better-performing algorithms. The improved match performance is also seen in a real-world application. We therefore recommend the use of our proposed selection procedure to identify informative matching fields for probabilistic record linkage algorithms.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"62 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580889","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

Methods for non-proportional hazards in clinical trials: A systematic review 临床试验中的非比例危害方法：系统回顾

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-09 DOI: 10.1177/09622802241242325

Maximilian Bardo, Cynthia Huber, Norbert Benda, Jonas Brugger, Tobias Fellinger, Vaidotas Galaune, Judith Heinz, Harald Heinzl, Andrew C Hooker, Florian Klinglmüller, Franz König, Tim Mathes, Martina Mittlböck, Martin Posch, Robin Ristl, Tim Friede

{"title":"Methods for non-proportional hazards in clinical trials: A systematic review","authors":"Maximilian Bardo, Cynthia Huber, Norbert Benda, Jonas Brugger, Tobias Fellinger, Vaidotas Galaune, Judith Heinz, Harald Heinzl, Andrew C Hooker, Florian Klinglmüller, Franz König, Tim Mathes, Martina Mittlböck, Martin Posch, Robin Ristl, Tim Friede","doi":"10.1177/09622802241242325","DOIUrl":"https://doi.org/10.1177/09622802241242325","url":null,"abstract":"For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"46 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580873","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

An additive-multiplicative model for longitudinal data with informative observation times 具有信息观测时间的纵向数据加乘模型

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-04-08 DOI: 10.1177/09622802241236951

Yang Li, Wanzhu Tu

{"title":"An additive-multiplicative model for longitudinal data with informative observation times","authors":"Yang Li, Wanzhu Tu","doi":"10.1177/09622802241236951","DOIUrl":"https://doi.org/10.1177/09622802241236951","url":null,"abstract":"Designed clinical studies often assess outcomes at pre-planned time points. In most situations, standard statistical models, such as generalized linear mixed models and generalized additive models, are sufficient to depict the temporal trends of the outcome and produce valid inference. Complicating factors, however, do exist in practical data analyses. One complication arises when the outcome and observational processes are interdependent, that is, the observational process is informative; another challenge is patient characteristics may influence the longitudinally observed outcomes in non-additive ways, for example, by multiplicative factors. In this research, we extend the standard longitudinal models to accommodate informative observation through a more flexible modeling structure—one with additive-multiplicative components that do not require explicit specification of the dependency structure between the outcome and observation processes. Along this vein, we provide the essential theory for inference in such models. Simulation studies showed the proposed method performs well for finite-sample scenarios, and the method was applied to analyze a motivating example from an alcohol-associated hepatitis observational study.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"51 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580917","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