arXiv - STAT - Methodology最新文献_第9页

A response-adaptive multi-arm design for continuous endpoints based on a weighted information measure 基于加权信息测量的连续终点反应自适应多臂设计

arXiv - STAT - Methodology Pub Date : 2024-09-08 DOI: arxiv-2409.04970

Gianmarco Caruso, Pavel Mozgunov

{"title":"A response-adaptive multi-arm design for continuous endpoints based on a weighted information measure","authors":"Gianmarco Caruso, Pavel Mozgunov","doi":"arxiv-2409.04970","DOIUrl":"https://doi.org/arxiv-2409.04970","url":null,"abstract":"Multi-arm trials are gaining interest in practice given the statistical and\u0000logistical advantages that they can offer. The standard approach is to use a\u0000fixed (throughout the trial) allocation ratio, but there is a call for making\u0000it adaptive and skewing the allocation of patients towards better performing\u0000arms. However, among other challenges, it is well-known that these approaches\u0000might suffer from lower statistical power. We present a response-adaptive\u0000design for continuous endpoints which explicitly allows to control the\u0000trade-off between the number of patients allocated to the 'optimal' arm and the\u0000statistical power. Such a balance is achieved through the calibration of a\u0000tuning parameter, and we explore various strategies to effectively select it.\u0000The proposed criterion is based on a context-dependent information measure\u0000which gives a greater weight to those treatment arms which have characteristics\u0000close to a pre-specified clinical target. We also introduce a simulation-based\u0000hypothesis testing procedure which focuses on selecting the target arm,\u0000discussing strategies to effectively control the type-I error rate. The\u0000potential advantage of the proposed criterion over currently used alternatives\u0000is evaluated in simulations, and its practical implementation is illustrated in\u0000the context of early Phase IIa proof-of-concept oncology clinical trials.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Really Doing Great at Model Evaluation for CATE Estimation? A Critical Consideration of Current Model Evaluation Practices in Treatment Effect Estimation CATE 估算的模型评估真的做得很好吗？对当前治疗效果估算模型评估实践的批判性思考

arXiv - STAT - Methodology Pub Date : 2024-09-08 DOI: arxiv-2409.05161

Hugo Gobato Souto, Francisco Louzada Neto

{"title":"Really Doing Great at Model Evaluation for CATE Estimation? A Critical Consideration of Current Model Evaluation Practices in Treatment Effect Estimation","authors":"Hugo Gobato Souto, Francisco Louzada Neto","doi":"arxiv-2409.05161","DOIUrl":"https://doi.org/arxiv-2409.05161","url":null,"abstract":"This paper critically examines current methodologies for evaluating models in\u0000Conditional and Average Treatment Effect (CATE/ATE) estimation, identifying\u0000several key pitfalls in existing practices. The current approach of\u0000over-reliance on specific metrics and empirical means and lack of statistical\u0000tests necessitates a more rigorous evaluation approach. We propose an automated\u0000algorithm for selecting appropriate statistical tests, addressing the\u0000trade-offs and assumptions inherent in these tests. Additionally, we emphasize\u0000the importance of reporting empirical standard deviations alongside performance\u0000metrics and advocate for using Squared Error for Coverage (SEC) and Absolute\u0000Error for Coverage (AEC) metrics and empirical histograms of the coverage\u0000results as supplementary metrics. These enhancements provide a more\u0000comprehensive understanding of model performance in heterogeneous\u0000data-generating processes (DGPs). The practical implications are demonstrated\u0000through two examples, showcasing the benefits of these methodological\u0000improvements, which can significantly improve the robustness and accuracy of\u0000future research in statistical models for CATE and ATE estimation.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"130 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting Age Distribution of Deaths: Cumulative Distribution Function Transformation 预测死亡年龄分布：累积分布函数变换

arXiv - STAT - Methodology Pub Date : 2024-09-08 DOI: arxiv-2409.04981

Han Lin Shang, Steven Haberman

引用次数: 0

Projective Techniques in Consumer Research: A Mixed Methods-Focused Review and Empirical Reanalysis 消费者研究中的投射技术：以混合方法为重点的回顾与实证再分析

arXiv - STAT - Methodology Pub Date : 2024-09-08 DOI: arxiv-2409.04995

Stephen L. France

引用次数: 0

Marginal Structural Modeling of Representative Treatment Trajectories 代表性治疗轨迹的边际结构模型

arXiv - STAT - Methodology Pub Date : 2024-09-07 DOI: arxiv-2409.04933

Jiewen Liu, Todd A. Miano, Stephen Griffiths, Michael G. S. Shashaty, Wei Yang

{"title":"Marginal Structural Modeling of Representative Treatment Trajectories","authors":"Jiewen Liu, Todd A. Miano, Stephen Griffiths, Michael G. S. Shashaty, Wei Yang","doi":"arxiv-2409.04933","DOIUrl":"https://doi.org/arxiv-2409.04933","url":null,"abstract":"Marginal structural models (MSMs) are widely used in observational studies to\u0000estimate the causal effect of time-varying treatments. Despite its popularity,\u0000limited attention has been paid to summarizing the treatment history in the\u0000outcome model, which proves particularly challenging when individuals'\u0000treatment trajectories exhibit complex patterns over time. Commonly used\u0000metrics such as the average treatment level fail to adequately capture the\u0000treatment history, hindering causal interpretation. For scenarios where\u0000treatment histories exhibit distinct temporal patterns, we develop a new\u0000approach to parameterize the outcome model. We apply latent growth curve\u0000analysis to identify representative treatment trajectories from the observed\u0000data and use the posterior probability of latent class membership to summarize\u0000the different treatment trajectories. We demonstrate its use in parameterizing\u0000the MSMs, which facilitates the interpretations of the results. We apply the\u0000method to analyze data from an existing cohort of lung transplant recipients to\u0000estimate the effect of Tacrolimus concentrations on the risk of incident\u0000chronic kidney disease.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

forester: A Tree-Based AutoML Tool in R forester：基于树的 R 语言 AutoML 工具

arXiv - STAT - Methodology Pub Date : 2024-09-07 DOI: arxiv-2409.04789

Hubert Ruczyński, Anna Kozak

引用次数: 0

Spatial Interference Detection in Treatment Effect Model 治疗效果模型中的空间干扰检测

arXiv - STAT - Methodology Pub Date : 2024-09-07 DOI: arxiv-2409.04836

Wei Zhang, Fang Yao, Ying Yang

引用次数: 0

Establishing the Parallels and Differences Between Right-Censored and Missing Covariates 确定右删失变量和缺失变量之间的相似性和差异性

arXiv - STAT - Methodology Pub Date : 2024-09-07 DOI: arxiv-2409.04684

Jesus E. Vazquez, Marissa C. Ashner, Yanyuan Ma, Karen Marder, Tanya P. Garcia

{"title":"Establishing the Parallels and Differences Between Right-Censored and Missing Covariates","authors":"Jesus E. Vazquez, Marissa C. Ashner, Yanyuan Ma, Karen Marder, Tanya P. Garcia","doi":"arxiv-2409.04684","DOIUrl":"https://doi.org/arxiv-2409.04684","url":null,"abstract":"While right-censored time-to-event outcomes have been studied for decades,\u0000handling time-to-event covariates, also known as right-censored covariates, is\u0000now of growing interest. So far, the literature has treated right-censored\u0000covariates as distinct from missing covariates, overlooking the potential\u0000applicability of estimators to both scenarios. We bridge this gap by\u0000establishing connections between right-censored and missing covariates under\u0000various assumptions about censoring and missingness, allowing us to identify\u0000parallels and differences to determine when estimators can be used in both\u0000contexts. These connections reveal adaptations to five estimators for\u0000right-censored covariates in the unexplored area of informative covariate\u0000right-censoring and to formulate a new estimator for this setting, where the\u0000event time depends on the censoring time. We establish the asymptotic\u0000properties of the six estimators, evaluate their robustness under incorrect\u0000distributional assumptions, and establish their comparative efficiency. We\u0000conducted a simulation study to confirm our theoretical results, and then\u0000applied all estimators to a Huntington disease observational study to analyze\u0000cognitive impairments as a function of time to clinical diagnosis.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Unified Framework for Cluster Methods with Tensor Networks 张量网络集群方法的统一框架

arXiv - STAT - Methodology Pub Date : 2024-09-07 DOI: arxiv-2409.04729

Erdong Guo, David Draper

引用次数: 0

Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm 自然实验估算器基准：新数据集和双重稳健算法

arXiv - STAT - Methodology Pub Date : 2024-09-06 DOI: arxiv-2409.04500

R. Teal Witter, Christopher Musco

{"title":"Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm","authors":"R. Teal Witter, Christopher Musco","doi":"arxiv-2409.04500","DOIUrl":"https://doi.org/arxiv-2409.04500","url":null,"abstract":"Estimating the effect of treatments from natural experiments, where\u0000treatments are pre-assigned, is an important and well-studied problem. We\u0000introduce a novel natural experiment dataset obtained from an early childhood\u0000literacy nonprofit. Surprisingly, applying over 20 established estimators to\u0000the dataset produces inconsistent results in evaluating the nonprofit's\u0000efficacy. To address this, we create a benchmark to evaluate estimator accuracy\u0000using synthetic outcomes, whose design was guided by domain experts. The\u0000benchmark extensively explores performance as real world conditions like sample\u0000size, treatment correlation, and propensity score accuracy vary. Based on our\u0000benchmark, we observe that the class of doubly robust treatment effect\u0000estimators, which are based on simple and intuitive regression adjustment,\u0000generally outperform other more complicated estimators by orders of magnitude.\u0000To better support our theoretical understanding of doubly robust estimators, we\u0000derive a closed form expression for the variance of any such estimator that\u0000uses dataset splitting to obtain an unbiased estimate. This expression\u0000motivates the design of a new doubly robust estimator that uses a novel loss\u0000function when fitting functions for regression adjustment. We release the\u0000dataset and benchmark in a Python package; the package is built in a modular\u0000way to facilitate new datasets and estimators.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0