Biometrics最新文献_第9页

Causal inference for time-to-event data with a cured subpopulation. 具有固化子群的时间到事件数据的因果推理。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae028

Yi Wang, Yuhao Deng, Xiao-Hua Zhou

{"title":"Causal inference for time-to-event data with a cured subpopulation.","authors":"Yi Wang, Yuhao Deng, Xiao-Hua Zhou","doi":"10.1093/biomtc/ujae028","DOIUrl":"https://doi.org/10.1093/biomtc/ujae028","url":null,"abstract":"When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Doubly robust proximal synthetic controls. 双稳健近端合成控制

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae055

Hongxiang Qiu, Xu Shi, Wang Miao, Edgar Dobriban, Eric Tchetgen Tchetgen

{"title":"Doubly robust proximal synthetic controls.","authors":"Hongxiang Qiu, Xu Shi, Wang Miao, Edgar Dobriban, Eric Tchetgen Tchetgen","doi":"10.1093/biomtc/ujae055","DOIUrl":"10.1093/biomtc/ujae055","url":null,"abstract":"To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141178765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis. 高维多受试者时间序列转换矩阵推理在大脑连接性分析中的应用。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae021

Xiang Lyu, Jian Kang, Lexin Li

{"title":"High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.","authors":"Xiang Lyu, Jian Kang, Lexin Li","doi":"10.1093/biomtc/ujae021","DOIUrl":"10.1093/biomtc/ujae021","url":null,"abstract":"Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded simultaneous test. We establish the uniform consistency for the estimators of our modified EM, and show that the subsequent test achieves both a consistent false discovery control, and its power approaches one asymptotically. We demonstrate the efficacy of our method through both simulations and a brain connectivity study of task-evoked functional magnetic resonance imaging.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140850327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas. Thanthirige Lakshika M. Ruberu、Danielle Braun、Giovanni Parmigiani 和 Swati Biswas 关于 "癌症风险渗透的贝叶斯元分析 "的讨论。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae041

Gianluca Baio

引用次数: 0

Regression models for average hazard. 平均危害回归模型

IF 1.4 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae037

Hajime Uno, Lu Tian, Miki Horiguchi, Satoshi Hattori, Kenneth L Kehl

{"title":"Regression models for average hazard.","authors":"Hajime Uno, Lu Tian, Miki Horiguchi, Satoshi Hattori, Kenneth L Kehl","doi":"10.1093/biomtc/ujae037","DOIUrl":"10.1093/biomtc/ujae037","url":null,"abstract":"Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11107592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas. Thanthirige Lakshika M. Ruberu、Danielle Braun、Giovanni Parmigiani 和 Swati Biswas 关于 "癌症风险渗透的贝叶斯元分析 "的讨论。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae044

Paul Gustafson

引用次数: 0

Behavioral carry-over effect and power consideration in crossover trials. 交叉试验中的行为延续效应和功率考虑。

IF 1.4 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae023

Danni Shi, Ting Ye

{"title":"Behavioral carry-over effect and power consideration in crossover trials.","authors":"Danni Shi, Ting Ye","doi":"10.1093/biomtc/ujae023","DOIUrl":"10.1093/biomtc/ujae023","url":null,"abstract":"A crossover trial is an efficient trial design when there is no carry-over effect. To reduce the impact of the biological carry-over effect, a washout period is often designed. However, the carry-over effect remains an outstanding concern when a washout period is unethical or cannot sufficiently diminish the impact of the carry-over effect. The latter can occur in comparative effectiveness research, where the carry-over effect is often non-biological but behavioral. In this paper, we investigate the crossover design under a potential outcomes framework with and without the carry-over effect. We find that when the carry-over effect exists and satisfies a sign condition, the basic estimator underestimates the treatment effect, which does not inflate the type I error of one-sided tests but negatively impacts the power. This leads to a power trade-off between the crossover design and the parallel-group design, and we derive the condition under which the crossover design does not lead to type I error inflation and is still more powerful than the parallel-group design. We also develop covariate adjustment methods for crossover trials. We evaluate the performance of cross-over design and covariate adjustment using data from the MTN-034/REACH study.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10985791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Well-spread samples with dynamic sample sizes. 样本分布均匀，样本大小动态变化。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae026

Blair Robertson, Chris Price, Marco Reale

引用次数: 0

Efficient data integration under prior probability shift. 先验概率偏移下的高效数据整合。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae035

Ming-Yueh Huang, Jing Qin, Chiung-Yu Huang

{"title":"Efficient data integration under prior probability shift.","authors":"Ming-Yueh Huang, Jing Qin, Chiung-Yu Huang","doi":"10.1093/biomtc/ujae035","DOIUrl":"https://doi.org/10.1093/biomtc/ujae035","url":null,"abstract":"Conventional supervised learning usually operates under the premise that data are collected from the same underlying population. However, challenges may arise when integrating new data from different populations, resulting in a phenomenon known as dataset shift. This paper focuses on prior probability shift, where the distribution of the outcome varies across datasets but the conditional distribution of features given the outcome remains the same. To tackle the challenges posed by such shift, we propose an estimation algorithm that can efficiently combine information from multiple sources. Unlike existing methods that are restricted to discrete outcomes, the proposed approach accommodates both discrete and continuous outcomes. It also handles high-dimensional covariate vectors through variable selection using an adaptive least absolute shrinkage and selection operator penalty, producing efficient estimates that possess the oracle property. Moreover, a novel semiparametric likelihood ratio test is proposed to check the validity of prior probability shift assumptions by embedding the null conditional density function into Neyman's smooth alternatives (Neyman, 1937) and testing study-specific parameters. We demonstrate the effectiveness of our proposed method through extensive simulations and a real data example. The proposed methods serve as a useful addition to the repertoire of tools for dealing dataset shifts.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141070300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial. 在生物标志物分层试验中对生物标志物阳性和阴性亚组进行高效测试。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae056

Lang Li, Anastasia Ivanova

引用次数: 0