Biometrics最新文献_第3页

Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae152

Peter Norwood, Marie Davidian, Eric Laber

{"title":"Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.","authors":"Peter Norwood, Marie Davidian, Eric Laber","doi":"10.1093/biomtc/ujae152","DOIUrl":"https://doi.org/10.1093/biomtc/ujae152","url":null,"abstract":"Response-adaptive randomization (RAR) has been studied extensively in conventional, single-stage clinical trials, where it has been shown to yield ethical and statistical benefits, especially in trials with many treatment arms. However, RAR and its potential benefits are understudied in sequential multiple assignment randomized trials (SMARTs), which are the gold-standard trial design for evaluation of multi-stage treatment regimes. We propose a suite of RAR algorithms for SMARTs based on Thompson Sampling (TS), a widely used RAR method in single-stage trials in which treatment randomization probabilities are aligned with the estimated probability that the treatment is optimal. We focus on two common objectives in SMARTs: (1) comparison of the regimes embedded in the trial and (2) estimation of an optimal embedded regime. We develop valid post-study inferential procedures for treatment regimes under the proposed algorithms. This is nontrivial, as even in single-stage settings standard estimators of an average treatment effect can have nonnormal asymptotic behavior under RAR. Our algorithms are the first for RAR in multi-stage trials that account for non-standard limiting behavior due to RAR. Empirical studies based on real-world SMARTs show that TS can improve in-trial subject outcomes without sacrificing efficiency for post-trial comparisons.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827259","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

An efficient joint model for high dimensional longitudinal and survival data via generic association features.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae149

Van Tuan Nguyen, Adeline Fermanian, Antoine Barbieri, Sarah Zohar, Anne-Sophie Jannot, Simon Bussy, Agathe Guilloux

{"title":"An efficient joint model for high dimensional longitudinal and survival data via generic association features.","authors":"Van Tuan Nguyen, Adeline Fermanian, Antoine Barbieri, Sarah Zohar, Anne-Sophie Jannot, Simon Bussy, Agathe Guilloux","doi":"10.1093/biomtc/ujae149","DOIUrl":"https://doi.org/10.1093/biomtc/ujae149","url":null,"abstract":"This paper introduces a prognostic method called FLASH that addresses the problem of joint modeling of longitudinal data and censored durations when a large number of both longitudinal and time-independent features are available. In the literature, standard joint models are either of the shared random effect or joint latent class type. Combining ideas from both worlds and using appropriate regularization techniques, we define a new model with the ability to automatically identify significant prognostic longitudinal features in a high-dimensional context, which is of increasing importance in many areas such as personalized medicine or churn prediction. We develop an estimation methodology based on the expectation-maximization algorithm and provide an efficient implementation. The statistical performance of the method is demonstrated both in extensive Monte Carlo simulation studies and on publicly available medical datasets. Our method significantly outperforms the state-of-the-art joint models in terms of C-index in a so-called \"real-time\" prediction setting, with a computational speed that is orders of magnitude faster than competing methods. In addition, our model automatically identifies significant features that are relevant from a practical point of view, making it interpretable, which is of the greatest importance for a prognostic algorithm in healthcare.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827261","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

Changepoint detection on daily home activity pattern: a sliced Poisson process method. 日常居家活动模式的变化点检测：一种切片泊松过程方法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae114

Israel Martínez-Hernández, Rebecca Killick

{"title":"Changepoint detection on daily home activity pattern: a sliced Poisson process method.","authors":"Israel Martínez-Hernández, Rebecca Killick","doi":"10.1093/biomtc/ujae114","DOIUrl":"https://doi.org/10.1093/biomtc/ujae114","url":null,"abstract":"The problem of health and care of people is being revolutionized. An important component of that revolution is disease prevention and health improvement from home. A natural approach to the health problem is monitoring changes in people's behavior or activities. These changes can be indicators of potential health problems. However, due to a person's daily pattern, changes will be observed throughout each day, with, eg, an increase of events around meal times and fewer events during the night. We do not wish to detect such within-day changes but rather changes in the daily behavior pattern from one day to the next. To this end, we assume the set of event times within a given day as a single observation. We model this observation as the realization of an inhomogeneous Poisson process where the rate function can vary with the time of day. Then, we propose to detect changes in the sequence of inhomogeneous Poisson processes. This approach is appropriate for many phenomena, particularly for home activity data. Our methodology is evaluated on simulated data. Overall, our approach uses local change information to detect changes across days. At the same time, it allows us to visualize and interpret the results, changes, and trends over time, allowing the detection of potential health decline.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457173","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

Functional generalized canonical correlation analysis for studying multiple longitudinal variables. 用于研究多个纵向变量的功能广义典型相关分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae113

Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus

引用次数: 0

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors. 采用高斯过程先验的群体级皮层表面图像标度回归的贝叶斯推断。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae116

Andrew S Whiteman, Timothy D Johnson, Jian Kang

{"title":"Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.","authors":"Andrew S Whiteman, Timothy D Johnson, Jian Kang","doi":"10.1093/biomtc/ujae116","DOIUrl":"10.1093/biomtc/ujae116","url":null,"abstract":"In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520911","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

Likelihood adaptively incorporated external aggregate information with uncertainty for survival data. 概率自适应地将外部总体信息与生存数据的不确定性结合起来。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae120

Ziqi Chen, Yu Shen, Jing Qin, Jing Ning

{"title":"Likelihood adaptively incorporated external aggregate information with uncertainty for survival data.","authors":"Ziqi Chen, Yu Shen, Jing Qin, Jing Ning","doi":"10.1093/biomtc/ujae120","DOIUrl":"10.1093/biomtc/ujae120","url":null,"abstract":"Population-based cancer registry databases are critical resources to bridge the information gap that results from a lack of sufficient statistical power from primary cohort data with small to moderate sample size. Although comprehensive data associated with tumor biomarkers often remain either unavailable or inconsistently measured in these registry databases, aggregate survival information sourced from these repositories has been well documented and publicly accessible. An appealing option is to integrate the aggregate survival information from the registry data with the primary cohort to enhance the evaluation of treatment impacts or prediction of survival outcomes across distinct tumor subtypes. Nevertheless, for rare types of cancer, even the sample sizes of cancer registries remain modest. The variability linked to the aggregated statistics could be non-negligible compared with the sample variation of the primary cohort. In response, we propose an externally informed likelihood approach, which facilitates the linkage between the primary cohort and external aggregate data, with consideration of the variation from aggregate information. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. Through the application of our proposed method, we integrate data from the cohort of inflammatory breast cancer (IBC) patients at the University of Texas MD Anderson Cancer Center with aggregate survival data from the National Cancer Data Base, enabling us to appraise the effect of tri-modality treatment on survival across various tumor subtypes of IBC.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520913","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

A generalized logrank-type test for comparison of treatment regimes in sequential multiple assignment randomized trials. 顺序多重分配随机试验中治疗方案比较的广义对数检验。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae139

Anastasios A Tsiatis, Marie Davidian

{"title":"A generalized logrank-type test for comparison of treatment regimes in sequential multiple assignment randomized trials.","authors":"Anastasios A Tsiatis, Marie Davidian","doi":"10.1093/biomtc/ujae139","DOIUrl":"10.1093/biomtc/ujae139","url":null,"abstract":"The sequential multiple assignment randomized trial (SMART) is the ideal study design for the evaluation of multistage treatment regimes, which comprise sequential decision rules that recommend treatments for a patient at each of a series of decision points based on their evolving characteristics. A common goal is to compare the set of so-called embedded regimes represented in the design on the basis of a primary outcome of interest. In the study of chronic diseases and disorders, this outcome is often a time to an event, and a goal is to compare the distributions of the time-to-event outcome associated with each regime in the set. We present a general statistical framework in which we develop a logrank-type test for comparison of the survival distributions associated with regimes within a specified set based on the data from a SMART with an arbitrary number of stages that allows incorporation of covariate information to enhance efficiency and can also be used with data from an observational study. The framework provides clarification of the assumptions required to yield a principled test procedure, and the proposed test subsumes or offers an improved alternative to existing methods. We demonstrate performance of the methods in a suite of simulation studies. The methods are applied to a SMART in patients with acute promyelocytic leukemia.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11636965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817045","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

On network deconvolution for undirected graphs. 关于无向图的网络解卷积。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae112

Zhaotong Lin, Isaac Pan, Wei Pan

{"title":"On network deconvolution for undirected graphs.","authors":"Zhaotong Lin, Isaac Pan, Wei Pan","doi":"10.1093/biomtc/ujae112","DOIUrl":"10.1093/biomtc/ujae112","url":null,"abstract":"Network deconvolution (ND) is a method to reconstruct a direct-effect network describing direct (or conditional) effects (or associations) between any two nodes from a given network depicting total (or marginal) effects (or associations). Its key idea is that, in a directed graph, a total effect can be decomposed into the sum of a direct and an indirect effects, with the latter further decomposed as the sum of various products of direct effects. This yields a simple closed-form solution for the direct-effect network, facilitating its important applications to distinguish direct and indirect effects. Despite its application to undirected graphs, it is not well known why the method works, leaving it with skepticism. We first clarify the implicit linear model assumption underlying ND, then derive a surprisingly simple result on the equivalence between ND and use of precision matrices, offering insightful justification and interpretation for the application of ND to undirected graphs. We also establish a formal result to characterize the effect of scaling a total-effect graph. Finally, leveraging large-scale genome-wide association study data, we show a novel application of ND to contrast marginal versus conditional genetic correlations between body height and risk of coronary artery disease; the results align with an inferred causal directed graph using ND. We conclude that ND is a promising approach with its easy and wide applicability to both directed and undirected graphs.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387636","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

ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications. ROMI：采用随机两阶段篮式试验设计，优化多种适应症的剂量。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae105

Shuqi Wang, Peter F Thall, Kentaro Takeda, Ying Yuan

{"title":"ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications.","authors":"Shuqi Wang, Peter F Thall, Kentaro Takeda, Ying Yuan","doi":"10.1093/biomtc/ujae105","DOIUrl":"10.1093/biomtc/ujae105","url":null,"abstract":"Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364261","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

A likelihood approach to incorporating self-report data in HIV recency classification.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae147

Wenlong Yang, Danping Liu, Le Bao, Runze Li

{"title":"A likelihood approach to incorporating self-report data in HIV recency classification.","authors":"Wenlong Yang, Danping Liu, Le Bao, Runze Li","doi":"10.1093/biomtc/ujae147","DOIUrl":"https://doi.org/10.1093/biomtc/ujae147","url":null,"abstract":"Estimating new HIV infections is significant yet challenging due to the difficulty in distinguishing between recent and long-term infections. We demonstrate that HIV recency status (recent versus long-term) could be determined from self-report testing history and biomarkers, which are increasingly available in bio-behavioral surveys. HIV recency status is partially observed, given the self-report testing history. For example, people who tested positive for HIV over 1 year ago should have a long-term infection. Based on the nationally representative samples collected by the Population-based HIV Impact Assessment (PHIA) Project, we propose a likelihood-based probabilistic model for HIV recency classification. The model incorporates individuals with known recency status based on testing histories and individuals whose recency status could not be determined and integrates the mechanism of how HIV recency status depends on biomarkers and the mechanism of how HIV recency status, together with the self-report time of the most recent HIV test, impacts the test results. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi PHIA data, as well as on simulated data. Our model obtains more efficient and less biased parameter estimates and is relatively robust to potential reporting error and model misspecification.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827257","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