Biostatistics最新文献

{"title":"Control arm augmentation and hierarchical modeling in time-to-event trials: advantages and pitfalls.","authors":"Ethan M Alt, Xiuya Chang, Qing Liu, Xun Jiang, May Mo, H Amy Xia, Joseph G Ibrahim","doi":"10.1093/biostatistics/kxaf021","DOIUrl":"10.1093/biostatistics/kxaf021","url":null,"abstract":"<p><p>In clinical trials, it is often valuable to borrow information from external data sources. Unfortunately, when the external data are fully or partially incompatible with the current trial data, type I error rates can be highly inflated under traditional blanket discounting schemes such as power priors, commensurate priors, and meta-analytic predictive priors. However, such inflation of the probability of a false positive can be necessary, as the alternative is to have an underpowered study. For clinical trials with time-to-event (TTE) outcomes, this problem is exacerbated since many observations are censored. In this paper, we develop the latent exchangeability prior for TTE data. We also present a novel framework to borrow information about the treatment effect between groups as well as incorporate information from external controls. Simulation results suggest that, although efficiency gains can be achieved by borrowing information among external controls, operating characteristics in general can be quite poor under a lack of exchangeability. We apply our approach to a real clinical trial in second-line metastatic colorectal cancer.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1093/biostatistics/kxae029","DOIUrl":"10.1093/biostatistics/kxae029","url":null,"abstract":"","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous clustering and estimation of networks in multiple graphical models.","authors":"Gen Li, Miaoyan Wang","doi":"10.1093/biostatistics/kxae015","DOIUrl":"10.1093/biostatistics/kxae015","url":null,"abstract":"<p><p>Gaussian graphical models are widely used to study the dependence structure among variables. When samples are obtained from multiple conditions or populations, joint analysis of multiple graphical models are desired due to their capacity to borrow strength across populations. Nonetheless, existing methods often overlook the varying levels of similarity between populations, leading to unsatisfactory results. Moreover, in many applications, learning the population-level clustering structure itself is of particular interest. In this article, we develop a novel method, called Simultaneous Clustering and Estimation of Networks via Tensor decomposition (SCENT), that simultaneously clusters and estimates graphical models from multiple populations. Precision matrices from different populations are uniquely organized as a three-way tensor array, and a low-rank sparse model is proposed for joint population clustering and network estimation. We develop a penalized likelihood method and an augmented Lagrangian algorithm for model fitting. We also establish the clustering accuracy and norm consistency of the estimated precision matrices. We demonstrate the efficacy of the proposed method with comprehensive simulation studies. The application to the Genotype-Tissue Expression multi-tissue gene expression data provides important insights into tissue clustering and gene coexpression patterns in multiple brain tissues.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11826093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HMM for discovering decision-making dynamics using reinforcement learning experiments.","authors":"Xingche Guo, Donglin Zeng, Yuanjia Wang","doi":"10.1093/biostatistics/kxae033","DOIUrl":"10.1093/biostatistics/kxae033","url":null,"abstract":"<p><p>Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure proximal immune correlates analysis.","authors":"Ying Huang, Dean Follmann","doi":"10.1093/biostatistics/kxae031","DOIUrl":"10.1093/biostatistics/kxae031","url":null,"abstract":"<p><p>Immune response decays over time, and vaccine-induced protection often wanes. Understanding how vaccine efficacy changes over time is critical to guiding the development and application of vaccines in preventing infectious diseases. The objective of this article is to develop statistical methods that assess the effect of decaying immune responses on the risk of disease and on vaccine efficacy, within the context of Cox regression with sparse sampling of immune responses, in a baseline-naive population. We aim to further disentangle the various aspects of the time-varying vaccine effect, whether direct on disease or mediated through immune responses. Based on time-to-event data from a vaccine efficacy trial and sparse sampling of longitudinal immune responses, we propose a weighted estimated induced likelihood approach that models the longitudinal immune response trajectory and the time to event separately. This approach assesses the effects of the decaying immune response, the peak immune response, and/or the waning vaccine effect on the risk of disease. The proposed method is applicable not only to standard randomized trial designs but also to augmented vaccine trial designs that re-vaccinate uninfected placebo recipients at the end of the standard trial period. We conducted simulation studies to evaluate the performance of our method and applied the method to analyze immune correlates from a phase III SARS-CoV-2 vaccine trial.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covariate-adjusted estimators of diagnostic accuracy in randomized trials.","authors":"Jon A Steingrimsson","doi":"10.1093/biostatistics/kxaf005","DOIUrl":"10.1093/biostatistics/kxaf005","url":null,"abstract":"<p><p>Randomized controlled trials evaluating the diagnostic accuracy of a marker frequently collect information on baseline covariates in addition to information on the marker and the reference standard. However, standard estimators of sensitivity and specificity do not use data on baseline covariates and restrict the analysis to data from participants with a positive reference standard in the intervention arm being evaluated. Covariate-adjusted estimators for marginal treatment effects have been developed and been advocated for by regulatory agencies because they can improve power compared to unadjusted estimators. Despite this, similar covariate-adjusted estimators for marginal sensitivity and specificity have not yet been developed. In this manuscript, we address this gap by developing covariate-adjusted estimators for marginal sensitivity and specificity of a diagnostic test that leverage baseline covariate information. The estimators also use data from all participants, not just participants with a positive reference standard in the intervention arm being evaluated. We derive the asymptotic properties of the estimators and evaluate the finite sample properties of the estimators using simulations and by analyzing data on lung cancer screening.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust transfer learning for individualized treatment rules in the presence of missing data.","authors":"Zhiyu Sui, Ying Ding, Lu Tang","doi":"10.1093/biostatistics/kxaf023","DOIUrl":"10.1093/biostatistics/kxaf023","url":null,"abstract":"<p><p>Individualized treatment rule (ITR) is a stepping stone to precision medicine. To ensure validity, ITRs are ideally derived from randomized trial data, but the use cases of ITRs extend beyond these trial populations. Transferring knowledge from experimental data to real-world data is of interest, while experimental data with selective inclusion criteria reflect a population distribution that may differ from the real-world target. In well-designed experiments, granular information crucial to decision making can be thoroughly collected. However, part of this may not be accessible in real-world scenarios. We propose a learning scheme for ITR that simultaneously addresses the issues of covariate shift and missing covariates with a quantile-based optimal treatment objective. Specifically, we compare the outcome uncertainty across treatment arms that is due to missing covariates and use it to guide treatment selection to reduce the likelihood of worse outcomes. The performance of this method is evaluated in simulations and a sepsis data application.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A surrogate endpoint-based provisional approval causal roadmap, illustrated by vaccine development.","authors":"Peter B Gilbert, James Peng, Larry Han, Theis Lange, Yun Lu, Lei Nie, Mei-Chiung Shih, Salina P Waddy, Ken Wiley, Margot Yann, Zafar Zafari, Debashis Ghosh, Dean Follmann, Michal Juraska, Iván Díaz","doi":"10.1093/biostatistics/kxaf018","DOIUrl":"10.1093/biostatistics/kxaf018","url":null,"abstract":"<p><p>For many rare diseases with no approved preventive interventions, promising interventions exist. However, it has proven difficult to conduct a pivotal phase 3 trial that could provide direct evidence demonstrating a beneficial effect of the intervention on the target disease outcome. When a promising putative surrogate endpoint(s) for the target outcome is available, surrogate-based provisional approval of an intervention may be pursued. Following the general Causal Roadmap rubric, we describe a surrogate endpoint-based provisional approval causal roadmap. Based on an observational study data set and a phase 3 randomized trial data set, this roadmap defines an approach to analyze the combined data set to draw a conservative inference about the treatment effect (TE) on the target outcome in the phase 3 study population. The observational study enrolls untreated individuals and collects baseline covariates, surrogate endpoints, and the target outcome, and is used to estimate the surrogate index-the regression of the target outcome on the surrogate endpoints and baseline covariates. The phase 3 trial randomizes participants to treated vs. untreated and collects the same data but is much smaller and hence very underpowered to directly assess TE, such that inference on TE is based on the surrogate index. This inference is made conservative by specifying 2 bias functions: one that expresses an imperfection of the surrogate index as a surrogate endpoint in the phase 3 study, and the other that expresses imperfect transport of the surrogate index in the untreated from the observational to the phase 3 study. Plug-in and nonparametric efficient one-step estimators of TE, with inferential procedures, are developed. The finite-sample performance of the estimators is evaluated in simulation studies. The causal roadmap is motivated by and illustrated with contemporary Group B Streptococcus vaccine development.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144369548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomarker-assisted reporting in nutritional epidemiology: addressing measurement error in exposure-disease associations.","authors":"Ying Huang, Ross L Prentice","doi":"10.1093/biostatistics/kxaf014","DOIUrl":"10.1093/biostatistics/kxaf014","url":null,"abstract":"<p><p>In nutritional epidemiology, self-reported dietary data are commonly used to investigate diet-disease relationships. However, the resulting association estimates are often subject to biases due to random and systematic measurement errors. Regression calibration has emerged as a crucial method for addressing these biases by refining self-reported nutrient intake with objective biomarkers, which differ from the true values only by a random \"noise\" component. This paper presents methodological tools for analyzing nutritional epidemiology cohort studies involving time-to-event data when a biomarker subsample is available alongside dietary assessments. We introduce novel regression calibration methods to tackle two common challenges in this field. First, a widely used approach assumes that the log hazard ratio (HR) follows a linear function of dietary exposure. However, assessing whether this assumption holds-or if a more flexible model is needed to capture potential deviations from linearity-is often necessary. Second, another prevalent analytical strategy involves estimating HRs based on categorized dietary exposure variables. New methods are critically needed to minimize bias in defining category boundaries and estimating hazard ratios within exposure categories, both of which can be distorted by measurement error. We apply these methods to reassess the relationship between sodium and potassium intake and cardiovascular disease risk using data from the Women's Health Initiative.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12129076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the power of time-since-infection models: data augmentation for improved instantaneous reproduction number estimation.","authors":"Jiasheng Shi, Yizhao Zhou, Jing Huang","doi":"10.1093/biostatistics/kxae054","DOIUrl":"10.1093/biostatistics/kxae054","url":null,"abstract":"<p><p>The time-since-infection (TSI) models, which use disease surveillance data to model infectious diseases, have become increasingly popular due to their flexibility and capacity to address complex disease control questions. However, a notable limitation of TSI models is their primary reliance on incidence data. Even when hospitalization data are available, existing TSI models have not been crafted to improve the estimation of disease transmission or to estimate hospitalization-related parameters-metrics crucial for understanding a pandemic and planning hospital resources. Moreover, their dependence on reported infection data makes them vulnerable to variations in data quality. In this study, we advance TSI models by integrating hospitalization data, marking a significant step forward in modeling with TSI models. We introduce hospitalization propensity parameters to jointly model incidence and hospitalization data. We use a composite likelihood function to accommodate complex data structure and a Monte Carlo expectation-maximization algorithm to estimate model parameters. We analyze COVID-19 data to estimate disease transmission, assess risk factor impacts, and calculate hospitalization propensity. Our model improves the accuracy of estimating the instantaneous reproduction number in TSI models, particularly when hospitalization data is of higher quality than incidence data. It enables the estimation of key infectious disease parameters without relying on contact tracing data and provides a foundation for integrating TSI models with other infectious disease models.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}