Statistics in Biosciences最新文献_第6页

Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome. 二元结果的异质治疗效果的贝叶斯指数模型。

IF 1

Statistics in Biosciences Pub Date : 2023-01-01 Epub Date: 2023-05-19 DOI: 10.1007/s12561-023-09370-0

Hyung G Park, Danni Wu, Eva Petkova, Thaddeus Tarpey, R Todd Ogden

引用次数: 0

Analysis of the Cox Model with Longitudinal Covariates with Measurement Errors and Partly Interval Censored Failure Times, with Application to an AIDS Clinical Trial. 具有测量误差和部分区间截尾失效时间的纵向协变量的Cox模型分析及其在艾滋病临床试验中的应用。

IF 0.8

Statistics in Biosciences Pub Date : 2023-01-01 Epub Date: 2023-05-20 DOI: 10.1007/s12561-023-09372-y

Yanqing Sun, Qingning Zhou, Peter B Gilbert

{"title":"Analysis of the Cox Model with Longitudinal Covariates with Measurement Errors and Partly Interval Censored Failure Times, with Application to an AIDS Clinical Trial.","authors":"Yanqing Sun, Qingning Zhou, Peter B Gilbert","doi":"10.1007/s12561-023-09372-y","DOIUrl":"10.1007/s12561-023-09372-y","url":null,"abstract":"Time-dependent covariates are often measured intermittently and with measurement errors. Motivated by the AIDS Clinical Trials Group (ACTG) 175 trial, this paper develops statistical inferences for the Cox model for partly interval censored failure times and longitudinal covariates with measurement errors. The conditional score methods developed for the Cox model with measurement errors and right censored data are no longer applicable to interval censored data. Assuming an additive measurement error model for a longitudinal covariate, we propose a nonparametric maximum likelihood estimation approach by deriving the measurement error induced hazard model that shows the attenuating effect of using the plug-in estimate for the true underlying longitudinal covariate. An EM algorithm is devised to facilitate maximum likelihood estimation that accounts for the partly interval censored failure times. The proposed methods can accommodate different numbers of replicates for different individuals and at different times. Simulation studies show that the proposed methods perform well with satisfactory finite-sample performances and that the naive methods ignoring measurement error or using the plug-in estimate can yield large biases. A hypothesis testing procedure for the measurement error model is proposed. The proposed methods are applied to the ACTG 175 trial to assess the associations of treatment arm and time-dependent CD4 cell count on the composite clinical endpoint of AIDS or death.Supplementary information: The online version contains supplementary material available at 10.1007/s12561-023-09372-y.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9988713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes 1型糖尿病青少年家庭亲子关系的感知增强隐马尔可夫模型

IF 1

Statistics in Biosciences Pub Date : 2022-12-30 DOI: 10.1007/s12561-022-09360-8

R. Lu, T. Nansel, Zhen Chen

引用次数: 0

Properties of the Estimators of the Cox Regression Model with Imputed Data 具有脉冲数据的Cox回归模型估计的性质

IF 1

Statistics in Biosciences Pub Date : 2022-12-30 DOI: 10.1007/s12561-022-09361-7

L. Chiapella, M. Quaglino, M. Mamprin

引用次数: 0

Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes. 针对性搜索个性化临床决策规则以优化临床结果。

IF 1

Statistics in Biosciences Pub Date : 2022-12-01 DOI: 10.1007/s12561-022-09343-9

Yanqing Wang, Yingqi Zhao, Yingye Zheng

{"title":"Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes.","authors":"Yanqing Wang, Yingqi Zhao, Yingye Zheng","doi":"10.1007/s12561-022-09343-9","DOIUrl":"https://doi.org/10.1007/s12561-022-09343-9","url":null,"abstract":"Novel biomarkers, in combination with currently available clinical information, have been sought to enhance clinical decision making in many branches of medicine, including screening, surveillance and prognosis. An individualized clinical decision rule (ICDR) is a decision rule that matches subgroups of patients with tailored medical regimen based on patient characteristics. We proposed new approaches to identify ICDRs by directly optimizing a risk-adjusted clinical benefit function that acknowledges the tradeoff between detecting disease and over-treating patients with benign conditions. In particular, we developed a novel plug-in algorithm to optimize the risk-adjusted clinical benefit function, which leads to the construction of both nonparametric and linear parametric ICDRs. In addition, we proposed a novel approach based on the direct optimization of a smoothed ramp loss function to further enhance the robustness of a linear ICDR. We studied the asymptotic theories of the proposed estimators. Simulation results demonstrated good finite sample performance for the proposed estimators and improved clinical utilities when compared to standard approaches. The methods were applied to a prostate cancer biomarker study.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270673/pdf/nihms-1901289.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10016922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 interrupted time series model for assessing complex health care interventions. 用于评估复杂医疗干预措施的广义间断时间序列模型。

IF 1

Statistics in Biosciences Pub Date : 2022-12-01 Epub Date: 2022-05-25 DOI: 10.1007/s12561-022-09346-6

Maricela Cruz, Hernando Ombao, Daniel L Gillen

{"title":"A generalized interrupted time series model for assessing complex health care interventions.","authors":"Maricela Cruz, Hernando Ombao, Daniel L Gillen","doi":"10.1007/s12561-022-09346-6","DOIUrl":"10.1007/s12561-022-09346-6","url":null,"abstract":"Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous-valued outcomes. We propose the \"Generalized Robust ITS\" (GRITS) model appropriate for outcomes whose underlying distribution belongs to the exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient falls from a hospital that implemented and evaluated a new care delivery model in multiple units.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208393/pdf/nihms-1884816.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9558674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semiparametric Density Ratio Model for Survival Data with a Cure Fraction 带有治愈分数的生存数据的半参数密度比模型

IF 1

Statistics in Biosciences Pub Date : 2022-10-07 DOI: 10.1007/s12561-022-09357-3

Weibin Zhong, G. Diao

引用次数: 0

A Step-Wise Multiple Testing for Linear Regression Models with Application to the Study of Resting Energy Expenditure 线性回归模型的逐步多重检验及其在静息能量消耗研究中的应用

IF 1

Statistics in Biosciences Pub Date : 2022-09-17 DOI: 10.1007/s12561-022-09355-5

Junyi Zhang, Zimian Wang, Zhezhen Jin, Zhiliang Ying

引用次数: 0

Bayesian Framework for Causal Inference with Principal Stratification and Clusters 具有主分层和聚类的贝叶斯因果推理框架

IF 1

Statistics in Biosciences Pub Date : 2022-07-23 DOI: 10.1007/s12561-022-09351-9

Li He, Yu-Bo Wang, W. Bridges, Zhulin He, S. Che

引用次数: 0

Introduction to Special Issue on Leveraging External Data to Improve Trial Efficiency 《利用外部数据提高审判效率》特刊导言

IF 1

Statistics in Biosciences Pub Date : 2022-06-08 DOI: 10.1007/s12561-022-09348-4

Lanju Zhang, Naitee Ting

引用次数: 1