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受试者工作特征曲线下面积的半参数回归AI 解读

Medical advances continue to provide new and potentially better means for detecting disease. Such is true in cancer, for example, where biomarkers are sought for early detection and where improvements in imaging methods may pick up the initial functional and molecular changes associated with cancer development. In other binary classification tasks, computational algorithms such as neural networks, support vector machines, and evolutionary algorithms have been applied to…

评价疾病生物标志物的半参数ROC分析AI 解读

The receiver operating characteristic (ROC) curve is a popular method for characterizing the accuracy of diagnostic tests when test results are not binary. Various methodologies for estimating and comparing ROC curves have been developed. One approach, due to Pepe, uses a parametric regression model ROCx(u) = g(h0(u) + θ'0x) with the baseline function h0(u) specified up to a finite-dimensional parameter. In this article we extend the regression models by allowing arbitrary…

利用期望估计方程处理协变量测量误差AI 解读

Summary Estimating equations which are not necessarily likelihood-based score equations are becoming increasingly popular for estimating regression model parameters. This paper is concerned with estimation based on general estimating equations when true covariate data are missing for all the study subjects, but surrogate or mismeasured covariates are available instead. The method is motivated by the covariate measurement error problem in marginal or partly conditional…

纵向数据中的部分条件均值建模AI 解读

We propose a general modeling approach to longitudinal data that is a hybrid of the marginal regression models of Zeger and Liang and of the classical transition models such as used in time series analyses. Rather than conditioning at time t only on covariate values, as is typical with the marginal approach, or on the entire history of the process up to t, as is typical with the transition model approach, we suggest models that condition on a subset of the process history.…

纵向数据的部分条件均值建模AI 解读

We propose a general modeling approach to longitudinal data that is a hybrid of the marginal regression models of Zeger and Liang and of the classical transition models such as used in time series analyses. Rather than conditioning at time t only on covariate values, as is typical with the marginal approach, or on the entire history of the process up to t, as is typical with the transition model approach, we suggest models that condition on a subset of the process history.…

回归模型中缺失及辅助协变量数据的平均得分方法AI 解读

We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but thought to be informative about the true missing covariate data of interest. We discuss a method which is nonparametric with respect to the association between available and…

回归中缺失及辅助协变量数据的平均得分法AI 解读

We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but thought to be informative about the true missing covariate data of interest. We discuss a method which is nonparametric with respect to the association between available and…

重复失效时间与时变协变量的图形展示及边际回归分析AI 解读

Recurrent event time data are common in medical research; examples include infections in AIDS patients and seizures in epilepsy patients. In this context, as well as in the more usual context of a single failure time variable, time-dependent covariates are frequently of interest. We suggest some rate functions that might be displayed when analyzing recurrent failure time data or when the effect of a categorical time-dependent covariate is of interest. Estimators of these…

利用代理结果数据与验证样本进行推断AI 解读

In the context of estimating β from the regression model Pβ(Y|X), relating response Y to covariates X, suppose that only a surrogate response S is available for most study subjects. Suppose that for a random subsample of the study cohort, termed the validation sample, the true outcome Y is available in addition to S. We consider maximum likelihood estimation of β from such data and show that it is nonrobust to misspecification of the distribution relating the surrogate to…

利用替代结局数据与验证样本进行推断AI 解读

In the context of estimating β from the regression model Pβ(Y|X), relating response Y to covariates X, suppose that only a surrogate response S is available for most study subjects. Suppose that for a random subsample of the study cohort, termed the validation sample, the true outcome Y is available in addition to S. We consider maximum likelihood estimation of β from such data and show that it is nonrobust to misspecification of the distribution relating the surrogate to…

多终点研究中风险相依事件的统计推断AI 解读

Kaplan–Meier and cumulative incidence functions are not sufficient descriptive devices for studies that have multiple time-to-event endpoints. For example, in cancer treatment research the probability of tumor recurrence conditional on not having died from treatment-related toxicities and the prevalence of graft-versus-host disease among leukemia-free patients surviving a bone marrow transplant are of interest. These quantities can be estimated nonparametrically using simple…

处理协变量测量错误的非参数方法AI 解读

Mismeasurement of covariate data is a frequent problem in statistical data analysis. However, when true and mismeasured data are obtained for a subsample of the observations, it is possible to estimate the parameters relating the outcome to the covariate of interest. Maximum likelihood methods that rely on parametric models for the mismeasurement have not met with much success. Realistic models for the mismeasurement process are difficult to construct; the form of the…

加权 Kaplan–Meier 统计量的大样本性质与最优性AI 解读

SUMMARY We propose a cumulative weighted difference in the Kaplan-Meier estimates as a test statistic for equality of distributions in the two-sample censored data survival analysis problem. For stability of such a statistic, the absolute value of the possibly random weight function must be bounded above by a multiple of (C −)1/2 + δ where 1 – C − is the left continuous censoring distribution function and δ > 0. For these weighted Kaplan–Meier (WKM) statistics, asymptotic…