日期精度：到日 11 篇 · 到月 11 篇 · 到年 4 篇
数据裂分与抽样设计：讨论
Leiner et al. offers a new paradigm for post-selection inference while connecting to the long-standing importance of conditioning in likelihood inference. By injecting noise to the data, the transf...
回归模型与多元生命表
Semiparametric, multiplicative-form regression models are specified for marginal single and double failure hazard rates for the regression analysis of multivariate failure time data. Cox-type estimating functions are specified for single and double failure hazard ratio parameter estimation, and corresponding Aalen-Breslow estimators are specified for baseline hazard rates. Generalization to allow classification of failure times into a smaller set of failure types, with…
事件发生时间数据的风险估计：妇女健康倡议应用
Accurate and individualized risk prediction is critical for population control of chronic diseases such as cancer and cardiovascular disease. Large cohort studies provide valuable resources for building risk prediction models, as the risk factors are collected at the baseline and subjects are followed over time until disease occurrence or termination of the study. However, for rare diseases the baseline risk may not be estimated reliably based on cohort data only, due to…
用于蛋白质组通路分析的正则化 Hotelling T² 检验
Recent proteomic studies have identified proteins related to specific phenotypes. In addition to marginal association analysis for individual proteins, analyzing pathways (functionally related sets of proteins) may yield additional valuable insights. Identifying pathways that differ between phenotypes can be conceptualized as a multivariate hypothesis testing problem: whether the mean vector μ of a p-dimensional random vector X is μ0. Proteins within the same biological…
基于风险函数的非参数生存函数估计
Summary A representation is developed that expresses the bivariate survivor function as a function of the hazard function for truncated failure time variables. This leads to a class of nonparametric survivor function estimators that avoid negative mass. The transformation from hazard function to survivor function is weakly continuous and compact differentiable, so that such properties as strong consistency, weak convergence to a Gaussian process and bootstrap applicability…
协变量测量误差下配对失效时间依赖性的估计
Summary In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the…
利用协变量可靠性样本的失效时间回归风险集校准法
Summary Regression parameter estimation in the Cox failure time model is considered when regression variables are subject to measurement error. Assuming that repeat regression vector measurements adhere to a classical measurement model, we can consider an ordinary regression calibration approach in which the unobserved covariates are replaced by an estimate of their conditional expectation given available covariate measurements. However, since the rate of withdrawal from the…
二元失效时间数据的一类加权依赖度量
Summary This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit…
比例优势回归模型中的半参数推断
For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have…
比例优势回归模型的半参数推断
For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have…
疾病风险因素的聚合数据研究
Statistical methods are proposed for estimating relative rate parameters, based on estimated disease rates and covariate data from random samples of individuals from each of several cohorts. A random effects model is used to derive mean and variance models for estimated disease rates. Estimating equations for relative rate parameters are then developed by replacing cohort covariate averages by corresponding sample averages. The asymptotic distribution of regression parameter…
相关失效时间数据中风险比参数的估计方程
Weighted partial likelihood estimating equations are proposed for the estimation of marginal hazard ratio parameters based on correlated failure time data. Asymptotic distribution theory is derived for the solution to such equations using martingale convergence results and inverse function theory. Simulation studies and theoretical efficiency calculations indicate that the inclusion of weights in the estimating equation produces important efficiency gains only if the…
利用部分指数模型估计多元均值参数
SUMMARY A class of partly exponential models is proposed for the regression analysis of multivariate response data. The class is parameterized in terms of the response mean and a general shape parameter. It includes the generalized linear error model and exponential dispersion models as special cases. Maximum likelihood equations for mean parameters are shown to be of the same form as certain generalized estimating equations, and maximum likelihood estimates of mean and…
利用二次指数模型分析相关二元响应
A model of quadratic exponential form is parameterized in terms of marginal means and pairwise correlations for the regression analysis of correlated binary data. Pseudo-maximum likelihood methods using a special case termed the multiplicative model are proposed, but are noted to be computationally unattractive if the ‘blocks’ of correlated responses are at all large. On the other hand score estimating functions for mean and correlation parameters are shown to be of a…
使用二次指数模型的相关二元回归
A model of quadratic exponential form is parameterized in terms of marginal means and pairwise correlations for the regression analysis of correlated binary data. Pseudo-maximum likelihood methods using a special case termed the multiplicative model are proposed, but are noted to be computationally unattractive if the ‘blocks’ of correlated responses are at all large. On the other hand score estimating functions for mean and correlation parameters are shown to be of a…
病例队列研究的渐近分布理论与效率结果
A case-cohort design was recently proposed [Prentice (1986)] as a means of reducing cost in large epidemiologic cohort studies. A "pseudolikelihood" procedure was described for relative risk regression parameter estimation. This procedure involves covariate data only on subjects who develop disease and on a random subset of the entire cohort. In contrast, the usual partial likelihood estimation procedure requires covariate histories on the entire cohort. Accordingly, a…
相对风险回归的残差
Several possible definitions of residuals are given for relative risk regression with time-varying covariates. Each such residual has a representation as an estimator of a stochastic integral with respect to the martingale arising from a subject's failure time counting process. Previously proposed residuals for individual study subjects and for specific time points are shown to be special cases of this definition, as are previously derived regression diagnostics. An…
相对风险回归的残差
Several possible definitions of residuals are given for relative risk regression with time-varying covariates. Each such residual has a representation as an estimator of a stochastic integral with respect to the martingale arising from a subject's failure time counting process. Previously proposed residuals for individual study subjects and for specific time points are shown to be special cases of this definition, as are previously derived regression diagnostics. An…
相关 Probit 回归模型的似然推断
Equicorrelated binary observations are modelled using a multivariate probit regression model. Log likelihood derivatives are reduced to simple linear combinations of equicorrelated multivariate normal probabilities, which are approximated using the method of Mendell & Elston (1974). A data set with overdispersion illustrates the use of this model.
相关 Probit 回归模型的似然推断
Equicorrelated binary observations are modelled using a multivariate probit regression model. Log likelihood derivatives are reduced to simple linear combinations of equicorrelated multivariate normal probabilities, which are approximated using the method of Mendell & Elston (1974). A data set with overdispersion illustrates the use of this model.
一般相对风险形式下 Cox 型回归模型的渐近分布理论
The theory and application of the Cox (1972) failure time regression model has, almost without exception, assumed an exponential form for the dependence of the hazard function on regression variables. Other regression forms may be more natural or descriptive in some applications. For example, a linear relative risk regression model provides a convenient framework for studying epidemiologic risk factor interactions. This note uses the counting process formulation of Andersen…
对 Andersen 与 Gill《计数过程中的 Cox 回归模型：大样本研究》的评论
In this issue Andersen and Gill (hereafter AG) present a stimulating development of asymptotic distribution theory for the Cox regression model with time-dependent covariates. They use a counting process formulation for the failure time data and martingale covergence results. This approach involves such conditions as $\sigma$-algebra right continuity and predictable, locally bounded, covariate processes. In this commentary we consider the implications of such assumptions for…