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随机比较研究中估计处理效应的稳健 ANCOVA 替代方法AI 解读

In comparing two treatments via a randomized clinical trial, the analysis of covariance (ANCOVA) technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its simple two sample estimation counterpart. Unfortunately, when the ANCOVA model is nonlinear, the resulting estimator is generally not consistent. Recently, various nonparametric alternatives to the ANCOVA, such as the augmentation methods,…

利用基线信息进行结局预测的最优分层AI 解读

A common practice in predictive medicine is to use current study data to construct a stratification procedure, which groups subjects according to baseline information and forms stratum-specific prevention or intervention strategies. A desirable stratification scheme would not only have small intra-stratum variation but also have a clinically meaningful discriminatory capability. We show how to obtain optimal stratification rules with such desirable properties from fitting a…

为未来比较研究有效选择目标人群AI 解读

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, utilizing the data from a current study involving similar…

参数化个体风险估计的校准AI 解读

For modern evidence-based medicine, decisions on disease prevention or management strategies are often guided by a risk index system. For each individual, the system uses his/her baseline information to estimate the risk of experiencing a future disease-related clinical event. Such a risk scoring scheme is usually derived from an overly simplified parametric model. To validate a model-based procedure, one may perform a standard global evaluation via, for instance, a receiver…

基于绝对预测误差估计量抽样分布的模型评价AI 解读

The construction of a reliable, practically useful prediction rule for future responses is heavily dependent on the ‘adequacy’ of the fitted regression model. In this article, we consider the absolute prediction error, the expected value of the absolute difference between the future and predicted responses, as the model evaluation criterion. This prediction error is easier to interpret than the average squared error and is equivalent to the misclassification error for a…

随机参数的最优置信区域AI 解读

Suppose that, under a two-level hierarchical model, the distribution of the vector of random parameters is known or can be estimated well. The data are generated via a fixed, but unobservable, realisation of the vector. We derive the smallest confidence region for a specific component of this random vector under a joint Bayesian/frequentist paradigm. On average this optimal region can be much smaller than the corresponding Bayesian highest posterior density region. The new…

删失生存观测的半参数 Box–Cox 幂变换模型AI 解读

The accelerated failure time model specifies that the logarithm of the failure time is linearly related to the covariate vector without assuming a parametric error distribution. In this paper, we consider the semiparametric Box–Cox transformation model, which includes the above regression model as a special case, to analyse possibly censored failure time observations. Inference procedures for the transformation and regression parameters are proposed via a resampling…

重复事件均值函数与发生率函数的半参数回归AI 解读

Summary The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate…

观测与删失时间依赖协变量的面板计数数据回归分析AI 解读

Summary Panel count data often occur in a long-term study where the primary end point is the time to a specific event and each subject may experience multiple recurrences of this event. Furthermore, suppose that it is not feasible to keep subjects under observation continuously and the numbers of recurrences for each subject are only recorded at several distinct time points over the study period. Moreover, the set of observation times may vary from subject to subject. In…

依赖删失重复测量下治疗差异的秩估计AI 解读

In comparing the effectiveness of two treatments, suppose that for each patient repeated measurements of an outcome variable are taken at prespecified time points, but some observations may be missing due to the patient's dependent right censoring. In this article, a simple rank estimation procedure, constructed based on an artificial censoring technique, is proposed for the treatment differences over time without imposing a parametric structure on the dependence between the…

依赖删失下基于重复测量的治疗差异秩估计AI 解读

In comparing the effectiveness of two treatments, suppose that for each patient repeated measurements of an outcome variable are taken at prespecified time points, but some observations may be missing due to the patient's dependent right censoring. In this article, a simple rank estimation procedure, constructed based on an artificial censoring technique, is proposed for the treatment differences over time without imposing a parametric structure on the dependence between the…

半参数变换模型下的生存概率预测AI 解读

Prediction of survival probabilities for future patients is one of the main goals of fitting survival data with regression models. In this article we consider a large class of semiparametric transformation models, which includes the well-known proportional hazards and proportional odds models, for the analysis of failure time data. Specifically, we propose pointwise and simultaneous confidence interval procedures for the survival probability of future patients with specific…

基于中位数回归模型的生存分析AI 解读

The median is a simple and meaningful measure for the center of a long-tailed survival distribution. To examine the covariate effects on survival, a natural alternative to the usual mean regression model is to regress the median of the failure time variable or a transformation thereof on the covariates. In this article we propose semiparametric procedures to make inferences for such median regression models with possibly censored observations. Our proposals can be…

删失数据下变换模型的分析AI 解读

In this paper we consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linearly related to the covariates with various completely specified error distributions. This class of regression models includes the proportional hazards and proportional odds models. Inference procedures derived from a class of generalised estimating equations are proposed to examine the covariate effects with censored observations.…

基于枢轴估计函数的重抽样方法AI 解读

Suppose that, under a semiparametric model setting, one is interested in drawing inferences about a finite-dimensional parameter vector β based on an estimating function. Generally a consistent point estimator β^ for βo, the true value for β, can be easily obtained by finding a root of the corresponding estimating equation. To estimate the variance of β^, however, may involve complicated and subjective nonparametric functional estimates. In this paper, a general and simple…

比例风险模型下生存曲线的置信带AI 解读

In this paper, we show how to construct simultaneous confidence bands for the subject-specific survival curve under the Cox proportional hazards model. The idea is to approximate the distribution of the normalized cumulative hazard estimator by a zero-mean Gaussian process whose distribution can be easily generated through simulation. Numerical studies indicate that the proposed bands are appropriate for practical use. A liver disease example is presented.

摘要暂缺

基于枢轴估计函数的重抽样方法AI 解读

SUMMARY Suppose that, under a semiparametric model setting, one is interested in drawing inferences about a finite-dimensi onal parameter vector /? based on an estimating function. Generally a consistent point estimator /J for /?0, the true value for /J, can be easily obtained by finding a root of the corresponding estimating equation. To estimate the variance of ft, however, may involve complicated and subjective nonparametric functional estimates. In this paper, a general…

基于中位生存时间的期中分析AI 解读

For a typical two-arm survival study, analyses are usually performed periodically to evaluate, for example, the treatment difference. In this paper, we propose to use differences or ratios of estimated median survival times repeatedly during a study to evaluate the relative merit of two treatment groups. This new procedure provides quantitative assessments of the treatment difference and is completely model-free. Our method is illustrated with a recent aids clinical study…

高度分层失效时间数据的线性回归分析AI 解读

In this article we consider cases where data consist of many small and independent groups of correlated failure time observations. For each failure time, which may be censored, some important covariates are also recorded. Our goal is to examine the covariate effects on the individual failure time observations. We assume that the logarithm of each failure time is linearly related to its covariates. We then take a population-averaged model approach to obtain inference…

利用鞅残差累积和检验 Cox 模型AI 解读

This paper presents a new class of graphical and numerical methods for checking the adequacy of the Cox regression model. The procedures are derived from cumulative sums of martingale-based residuals over follow-up time and/or covariate values. The distributions of these stochastic processes under the assumed model can be approximated by zero-mean Gaussian processes. Each observed process can then be compared, both visually and analytically, with a number of simulated…

多元失效时间观测的线性回归分析AI 解读

In this article we consider the case that each patient in a longitudinal study may experience two or more distinct failures. The corresponding failure times, which are possible censored, are recorded for each patient. The logarithm of each marginal failure time is assumed to be linearly related to its covariates; however, the distributional form of the error term in the model does not have to be specified in the analysis. Furthermore, no specific structure of dependence…

基于线性回归模型预测生存概率AI 解读

After fitting survival data with a linear regression model, it is important to know how to use the results to make prediction of the t-year survival probability or median failure time for future patients with certain covariates. We propose some simple prediction procedures without using complicated and unstable nonparametric functional estimates. The new methods are illustrated with the Stanford heart transplant data.

基于线性回归模型预测生存概率AI 解读

After fitting survival data with a linear regression model, it is important to know how to use the results to make prediction of the t-year survival probability or median failure time for future patients with certain covariates. We propose some simple prediction procedures without using complicated and unstable nonparametric functional estimates. The new methods are illustrated with the Stanford heart transplant data.