基于Weibull回归模型的遗传方差分量模型研究

目的研究家系资料中生存时间资料的遗传方差分量模型，评价环境因素和遗传因素对删失性状的影响。方法将单随机效应的Weibull回归模型扩展到多个随机效应，建立遗传方差分量模型，运用MCMC的方法进行参数估计，通过模拟研究说明参数估计的准确性。结果模拟研究表明该方法可得到近似无偏的参数估计。结论基于Weibull回归模型的遗传方差分量模型可用于家系资料，研究生存时间资料的遗传和环境因素的作用。     

核心家系中质量性状遗传方差分量模型的研究

目的研究核心家系中带协变量的质量性状的遗传方差分量模型,定量地评价遗传因素和环境因素对质量性状的作用。方法将广义线性混合模型应用于核心家系资料建立遗传方差分量模型,运用MCMC方法进行参数估计。结果模拟研究表明大样本时MCMC法可得到近似无偏的参数估计。结论该方法可应用于家系资料,分析遗传因素和环境因素对质量表型的影响大小。     

核心家系中数量性状遗传方差分量模型的研究

目的 研究核心家系中带协变量的数量性状的遗传方差分量模型，探索数量性状的遗传因素和环境因素作用大小的分析方法。方法 将线性涨合模型应用于核心家系资料，根据核心家系成员的遗传关系建立遗传方差分量模型，运用SAS软件中的Mixed模块和WinBUGS软件进行参数估计，分析各影响因素作用大小。结果 模拟研究表明REML法和MCMC法都可得到近似无偏的参数估计。REML法计算耗时较少，但该法对各参数的区间估计具有一定的局限性，而MCMC法可方便地得到各参数的可信区间。结论 该模型可应用于实际家系资料，分析遗传因素和环境因素对数量表型的影响大小。     

三臂非劣效性设计生存时间数据的半参数统计推断方法

目的 推荐并改进一种用于新药临床试验中生存分析三臂非劣效性设计的半参数统计推断方法.方法 采用Cox比例风险模型建立用于生存时间数据的三臂非劣效检验方法,通过模拟试验验证该方法的有效性,并给出样本含量估计的R语言程序.结果 模拟试验结果证实,在样本删失风险率维持在一定水平时,三臂Cox模型能有效地控制Ⅰ类错误发生概率,并保证较高的检验效能,实际应用简单有效.结论 本文推荐的方法,可以有效地应用于三臂生存时间数据的非劣效性研究.     

样本量及删失率对生存分析模型有效性和偏倚性的影响

目的 探讨样本量及删失率对生存分析模型有效性和偏倚性的影响,为根据资料特点选用适合的生存分析方法提供参考依据.方法 以实际数据为总体,通过抽样模拟和删失模拟,比较不同样本量和删失率下,Cox模型和加速失效时间模型的有效性和偏倚性及其变化趋势.结果 对于大样本(≥500)或删失率较小(≤30％)的数据,两种模型的有效性和偏倚性相近,分析结果均较为可靠.当样本较小(300 ～400)或删失率较高(40％～60％)时,加速失效时间模型的分析结果更为可靠.结论 Cox模型对于大样本或删失率低的数据更为实用.加速失效时间模型的分析结果在样本量较少或删失率较高时更为可靠.     

不同删失比例下AFT模型与Cox模型表现比较的模拟研究

目的评价当生存数据出现不同删失比例的条件下,AFT模型和Cox模型的表现优劣。方法采用自编SAS宏程序模拟不同参数设置、不同比例均匀删失的Weibull分布,分别采用AFT模型和Cox模型对模拟产生的数据进行拟合,并从偏倚、准确性和覆盖程度三个指标对两个模型的表现进行评价。结果在分布参数和自变量效应值固定的情况下,AFT模型在参数估计偏倚和准确性两方面的表现却始终优于Cox模型。删失比例越大,AFT模型的表现相对而言越优异。而在覆盖程度方面,对于相同参数设置及删失比例的模拟数据,两个模型的表现相似。结论 AFT模型的总体表现优于Cox模型,当删失生存数据同时满足两类模型的应用条件时,应该优先选择偏倚更小、准确性更高的AFT模型进行分析。     

生存结局的多中介变量的中介分析方法比较

目的本研究以生存结局为切入点,探讨含两个中介变量时的中介生存分析模型(Aalen相加风险模型、Cox比例风险模型、加速失效时间AFT模型),为预后的多中介变量分析方法的选择提供应用建议。方法通过统计模拟试验,设定不同的相关系数、效应比、删失率等,从第一类错误及检验效能等方面对上述三种方法进行统计学性质评价。结果中介变量与暴露的相关系数越大,越容易发现中介变量的中介效应;删失率与效应比对Aalen模型的影响较大,对其他两种模型的影响较小;随着删失率的降低,Aalen模型的第一类错误反而膨胀,故Aalen模型不适用于多中介变量的分析;样本量越大,三种模型的检验效能差别减小且趋于稳定。不同参数设定下,AFT模型的检验效能最大,其次为Cox模型,最后为Aalen模型。结论 AFT模型优于其他两种方法,推荐用于生存结局的多中介变量的中介分析;进行中介分析时需要足够的样本量。     

有监督的主成分分析和偏Cox回归模型在基因数据生存预测中的应用

目的探讨有监督的主成分分析及偏Cox回归模型在基因数据生存预测中的应用。方法针对基因数据的协变量个数大于样本例数,以及变量间存在相关性等特点进行模拟研究,并对国际上公开的三个基因数据集进行分析,考察两种模型的预测性能。结果模拟研究显示随着影响生存的基因块的方差的增大以及组内相关系数的增高,两种方法的预测性能变好;随着删失比例的增加,两种方法的预测性能变差。实例分析提示不同的数据集最适方法不同。结论 SuperPC和偏Cox回归都适用于基因数据的生存分析。在模拟中SuperPC比偏Cox回归的表现好,但偏Cox回归计算速度较快。     

基于基准实验的生存数据机器学习和COX模型的区分度性能比较

目的 比较随机生存森林模型、梯度提升模型、极限梯度提升模型与Cox比例风险回归模型对生存数据的区分度性能,为生存分析方法的应用提供参考。方法 基于基准实验框架,选择SEER数据库、TCGA数据库、R软件包共13个数据集,分别构建三种机器学习模型与Cox模型,以嵌套交叉验证获得Harrell’s C-index作为模型区分度性能评价指标,采用秩和检验比较模型间性能。结果 各数据集的C-index主要集中在0.6～0.75之间。单数据集的结果不全相同,各模型C-index差异仅在部分数据集有意义,且没有一致结论;四种方法的性能在所有数据集、高删失率数据集、低删失率数据集等不同组数据集间的C指数差异均无统计学意义。结论 在不同场景下的生存数据分析中,三种机器学习模型区分度性能与传统Cox模型相近。     

指定删失比例的生存数据模拟及R实现

目的探讨生成具有指定删失比例的模拟数据的新方法,并编写完整的模拟数据产生的R代码,方便使用。方法基于Cox比例风险模型框架分别推导考虑和不考虑协变量情况下的删失参数的闭式表达式或核密度估计,并基于R 4.0.2软件编写函数模拟产生满足指定删失比例的生存数据。通过1000次模拟数据来验证生成数据的删失比例是否与指定的删失比例一致。结果函数CenDatNoCov、CenDatBin、CenDatNorm和CenDatMixed可分别返回不考虑协变量,协变量为二项分布、正态分布以及混合分布情况下的删失数据,其中后三者内嵌的函数CensProbBin,CensProbNorm,CensProbMixed可分别得到相应情况下的删失参数值。模拟生成数据的删失比例与指定的删失比例近似相等。结论本研究所提供的方法及编写的R函数可有效生成指定删失比例的数据。     

Semiparametric linear transformation models: Effect measures,estimators, and applications

Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. We introduce the probabilistic index as a uniform effect measure for the class of transformation models. We discuss and compare three estimators using a working Cox regression model: the partial likelihood estimator, an estimator based on binary generalized linear models and one based on probabilistic index model estimating equations. The latter has a superior performance in terms of bias and variance when the working model is misspecified. For the purpose of illustration, we analyze data that were collected at an urban alcohol and drug detoxification unit.     

截尾分位数回归及其在生存分析中的应用

目的 探讨传统的生存分析方法中违背比例风险假设、对数线性假设或条件异方差存在时的替代方法及其应用.方法 介绍截尾分位数回归方法并对其进行统计模拟,与COX回归和加速失效回归模型进行比较,通过实例对肾衰患者留置导尿管后的感染时间进行生存分析.结果 COX模型对比例风险假设及离群点敏感且不能很好地反映数据的异质性.结论 在一定条件下截尾分位数回归模型作为传统生存分析的替代方法可克服其缺点.     

Cox回归模型与对数线性回归模型在生存分析中应用的比较

运用Ｃｏｘ回归模型和对数线性回归模型对１６８９例肝癌病人生存时间的分析，发现Ｃｏｘ回归模型能够提供主要的预后影响因素，其结果与特定的参数回归模型相接近，使临床上能够快速地获得预后的影响因素。     

Variance components analysis for pedigree-based censored survival data using generalized linear mixed models (GLMMs) and Gibbs sampling in BUGS

Complex human diseases are an increasingly important focus of genetic research. Many of the determinants of these diseases are unknown and there is often a strong residual covariance between relatives even when all known genetic and environmental factors have been taken into account. This must be modeled correctly whether scientific interest is focused on fixed effects, as in an association analysis, or on the covariance structure itself. Analysis is straightforward for multivariate normally distributed traits, but difficulties arise with other types of trait. Generalized linear mixed models (GLMMs) offer a potentially unifying approach to analysis for many classes of phenotype including right censored survival times. This includes age-at-onset and age-at-death data and a variety of other censored traits. Markov chain Monte Carlo (MCMC) methods, including Gibbs sampling, provide a convenient framework within which such GLMMs may be fitted. In this paper, we use BUGS ("Bayesian inference using Gibbs sampling": a readily available, generic Gibbs sampler) to fit GLMMs for right-censored survival times in nuclear and extended families. We discuss parameter interpretation and statistical inference, and show how to circumvent a number of important theoretical and practical problems. Using simulated data, we show that model parameters are consistent. We further illustrate our methods using data from an ongoing cohort study. Finally, we propose that the random effects associated with a genetic component of variance (e.g., sigma(2)(A)) in a GLMM may be regarded as an adjusted "phenotype" and used as input to a conventional model-based or model-free linkage analysis. This provides a simple way to conduct a linkage analysis for a trait reflected in a right-censored survival time while comprehensively adjusting for observed confounders at the level of the individual and latent environmental effects shared across families.     

Logistic‐AFT location‐scale mixture regression models with nonsusceptibility for left‐truncated and general interval‐censored data

In conventional survival analysis there is an underlying assumption that all study subjects are susceptible to the event. In general, this assumption does not adequately hold when investigating the time to an event other than death. Owing to genetic and/or environmental etiology, study subjects may not be susceptible to the disease. Analyzing nonsusceptibility has become an important topic in biomedical, epidemiological, and sociological research, with recent statistical studies proposing several mixture models for right‐censored data in regression analysis. In longitudinal studies, we often encounter left, interval, and right‐censored data because of incomplete observations of the time endpoint, as well as possibly left‐truncated data arising from the dissimilar entry ages of recruited healthy subjects. To analyze these kinds of incomplete data while accounting for nonsusceptibility and possible crossing hazards in the framework of mixture regression models, we utilize a logistic regression model to specify the probability of susceptibility, and a generalized gamma distribution, or a log‐logistic distribution, in the accelerated failure time location‐scale regression model to formulate the time to the event. Relative times of the conditional event time distribution for susceptible subjects are extended in the accelerated failure time location‐scale submodel. We also construct graphical goodness‐of‐fit procedures on the basis of the Turnbull–Frydman estimator and newly proposed residuals. Simulation studies were conducted to demonstrate the validity of the proposed estimation procedure. The mixture regression models are illustrated with alcohol abuse data from the Taiwan Aboriginal Study Project and hypertriglyceridemia data from the Cardiovascular Disease Risk Factor Two‐township Study in Taiwan. Copyright © 2013 John Wiley & Sons, Ltd.     

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.     

The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis.

For the past two decades the Cox proportional hazards model has been used extensively to examine the covariate effects on the hazard function for the failure time variable. On the other hand, the accelerated failure time model, which simply regresses the logarithm of the survival time over the covariates, has seldom been utilized in the analysis of censored survival data. In this article, we review some newly developed linear regression methods for analysing failure time observations. These procedures have sound theoretical justification and can be implemented with an efficient numerical method. The accelerated failure time model has an intuitive physical interpretation and would be a useful alternative to the Cox model in survival analysis.     

Sequence Kernel Association Test for Survival Traits

Rare variant tests have been of great interest in testing genetic associations with diseases and disease‐related quantitative traits in recent years. Among these tests, the sequence kernel association test (SKAT) is an omnibus test for effects of rare genetic variants, in a linear or logistic regression framework. It is often described as a variance component test treating the genotypic effects as random. When the linear kernel is used, its test statistic can be expressed as a weighted sum of single‐marker score test statistics. In this paper, we extend the test to survival phenotypes in a Cox regression framework. Because of the anticonservative small‐sample performance of the score test in a Cox model, we substitute signed square‐root likelihood ratio statistics for the score statistics, and confirm that the small‐sample control of type I error is greatly improved. This test can also be applied in meta‐analysis. We show in our simulation studies that this test has superior statistical power except in a few specific scenarios, as compared to burden tests in a Cox model. We also present results in an application to time‐to‐obesity using genotypes from Framingham Heart Study SNP Health Association Resource.