基于新统计方法的生存曲线投影和获益时间点估计

T. Monleón-Getino
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引用次数: 0

摘要

生存分析涉及到事件时间数据的分析,在肿瘤学等领域进行研究是至关重要的,通常使用生存函数S(t)计算,但在存在竞争风险(存在竞争事件)的情况下,有必要引入其他统计概念和方法,如累积发病率函数CI(t)。这被定义为事件时间小于或等于的受试者比例。本研究描述了一种方法,该方法能够从数字上获得CI(t)曲线的形状,并将受益时间点(BTP)估计为CI(t)最大值达到90%、95%或99%时的时间(t)。一旦你得到了CI(t)的数值函数,它就可以在无限长的时间内投影,并具有它所带来的所有限制。为了完成这项任务,提出了R函数威布尔累积事件()。在第一步中,这些函数将使用Kaplan–Meier方法获得的生存函数(S(t))转换为CI(t)。在第二步中,计算CI(t)的最佳拟合函数,以便使用两个过程来估计BTP,1)参数函数:通过非线性回归(nls)过程估计4个参数的威布尔增长曲线;或2)非参数方法:使用局部多项式回归(LPR)或LOESS拟合。为了说明该方法,给出了两个使用威布尔累积incidence()函数的例子。所提出的方法将有助于更好地跟踪疾病的演变(尤其是在存在竞争性风险的情况下),将时间投射到无穷大,这种方法可能有助于确定癌症等疾病当前趋势的原因。我们认为BTP点在心脏病或癌症等大型疾病中可能很重要,以寻求疾病的拐点、治疗关联或推测疾病的过程,并改变这些点的治疗方法。这些要点对于进一步做出医疗决策可能很重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method
Survival analysis concerns the analysis of time-to-event data and it is essential to study in fields such as oncology, the survival function, S(t), calculation is usually used, but in the presence of competing risks (presence of competing events), is necessary introduce other statistical concepts and methods, as is the Cumulative incidence function CI(t). This is defined as the proportion of subjects with an event time less than or equal to. The present study describe a methodology that enables to obtain numerically a shape of CI(t) curves and estimate the benefit time points (BTP) as the time (t) when a 90, 95 or 99% is reached for the maximum value of CI(t). Once you get the numerical function of CI(t), it can be projected for an infinite time, with all the limitations that it entails. To do this task the R function Weibull.cumulative.incidence() is proposed. In a first step these function transforms the survival function (S(t)) obtained using the Kaplan–Meier method to CI(t). In a second step the best fit function of CI(t) is calculated in order to estimate BTP using two procedures, 1) Parametric function: estimates a Weibull growth curve of 4 parameters by means a non-linear regression (nls) procedure or 2) Non parametric method: using Local Polynomial Regression (LPR) or LOESS fitting. Two examples are presented and developed using Weibull.cumulative.incidence() function in order to present the method. The methodology presented will be useful for performing better tracking of the evolution of the diseases (especially in the case of the presence of competitive risks), project time to infinity and it is possible that this methodology can help identify the causes of current trends in diseases like cancer. We think that BTP points can be important in large diseases like cardiac illness or cancer to seek the inflection point of the disease, treatment associate or speculate how is the course of the disease and change the treatments at those points. These points can be important to take medical decisions furthermore.
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