Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes
Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes
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DOI：
10.1016/j.jclinepi.2009.03.012
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发表时间：
2010-01-01
影响因子：
7.2
通讯作者：
Austin, Peter C.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.
目的：在随机对照试验和观察性队列研究中，考克斯比例风险回归模型常用于确定暴露与至事件发生时间结局之间的相关性。由此产生的风险比是一个相对措施的影响，提供有限的临床information.Study设计和设置：一种方法描述了用于推导绝对减少的风险事件发生在一个给定的持续时间的后续时间从考克斯回归模型。需要治疗的相关数量可以从这个数量中推导出来。该方法涉及基于回归模型中的协变量，确定如果队列中的每例受试者接受治疗以及如果每例受试者未接受治疗，则在指定的随访持续时间内发生结局的概率。然后将这些概率在整个研究人群中进行平均，以确定如果所有受试者均接受治疗和如果所有受试者均未接受治疗，则在特定随访时间内人群中事件发生的平均概率。当使用考克斯回归校正具有重要临床意义的基线中可能的不平衡时，可在前瞻性研究中得出治疗效果的绝对测量值协变量(C)2010年爱思唯尔公司All rights reserved.
Objective: Cox proportional hazards regression models are frequently used to determine the association between exposure and time-to-event outcomes in both randomized controlled trials and in observational cohort studies. The resultant hazard ratio is a relative measure of effect that provides limited clinical information.Study Design and Setting: A method is described for deriving absolute reductions in the risk of an event occurring within a given duration of follow-up time from a Cox regression model. The associated number needed to treat can be derived from this quantity. The method involves determining the probability of the outcome occurring within the specified duration of follow-up if each subject in the cohort was treated and if each subject was untreated, based oil the covariates in the regression model. These probabilities are then averaged across the study population to determine the average probability of the occurrence of an event within a specific duration of follow-up in the Population if all Subjects were treated and if all subjects were untreated.Results: Risk differences and numbers needed to treat.Conclusions: Absolute measures of treatment effect can be derived in prospective studies when Cox regression is used to adjust for possible imbalance in prognostically important baseline covariates. (C) 2010 Elsevier Inc. All rights reserved.