Some old and some new statistical tools for outcomes research.
Some old and some new statistical tools for outcomes research.
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DOI：
10.1161/circulationaha.108.766907
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发表时间：
2008-08-19
期刊：
影响因子：
37.8
通讯作者：
Normand SL
中科院分区：
文献类型：
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作者：
Normand SL
急性心肌梗塞 (AMI) 后的治疗可提高生存率吗？因果推理侧重于特定个体在不同治疗方案下会发生什么。相反，预测推理侧重于比较接受不同治疗的个体组之间的结果。因果推断可以被认为是预测推断的一种特殊情况，其中可以识别可能接受任一治疗的受试者并用于推断治疗效果（例如，如果患者接受与观察到的治疗不同的治疗，患者的生存会发生什么？）。随机临床试验10的具体特征使研究人员能够得出治疗或干预措施是否有效的结论。首先，实验者使用已知的机制确定对患者的治疗分配。该机制是由实验者确定并用标准软件实现的随机分配概率。治疗分配可以对应于所有试验患者的治疗组之间的平等分配或重要患者组（例如糖尿病患者和非糖尿病患者）内的治疗组之间的平等分配。分配概率可以是固定的，也可以是随着研究进展而变化的自适应程序11。随机分配的结果（例如，受试者被分配到治疗 A）有几个关键属性，其中 2 个包括（1）它可以预测所采取的治疗，因此，如果我们将接受治疗的概率作为治疗分配的函数进行建模，则治疗分配变量的优势比会很大，（2）如果我们考虑到所接受的治疗，则治疗分配与结果无关。这意味着是接受的治疗导致患者结果发生变化，而不是分配的治疗。具有这些属性的变量也称为“工具变量”。 12, 13 随机试验的第二个特征是一个令人惊讶的简单事实：每个符合研究纳入标准的受试者都有机会接受治疗。这意味着试验参与者接受研究治疗的概率始终大于零。这是由于为定义目标人群而制定的研究纳入/排除标准以及对接受研究治疗的对象的实验控制。这个看似微不足道的点在观察性研究中经常被忽视。第三个特征是，理论上不存在与治疗分配和结果相关的未测量或测量变量（称为混杂因素）。从统计学上讲，这意味着“潜在”结果和治疗分配在给定患者协变量的情况下是独立的。这意味着，由于参与者被随机分配到治疗组，并且每个参与者都有机会接受治疗，因此治疗组之间的唯一区别是治疗分配。治疗效果的标准估计是意向治疗估计，其中从分配到比较治疗的平均结果中减去分配到治疗的平均结果。意向治疗估计仅在完全治疗依从性且无缺失数据的假设下才有效，14 并且很少有研究符合这些标准。随机研究还有其他重要特征，例如盲法，这里不讨论。
ment increase survival after acute myocardial infarction (AMI)? Causal inference focuses on what would happen to a specific individual under different treatment options. In contrast, predictive inference focuses on the comparison of outcomes between groups of individuals who have received different treatments. Causal inference can be thought of as a special case of predictive inference in which subjects who could have received either treatment are identified and used to infer treatment effects (eg, what would have happened to a patient’s survival had the patient received a different treatment than the one observed?). Specific features of a randomized clinical trial10 permit researchers to conclude whether a treatment or intervention is efficacious. First, the experimenter determines the assignment of treatments to patients using a known mechanism. This mechanism is the randomization allocation probability determined by the experimenter and implemented with standard software. Treatment allocation may correspond to equal allocation between treatment arms for all trial patients or equal allocation between treatment arms within important patient groups, such as diabetic and nondiabetic patients. Allocation probabilities can be fixed, or they can be adaptive procedures11 that change as the study progresses. The outcome of the random assignment (eg, subject is assigned to treatment A) has several key properties, 2 of which include (1) that it is predictive of treatment taken, so that if we were to model the probability of treatment received as a function of treatment assignment, the odds ratio of the treatment assignment variable would be large, and (2) that treatment assignment is not related to outcome if we take into account the treatment received. This implies that it is the treatment received that causes a change in patient outcome and not the treatment assigned. A variable with these properties is also referred to as an “instrumental variable.” 12, 13 A second characteristic of a randomized trial is a surprisingly simple fact: Every subject who meets the study inclusion criteria has a chance of receiving the treatment. This implies that the probability that a trial participant receives the study treatment is always greater than zero. This is due both to study inclusion/exclusion criteria that are developed to define the target population and to experimental control over who receives the study treatment. This seemingly trivial point is often ignored in observational studies. The third feature is that, in theory, no unmeasured or measured variables (denoted confounders) are present that relate to both treatment assignment and outcome. Statistically, this implies that the “potential” outcome and treatment assignment are independent given the patient covariates. This means that because participants have been allocated randomly to treatment groups, and each participant had a chance of receiving treatment, the only difference between the treatment groups is treatment assignment. The standard estimate of the treatment effect is the intention-to-treat estimate, in which the average outcome of those assigned to treatment is subtracted from the average outcome of those assigned to the comparison treatment. The intention-to-treat estimate is only valid under the assumption of full treatment compliance and no missing data, 14 and very few studies meet these criteria. Randomized studies have additional important features, such as blinding, which will not be discussed here.