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轻度每日饮酒与寿命观察性研究中证据因子的新构造AI 解读

Does light daily alcohol consumption lengthen life? In any observational study of this question, we expect drinking behavior—that is, treatment assignment—to be confounded by measured and unmeasured covariates. An observational study has two evidence factors if there are two essentially independent tests of the same null hypothesis about causal effects, where the two tests are susceptible to different unmeasured biases in treatment assignment. Because they are independent,…

观察性研究中的效应混杂AI 解读

In experimental design, aliasing of effects occurs in fractional factorial experiments, where certain low order factorial effects are indistinguishable from certain high order interactions: low order contrast weights may be orthogonal to one another, while their higher order interactions are aliased and not identified. In observational studies, aliasing occurs when certain combinations of covariates—for example, time period and various eligibility criteria for…

观察性区组设计的Bahadur效率AI 解读

To be convincing, an observational or nonrandomized study of causal effects must demonstrate that its conclusions cannot be readily explained by a small unmeasured bias in the way individuals were assigned to treatment or control. The Bahadur relative efficiency of a sensitivity analysis compares the performance of different test statistics or different research designs when sensitivity to unmeasured bias is appraised: better statistics and better designs exhibit…

多假设观察性研究中的交叉筛选AI 解读

We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that needs to be defined carefully, hidden within a sensitivity analysis for nonrandom assignment is an enormous correction for…

探索效应修饰的观察性研究中族错误率的强控制AI 解读

An effect modifier is a pretreatment covariate that affects the magnitude of the treatment effect or its stability. When there is effect modification, an overall test that ignores an effect modifier may be more sensitive to unmeasured bias than a test that combines results from subgroups defined by the effect modifier. If there is effect modification, one would like to identify specific subgroups for which there is evidence of effect that is insensitive to small or moderate…

新外科医生健康结局观察研究中的精细协变量平衡与大规模稀疏最优匹配AI 解读

Every newly trained surgeon performs her first unsupervised operation. How do the health outcomes of her patients compare with the patients of experienced surgeons? Using data from 498 hospitals, we compare 1252 pairs comprised of a new surgeon and an experienced surgeon working at the same hospital. We introduce a new form of matching that matches patients of each new surgeon to patients of an otherwise similar experienced surgeon at the same hospital, perfectly balancing…

多对照匹配观察研究中具有更高设计敏感度的加权 M 统计量AI 解读

In a nonrandomized or observational study, a weak association between receipt of the treatment and an outcome may be explained not as effects caused by the treatment but rather by a small bias in the assignment of individuals to treatment or control; however, a strong association may be explained as noncausal only by a large bias. The strength of the association between treatment and outcome is not uniform across the data from a study, and this motivates giving greater…

观察研究中的整群处理分配与未观测偏差敏感性AI 解读

Clustered treatment assignment occurs when individuals are grouped into clusters prior to treatment and whole clusters, not individuals, are assigned to treatment or control. In randomized trials, clustered assignments may be required because the treatment must be applied to all children in a classroom, or to all patients at a clinic, or to all radio listeners in the same media market. The most common cluster randomized design pairs 2S clusters into S pairs based on similar…

病例定义与设计敏感度AI 解读

In a case-referent study, cases of disease are compared to non-cases with respect to their antecedent exposure to a treatment in an effort to determine whether exposure causes some cases of the disease. Because exposure is not randomly assigned in the population, as it would be if the population were a vast randomized trial, exposed and unexposed subjects may differ prior to exposure with respect to covariates that may or may not have been measured. After controlling for…

观察性研究中的效应修饰与设计敏感性AI 解读

In an observational study of treatment effects, subjects are not randomly assigned to treatment or control, so differing outcomes in treated and control groups may reflect a bias from nonrandom assignment rather than a treatment effect. After adjusting for measured pretreatment covariates, perhaps by matching, a sensitivity analysis determines the magnitude of bias from an unmeasured covariate that would need to be present to alter the conclusions of the naive analysis that…

在早产儿围产期护理观察研究中构建更强工具变量AI 解读

An instrument is a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment. Nonetheless, in settings in which treatment assignment is mostly deliberate and not random, there may exist some essentially random nudges to accept treatment, so that use of an instrument might extract bits of random treatment assignment from a setting that is otherwise quite biased in its treatment assignments. An instrument…

观察性研究中的设计敏感度与效率AI 解读

An observational study attempts to draw inferences about the effects caused by a treatment when subjects are not randomly assigned to treatment or control as they would be in a randomized trial. After adjustments have been made for imbalances in measured covariates, the key source of uncertainty in an observational study is the possibility that subjects were not comparable prior to treatment in terms of some unmeasured covariate, so that differing outcomes in treated and…

匹配观察研究中敏感性分析的扩维解释AI 解读

A sensitivity analysis displays the increase in uncertainty that attends an inference when a key assumption is relaxed. In matched observational studies of treatment effects, a key assumption in some analyses is that subjects matched for observed covariates are comparable, and this assumption is relaxed by positing a relevant covariate that was not observed and not controlled by matching. What properties would such an unobserved covariate need to have to materially alter the…

新生儿重症监护观察研究中等效与差异检验的敏感性分析AI 解读

In randomized experiments, it is sometimes important to demonstrate that two treatments do not differ greatly in their effects, or to demonstrate that the treatments have very different effects on one outcome but similar effects on another outcome. These “demonstrations” may take the form of rejecting a null hypothesis of inequivalence in an equivalence test, or rejecting a null hypothesis of equal and inequivalent effects on two outcomes. The procedures often express a…

敏感性分析中的渐近可分性AI 解读

Summary In an observational study in which each treated subject is matched to several untreated controls by using observed pretreatment covariates, a sensitivity analysis asks how hidden biases due to unobserved covariates might alter the conclusions. The bounds required for a sensitivity analysis are the solution to an optimization problem. In general, this optimization problem is not separable, in the sense that one cannot find the needed optimum by performing a separate…

非随机样本与观察性研究中的分位数AI 解读

In a nonrandom sample or a nonrandomized comparison of treated and control groups, methods are developed for displaying the sensitivity of confidence intervals for population quantiles to the magnitude of the departure from random selection. Quantiles of a single distribution and for a shift in two distributions are both discussed. These confidence intervals are based on a new inequality for the distribution of the number of successes in a nonrandom sample or nonrandomized…

非随机样本与观察性研究中的分位数AI 解读

In a nonrandom sample or a nonrandomized comparison of treated and control groups, methods are developed for displaying the sensitivity of confidence intervals for population quantiles to the magnitude of the departure from random selection. Quantiles of a single distribution and for a shift in two distributions are both discussed. These confidence intervals are based on a new inequality for the distribution of the number of successes in a nonrandom sample or nonrandomized…

观察性研究中处理效应的Hodges–Lehmann点估计AI 解读

A Hodges-Lehmann point estimate of an additive treatment effect is a robust estimate derived from the randomization distribution of a rank test. This article shows how to carry out a sensitivity analysis for such an estimate in an observational study where treatments are not randomly assigned. Two cases are discussed in detail: matched pairs and the comparison of two unmatched groups. The method uses a model for the distribution of treatment assignments when hidden biases…

观察研究中治疗效应的 Hodges–Lehmann 点估计AI 解读

A Hodges-Lehmann point estimate of an additive treatment effect is a robust estimate derived from the randomization distribution of a rank test. This article shows how to carry out a sensitivity analysis for such an estimate in an observational study where treatments are not randomly assigned. Two cases are discussed in detail: matched pairs and the comparison of two unmatched groups. The method uses a model for the distribution of treatment assignments when hidden biases…

观察性研究中双样本置换推断的敏感性AI 解读

In observational studies, subjects are not randomly assigned to treatment or control, so they may differ in their chances of receiving the treatment. A simple method is developed and demonstrated for displaying the sensitivity of conventional two-group permutation inferences to departures from random assignment of treatments. The unmatched case, discussed here, differs in certain technical and computational details from the matched case, discussed previously; however, the…

观察性研究中两样本置换推断的敏感性AI 解读

In observational studies, subjects are not randomly assigned to treatment or control, so they may differ in their chances of receiving the treatment. A simple method is developed and demonstrated for displaying the sensitivity of conventional two-group permutation inferences to departures from random assignment of treatments. The unmatched case, discussed here, differs in certain technical and computational details from the matched case, discussed previously; however, the…

观察性研究中隐蔽偏差的置换检验：Holley 不等式与 Savage 格AI 解读

Randomized experiments and observational studies both attempt to estimate the effects produced by a treatment, but in observational studies, subjects are not randomly assigned to treatment or control. A theory of observational studies would closely resemble the theory for randomized experiments in all but one critical respect: In observational studies, the distribution of treatment assignments is not known. The problems that are special to observational studies revolve…

多控制匹配的敏感性分析AI 解读

In observational studies, treated and control subjects may difler in ways that have not been observed, so that reference distributions for statistics that would be appropriate in randomized experiments may lead to incorrect inferences. A previous paper (Rosenbaum, 1987a) proposed a method for displaying the sensitivity of permutation inferences to bias due to an unobserved covariate in two cases: (i) the case of matched pairs, with arbitrary response variables, and (ii) the…

多个对照匹配的敏感性分析AI 解读

In observational studies, treated and control subjects may difler in ways that have not been observed, so that reference distributions for statistics that would be appropriate in randomized experiments may lead to incorrect inferences. A previous paper (Rosenbaum, 1987a) proposed a method for displaying the sensitivity of permutation inferences to bias due to an unobserved covariate in two cases: (i) the case of matched pairs, with arbitrary response variables, and (ii) the…