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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…

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匹配观察研究中若干置换推断的敏感性分析AI 解读

In observational studies, treatments are not randomly assigned to experimental units, so that randomization tests and their associated interval estimates are not generally applicable. In an effort to compensate for the lack of randomization, treated and control units are often matched on the basis of observed covariates; however, the possibility remains of bias due to residual imbalances in unobserved covariates. A general though simple method is proposed for displaying the…

匹配观察性研究中部分置换推断的敏感性分析AI 解读

In observational studies, treatments are not randomly assigned to experimental units, so that randomization tests and their associated interval estimates are not generally applicable. In an effort to compensate for the lack of randomization, treated and control units are often matched on the basis of observed covariates; however, the possibility remains of bias due to residual imbalances in unobserved covariates. A general though simple method is proposed for displaying the…

单调潜变量模型中的条件关联与单维性AI 解读

Latent variable models represent the joint distribution of observable variables in terms of a simple structure involving unobserved or latent variables, usually assuming the conditional independence of the observable variables given the latent variables. These models play an important role in educational measurement and psychometrics, in sociology and in population genetics, and are implicit in some work on systems reliability. We study a broad class of latent variable…

观察性研究中的条件置换检验与倾向得分AI 解读

In observational studies, the distribution of treatment assignments is unknown, and therefore randomization tests are not generally applicable. However, permutation tests that condition on sample information about the treatment assignment mechanism can be applicable in observational studies, provided treatment assignment is strongly ignorable. These tests use the conditional distribution of the treatment assignments given a sufficient statistic for the unknown parameter of…

观察研究从关联到因果：强可忽略处理分配检验的作用AI 解读

If treatment assignment is strongly ignorable, then adjustment for observed covariates is sufficient to produce consistent estimates of treatment effects in observational studies. A general approach to testing this critical assumption is developed and applied to a study of the effects of nuclear fallout on the risk of childhood leukemia. R.A. Fisher's advice on the interpretation of observational studies was “Make your theories elaborate”; formally, make causal theories…

倾向得分在观察性因果效应研究中的核心作用AI 解读

The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity…