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毒性严重程度数据中的未观察到不良反应剂量水平AI 解读

Toxicity data are often categorized by severity of response and dose level with the assumption that there is a tolerated dose below which there is no toxicity. For data from a controlled experiment, the largest observed dose at or below the tolerated dose is called the no-observed-adverse-effect level (NOAEL). The problem of identifying the NOAEL can be viewed statistically as estimating the maximal observed dose for which there is no increased severity or frequency of toxic…

毒性严重程度数据中的无可观察不良效应水平AI 解读

Toxicity data are often categorized by severity of response and dose level with the assumption that there is a tolerated dose below which there is no toxicity. For data from a controlled experiment, the largest observed dose at or below the tolerated dose is called the no-observed-adverse-effect level (NOAEL). The problem of identifying the NOAEL can be viewed statistically as estimating the maximal observed dose for which there is no increased severity or frequency of toxic…

用于 K 个 2×J 列联表的投影法 Mantel–Haenszel 估计量AI 解读

A projection method is introduced to generalize the Mantel-Haenszel estimator for estimating the common odds ratios in K 2 × J (J > 2) tables. The method provides estimators in an explicit form and needs no iterative computation. It is shown that the generalized Mantel-Haenszel estimator proposed by Greenland, and Yanagawa and Fujii is obtained by this method as a special case. Furthermore, selecting appropriate weight, it is shown that the method leads to an estimator that…

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采用广义Mantel–Haenszel估计量的列联表齐性检验AI 解读

A conditional test for the homogeneity of the odds ratios in L2 × K (K > 2) tables is developed based on a multiple hypergeometric distribution. The test is a generalization of the Breslow-Day test (1980) and is useful in sparse tables. For computational feasibility, the conditional maximum likelihood estimator is replaced by a consistent but inefficient estimator in the test statistic. If this is the case, it is shown that the asymptotic distribution is not chi squared, as…

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