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可忽略性、充分性与辅助性AI 解读

Summary This paper explores the relationship between ignorability, sufficiency and ancillarity in the coarse data model of Heitjan and Rubin. Bayes or likelihood ignorability has a natural relationship to sufficiency, and frequentist ignorability an analogous relationship to ancillarity. Weaker conditions, termed observed likelihood sufficiency, observed specific sufficiency and observed ancillarity, expand the concepts to models where the coarsening mechanism is sometimes,…

保存肿瘤标本取样方案的贝叶斯分析AI 解读

Intratumor DNA heterogeneity—as assessed by flow-cytometric cellular DNA analysis—is predictive of prognosis in many types of cancer. But some truly heterogeneous tumors apparently have a substantial chance of being misclassified as normal. One way to enhance the assay's sensitivity is to test multiple samples from each tumor specimen. This article describes an analysis of data from a study of endometrial cancer, with a view to establishing the number of samples needed to…

保存肿瘤标本取样方案的贝叶斯分析AI 解读

Intratumor DNA heterogeneity—as assessed by flow-cytometric cellular DNA analysis—is predictive of prognosis in many types of cancer. But some truly heterogeneous tumors apparently have a substantial chance of being misclassified as normal. One way to enhance the assay's sensitivity is to test multiple samples from each tumor specimen. This article describes an analysis of data from a study of endometrial cancer, with a view to establishing the number of samples needed to…

一般不完全数据模型中的可忽略性AI 解读

Rubin (1976) defined ignorability conditions for frequentist and Bayes/likelihood analyses of data subject to missing observations. More recently, Heitjan & Rubin (1991) and Heitjan (1993) generalised the Rubin model to encompass other forms of incompleteness, establishing ignorability conditions for Bayes/likelihood inferences only. This paper extends the Heitjan-Rubin model by explicitly defining the observed degree of coarseness as a data element. This permits the…

用多重插补评估血压的长期趋势AI 解读

The National Center for Health Statistics makes available data from three national health evaluation surveys that it has conducted since 1960: NHES I (1960-1962), NHANES I (1971-1975), and NHANES II (1976-1980). There has been considerable interest in using these data to assess secular trends in cardiovascular risk factors such as blood pressure (BP). Unfortunately, underlying trends in BP are confounded with trends in physician treatment of hypertension over the same…

评估血压长期趋势：多重插补方法AI 解读

The National Center for Health Statistics makes available data from three national health evaluation surveys that it has conducted since 1960: NHES I (1960-1962), NHANES I (1971-1975), and NHANES II (1976-1980). There has been considerable interest in using these data to assess secular trends in cardiovascular risk factors such as blood pressure (BP). Unfortunately, underlying trends in BP are confounded with trends in physician treatment of hypertension over the same…

一般不完整数据模型中的可忽略性AI 解读

Rubin (1976) defined ignorability conditions for frequentist and Bayes/likelihood analyses of data subject to missing observations. More recently, Heitjan & Rubin (1991) and Heitjan (1993) generalised the Rubin model to encompass other forms of incompleteness, establishing ignorability conditions for Bayes/likelihood inferences only. This paper extends the Heitjan-Rubin model by explicitly defining the observed degree of coarseness as a data element. This permits the…

可忽略性与粗化数据AI 解读

We present a general statistical model for data coarsening, which includes as special cases rounded, heaped, censored, partially categorized and missing data. Formally, with coarse data, observations are made not in the sample space of the random variable of interest, but rather in its power set. Grouping is a special case in which the degree of coarsening is known and nonstochastic. We establish simple conditions under which the possible stochastic nature of the coarsening…

通过多重插补对粗化数据推断：年龄堆积的应用AI 解读

Multiple imputation is applied to a demographic data set with coarse age measurements for Tanzanian children. The heaped ages are multiply imputed with plausible true ages using (a) a simple naive model and (b) a new, relatively complex model that relates true age to the observed values of heaped age, sex, and anthropometric variables. The imputed true ages are used to create valid inferences under the models and compare inferences across models, thereby revealing…