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An alternative way of estimating a cumulative logistic model with complex survey data

机译：用复杂的调查数据估算累积物流模型的另一种方法

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摘要

When fitting an ordered categorical variable with L 2 levels to a set of covariates onto complex survey data, it is common to assume that the elements of the population fit a simple cumulative logistic regression model (proportional-odds logistic-regression model). This means the probability that the categorical variable is at or below some level is a binary logistic function of the model covariates. Moreover, except for the intercept, the values of the logistic-regression parameters are the same at each level. The conventional "design-based" method used for fitting the proportional-odds model is based on pseudo-maximum likelihood. We compare estimates computed using pseudo-maximum likelihood with those computed by assuming an alternative design-sensitive robust model-based framework. We show with a simple numerical example how estimates using the two approaches can differ. The alternative approach is easily extended to fit a general cumulative logistic model, in which the parallel-lines assumption can fail. A test of that assumption easily follows.
机译：当将L> 2级的有序分类变量与一组协变量拟合到复杂的调查数据时，通常会假设总体元素适合简单的累积对数回归模型（比例对数回归模型）。这意味着分类变量等于或低于某个水平的概率是模型协变量的二进制逻辑函数。此外，除了截距外，逻辑回归参数的值在每个级别上都相同。用于拟合比例奇数模型的常规“基于设计”方法基于伪最大似然。我们将使用伪最大似然法计算出的估计值与通过假设基于替代设计敏感的鲁棒模型的框架所计算出的估计值进行比较。我们用一个简单的数值示例来说明使用两种方法的估计值如何不同。可以轻松扩展该替代方法以适合一般的累积逻辑模型，在该模型中，平行线假设可能会失败。很容易对该假设进行检验。

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