A Bayesian approach to logistic regression models having measurement error following a mixture distribution.
A Bayesian approach to logistic regression models having measurement error following a mixture distribution.
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逻辑回归模型的贝叶斯方法，其测量误差遵循混合分布。
DOI：
10.1002/sim.4780121204
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发表时间：
1993
影响因子：
2
通讯作者：
Rosner,B
中科院分区：
文献类型：
--
作者：
Schmid,CH;Rosner,B
为了估计Logistic回归模型中的参数，当预测变量受到随机或系统测量误差的影响时，我们采用贝叶斯方法，并在给定其观测值的情况下，对预测变量真实值的条件后验分布上的真实Logistic概率进行平均。我们允许这种后验分布组成的混合物时，测量误差分布的变化形式与观察到的曝光。我们应用这种方法来研究饮酒对乳腺癌的风险，使用护士健康研究数据。我们估计测量误差从一个小的子样本，我们比较真实的报告消费。有些自称不喝酒的人真的不喝酒。由此产生的风险估计值与忽略测量误差的标准逻辑回归计算的风险估计值有很大差异。
To estimate the parameters in a logistic regression model when the predictors are subject to random or systematic measurement error, we take a Bayesian approach and average the true logistic probability over the conditional posterior distribution of the true value of the predictor given its observed value. We allow this posterior distribution to consist of a mixture when the measurement error distribution changes form with observed exposure. We apply the method to study the risk of alcohol consumption on breast cancer using the Nurses Health Study data. We estimate measurement error from a small subsample where we compare true with reported consumption. Some of the self‐reported non‐drinkers truly do not drink. The resulting risk estimates differ sharply from those computed by standard logistic regression that ignores measurement error.