A COMPARISON OF DIRECT ADJUSTMENT AND REGRESSION ADJUSTMENT OF EPIDEMIOLOGIC MEASURES
A COMPARISON OF DIRECT ADJUSTMENT AND REGRESSION ADJUSTMENT OF EPIDEMIOLOGIC MEASURES
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DOI：
10.1016/0021-9681(85)90109-2
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发表时间：
1985-01-01
期刊：
影响因子：
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通讯作者：
CHAMBLESS, LE
中科院分区：
文献类型：
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作者：
WILCOSKY, TC;CHAMBLESS, LE
虽然回归调整可以提供一种有用的替代直接调整，特别是当数据稀疏时，许多研究人员没有意识到调整后的汇总度量可以很容易地从回归系数中得到。通过非技术性的实例讨论，比较了直接平差方法与基于协方差模型分析的三种回归平差方法：条件预测法、分层预测法和边际预测法。分层预测和直接调整方法都产生了特定层测度的加权平均值的汇总测度，而条件预测方法的调整测度类似于特定层估计。与其他可以使用内部或外部权重的调整方法相比，边际预测方法总是给出一个内部调整的度量。在一定条件下，三种回归平差方法产生相同的结果。直接调整的主要优点包括计算简单和相对较少的统计假设。然而，回归调整对于相互作用和群体差异的统计检验更方便，并且通常排除了对连续变量进行分类的需要，从而避免了空层的问题。
Although regression adjustment can provide a useful alternative to direct adjustment, especially when data are sparse, many researchers are unaware that adjusted summary measures can be easily derived from regression coefficients. In a nontechnical discussion with examples, the direct adjustment procedure is compared with three methods of regression adjustment based on analysis of covariance models: the conditional prediction method, the stratified prediction method, and the marginal prediction method. Both the stratified prediction and direct adjustment methods yield summary measures that are weighted averages of stratum-specific measures, while adjusted measures from the conditional prediction method are similar to stratum-specific estimates. In contrast to the other adjustment procedures, which can use internal or external weights, the marginal prediction method always gives an internally adjusted measure. Under certain conditions, the three regression adjustment procedures produce identical results. Major advantages of direct adjustment include computational simplicity and relatively few statistical assumptions. Regression adjustment, however, is more convenient for statistical tests for interactions and group differences, and often precludes the need to categorize continuous variables, so that problems with empty strata are avoided.