Regression artifacts in time-series and longitudinal data
Regression artifacts in time-series and longitudinal data
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DOI：
10.1016/s0149-7189(96)00025-0
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发表时间：
1996-11-01
影响因子：
1.6
通讯作者：
Campbell, DT
中科院分区：
文献类型：
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作者：
Campbell, DT
因果推理中的“回归伪影”问题，也称为用于匹配或回归调整的“独立"变量中的en或可靠不相关方差问题，首先在时间序列数据中说明，其中处理由极端措施触发。心理治疗中的“抵消效应”，安德伍德的扇形学习曲线，以及艾滋病治疗中潜在的伪效应都被用作例证。如果各种滞后的自相关模式是已知的，则这种伪影的幅度是可计算的，并且因此可以与真正的效应区分开。对于纵向研究中，人口的受访者被反复测量，锚定的匹配或回归调整的问题，在一个单一的波的测量（通常是第一个）被说明为受近端自相关性质的措施。从一个著名的研究工作培训的影响的数据重新解释在这方面的考虑。版权所有（C）1996 Elsevier Science Ltd.
The problem of ''regression artifacts'' in causal inference, otherwise known as the problem of en or and reliable irrelevant variance in ''independent'' variables used for matching or regression adjustment, is illustrated first in the time-series data where a treatment is triggered by an extreme measure. The ''offset effect'' in psychotherapy, Underwood's scalloped learning curve, and potential pseudo-effects in AIDS therapies are used as illustrations. The magnitude of such artifacts is computable if the autocorrelation pattern for various lags is known, and thus could be distinguished from genuine effects. For longitudinal studies in which a population of respondents is repeatedly measured, the problem of anchoring the matching or regression adjustments on a single wave of measurement (usually the first) is illustrated as affected by the proximally autocorrelated nature of such measures. Data from a famous study of the effects of job training are reinterpreted in light of this consideration. Copyright (C) 1996 Elsevier Science Ltd.