When is baseline adjustment useful in analyses of change? An example with education and cognitive change
When is baseline adjustment useful in analyses of change? An example with education and cognitive change
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DOI：
10.1093/aje/kwi187
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发表时间：
2005-08-01
影响因子：
5
通讯作者：
Robins, JM
中科院分区：
文献类型：
--
作者：
Glymour, MM;Weuve, J;Robins, JM
在研究健康状况变化的决定因素时，一个关键的分析决策是是否调整基线健康状况。在本文中，作者研究了基线调整的后果，并以教育程度对老年认知功能变化的影响为例。根据美国资产和健康动态调查（n = 5，726;出生于1924年之前）的数据，他们表明，与没有基线调整的模型相比，基线认知测试分数的调整大大增加了学校教育对认知测试分数变化影响的回归系数估计。为了解释这一发现，他们考虑了各种关于变量之间关系的合理假设。每一组假设都用因果图表示。作者应用简单的规则来评估因果图，以证明在许多看似合理的情况下，基线调整会导致教育和认知评分变化之间的虚假统计关联。更一般地说，当接触与基线健康状况有关时，如果健康状况在基线评估之前发生变化，或者因变量测量不可靠或不稳定，则可能出现这种偏倚。在某些情况下，当基线调整后的估计值存在偏倚时，不进行基线调整的变化评分分析可提供无偏倚的因果效应估计值。
In research on the determinants of change in health status, a crucial analytic decision is whether to adjust for baseline health status. In this paper, the authors examine the consequences of baseline adjustment, using for illustration the question of the effect of educational attainment on change in cognitive function in old age. With data from the US-based Assets and Health Dynamics Among the Oldest Old survey (n = 5,726; born before 1924), they show that adjustment for baseline cognitive test score substantially inflates regression coefficient estimates for the effect of schooling on change in cognitive test scores compared with models without baseline adjustment. To explain this finding, they consider various plausible assumptions about relations among variables. Each set of assumptions is represented by a causal diagram. The authors apply simple rules for assessing causal diagrams to demonstrate that, in many plausible situations, baseline adjustment induces a spurious statistical association between education and change in cognitive score. More generally, when exposures are associated with baseline health status, this bias can arise if change in health status preceded baseline assessment or if the dependent variable measurement is unreliable or unstable. In some cases, change-score analyses without baseline adjustment provide unbiased causal effect estimates when baseline-adjusted estimates are biased.