McClelland Robyn L, Kronmal Richard A, Haessler Jeffrey, Blumenthal Roger S, Goff David C
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Stat Med. 2008 Oct 30;27(24):5039-53. doi: 10.1002/sim.3341.
When the outcome of interest is a quantity whose value may be altered through the use of medications, estimation of associations with this outcome is a challenging statistical problem. For participants taking medication the treated value is observed, but the underlying 'untreated' value may be the measure that is truly of interest. Problematically, those with the highest untreated values may have some of the lowest observed measurements due to the effectiveness of medications. In this paper we propose an approach in which we parametrically estimate the underlying untreated variable of interest as a function of the observed treated value, and dose and type of medication. Multiple imputation is used to incorporate the variability induced by the estimation. We show that this approach yields more realistic parameter estimates than other more traditional approaches to the problem and that study conclusions may be altered in a meaningful way by using the imputed values.
当感兴趣的结果是一个其值可通过药物使用而改变的量时，估计与该结果的关联是一个具有挑战性的统计问题。对于正在服用药物的参与者，观察到的是治疗后的值，但潜在的“未治疗”值可能才是真正感兴趣的测量值。问题在于，由于药物的有效性，那些未治疗值最高的人可能具有一些最低的观察测量值。在本文中，我们提出了一种方法，即通过参数估计将感兴趣的潜在未治疗变量作为观察到的治疗值、药物剂量和类型的函数。使用多重填补来纳入估计所引起的变异性。我们表明，与解决该问题的其他更传统方法相比，这种方法能产生更现实的参数估计，并且使用填补值可能会以有意义的方式改变研究结论。
