Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure
Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure
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DOI：
10.1002/sim.2165
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发表时间：
2005-10-15
影响因子：
2
通讯作者：
Burton, PR
中科院分区：
文献类型：
--
作者：
Tobin, MD;Sheehan, NA;Burton, PR
当数量性状受到治疗的影响时，基于人群的研究可能会受到严重影响。例如，在一项典型的定量血压 (BP) 研究中，15% 或更多的中年受试者可能会接受抗高血压治疗。如果不进行适当的修正，这可能会导致科学兴趣的病因决定因素的估计效果大幅缩减，并且统计功效显着降低。校正依赖于在治疗受试者中根据观察到的血压对潜在血压进行插补，并调用了关于生物临床环境的一项或多项假设。可以做出一系列不同的假设，并且可以使用许多不同的分析模型。在本文中，我们提出了一种基于审查正态回归模型的方法，并将其与当前使用或提倡的一系列其他方法进行比较。我们在模拟数据集中比较这些方法，并评估当治疗效果未得到适当解决时所产生的估计偏差和功效损失。我们还将相同的方法应用于实际数据，并展示了与模拟研究中一致的行为模式。尽管所有分析方法都必然是近似值，但我们得出的结论是，其中两种调整方法似乎在一系列实际设置中表现良好。这些是：（1）在治疗受试者中观察到的血压上添加一个合理的常数； (2)删失正态回归模型。在某些情况下，还可以提倡基于平均有序残差的第三种非参数方法。另一方面，三种相对常用的方法存在根本缺陷，根本不应该使用。这些是：（i）完全忽略该问题并分析治疗受试者中观察到的血压，就好像它是潜在的血压一样； (ii) 将常规回归模型作为二元协变量进行处理； (iii) 从分析中排除接受治疗的受试者。鉴于更有效的方法易于实施，因此没有理由进行有缺陷的分析，浪费精力并导致过度偏差。版权所有 (C) 2005 John Wiley & Sons, Ltd.
A population-based study of a quantitative trait may be seriously compromised when the trait is subject to the effects of a treatment. For example, in a typical study of quantitative blood pressure (BP) 15 per cent or more of middle-aged subjects may take antihypertensive treatment. Without appropriate correction, this can lead to substantial shrinkage in the estimated effect of aetiological determinants of scientific interest and a marked reduction in statistical power. Correction relies upon imputation, in treated subjects, of the underlying BP from the observed BP having invoked one or more assumptions about the bioclinical setting. There is a range of different assumptions that may be made, and a number of different analytical models that may be used. In this paper, we motivate an approach based on a censored normal regression model and compare it with a range of other methods that are currently used or advocated. We compare these methods in simulated data sets and assess the estimation bias and the loss of power that ensue when treatment effects are not appropriately addressed. We also apply the same methods to real data and demonstrate a pattern of behaviour that is consistent with that in the simulation studies. Although all approaches to analysis are necessarily approximations, we conclude that two of the adjustment methods appear to perform well across a range of realistic settings. These are: (1) the addition of a sensible constant to the observed BP in treated subjects; and (2) the censored normal regression model. A third, non-parametric, method based on averaging ordered residuals may also be advocated in some settings. On the other hand, three approaches that are used relatively commonly are fundamentally flawed and should not be used at all. These are: (i) ignoring the problem altogether and analysing observed BP in treated subjects as if it was underlying BP; (ii) fitting a conventional regression model with treatment as a binary covariate; and (iii) excluding treated subjects from the analysis. Given that the more effective methods are straightforward to implement, there is no argument for undertaking a flawed analysis that wastes power and results in excessive bias. Copyright (C) 2005 John Wiley & Sons, Ltd.