Tritchler D
Division of Epidemiology and Statistics, Ontario Cancer Institute, Toronto, Canada.
Biometrics. 1996 Dec;52(4):1450-6.
This paper considers randomized interventions which do not completely determine an intended determinant of response, and which may also manipulate additional, possibly unobserved, variables influencing response. The example we use throughout this paper is counseling for a low-fat diet for breast cancer prevention, where the intervention is counseling and dietary fat is hypothesized to reduce breast cancer risk. We use additive linear models to derive conditions and assumptions for considering fat to be the sole explanation of an observed treatment effect. A modified experimental design which supports stronger conclusions about causality is proposed.
本文考虑的随机干预措施并非能完全确定预期的反应决定因素，而且还可能会操纵其他可能未被观察到的影响反应的变量。我们在整篇论文中使用的例子是为预防乳腺癌而进行的低脂饮食咨询，其中干预措施是咨询，并且假设膳食脂肪可降低乳腺癌风险。我们使用加法线性模型来推导将脂肪视为观察到的治疗效果的唯一解释的条件和假设。本文提出了一种经过改进的实验设计，该设计能支持有关因果关系的更强有力的结论。
