Statistical Methods for Improving Causal Analyses

我们正在努力开发统计方法来帮助医学研究人员寻找疾病的原因。许多医学研究都涉及到从人们那里收集数据，并使用统计模型来告诉我们哪些因素会导致后来的健康。例如，我们可能会问一群人他们在儿童、青少年和成年时的饮食，并用这个来试图告诉我们，不良的饮食是否会在以后的生活中导致癌症。这些研究有几个问题，使我们很难得出结论。其中之一是，同意参加研究的人与不同意的人通常是不同的。另一个原因是，随着时间的推移，人们往往会退出一项研究--同样，退出的人往往与留下来的人不同。第三个问题是，人在一生中都会发生变化--我们可能想知道疾病的原因是在出生时发生的，还是在童年期间发生的，或者即使是在成年人身上，是否有机会预防这种疾病。如果这些问题中的任何一个出现，统计模型可能不会给出正确的答案--这可能意味着给出了错误的健康建议，或者开发了错误的治疗方法。我们的目标是开发能够克服这些现实问题的方法，并帮助医学研究人员对他们关于疾病原因的结论更有信心。

We are trying to develop statistical methods to help medical researchers in their search for causes of disease. A lot of medical research involves gathering data from people and using statistical models to tell us which factors cause later health. For example, we might ask a group of people about their diet as children, as teenagers and as adults, and use this to try to tell us whether poor diet causes cancer in later life.There are several problems with these studies that make it hard to draw conclusions. One is that people agreeing to be in a study are often different from people who don’t agree. Another is that people tend to drop out of a study over time – and again the people who drop out are often different from the people who stay. A third problem is that people change as they go through life – and we might want to know whether the cause of a disease happens at birth, or during childhood, or whether there are chances to prevent the disease even in adults. Statistical models may not give the right answers if any of these problems occur – and this could mean that the wrong health advice is given, or the wrong treatments developed. We aim to develop methods that can overcome these real-life problems, and help medical researchers to be more confident in their conclusions about causes of disease.