Statistical Methods for Improving Causal Analyses

我们正在努力发展统计方法，以帮助医学研究人员寻找疾病的原因。许多医学研究涉及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，我们可能会询问一组人在儿童、青少年和成年时的饮食情况，并试图以此来告诉我们不良的饮食是否会导致晚年的癌症。这些研究存在一些问题，很难得出结论。一个是同意参加研究的人通常与不同意的人不同。另一个原因是，随着时间的推移，人们往往会退出研究--而且退出的人往往与留下来的人不同。第三个问题是，人们在一生中会发生变化--我们可能想知道疾病的原因是在出生时发生的，还是在儿童时期发生的，或者即使在成年人中也有机会预防疾病。如果出现任何这些问题，统计模型可能无法给出正确的答案-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加自信。

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.