New statistics for the self-controlled case series method: weakening the assumptions

在许多领域中，一个重要的统计问题是确定一个称为暴露的变量是否影响感兴趣事件的发生。例如，给药是否会导致不良反应；某些生产模式是否会增加制造过程中出错的机会；一种生活事件的发生是否会影响另一种生活事件的发生。已经开发了各种统计方法来识别这样的关联，并估计它们的强度。这个项目涉及最近出现的一种特殊方法，即案例序列方法。这种方法与其他方法的不同之处在于，只需要对经历过感兴趣事件的个人(或物品)进行抽样。因此，没有经历过这一事件的个人是不需要的。当感兴趣的事件很少发生时，这是有利的。在每个人的观察期内进行比较，并自动考虑许多可能扭曲接触和事件之间的联系的因素，即混杂因素，从而获得额外的好处。这种双重好处--减少研究规模和很好地控制混杂因素--是有代价的，即需要比其他方法所要求的更强的假设。本项目的目的是研究这些假设，以便更好地了解它们的作用，如果可能的话，削弱它们，从而扩大该方法的应用范围。该项目很重要，因为病例系列方法正越来越多地被用于一系列领域，特别是在流行病学中。这是因为它相对便宜，并使原本不切实际或有偏见的研究成为可能(例如，在流行病学中，如果最有可能遭受该事件的患者也往往是那些经历过接触的患者)。然而，随着这种方法越来越受欢迎，在没有适当考虑假设的情况下应用它的诱惑也增加了。在检查这些假设是否真的需要，以及它们是否可能以各种方式被削弱方面，做的工作相对较少。我们将研究四个关键假设。第一个，也可能是最严格的要求是，暴露不受以前事件的影响。例如，如果感兴趣的事件是死亡，那么这一假设就不成立。我们在这个标题下的主要目标将是开发一种适用于死亡等事件的病例系列方法。第二个关键假设与点接触有关，即在特定时间点发生的接触，如疫苗接种或电力激增。对于这样的暴露，定义了一个风险函数，该函数描述了在点暴露之后结果事件发生的概率如何变化。目前，关于风险函数的形状，特别是增加的风险期的持续时间，需要一些假设。我们将研究更灵活的风险函数，这不需要这样的假设。我们将调查的第三个假设是，如果一个人经历了几个事件，那么这些事件相互独立地发生。这在实践中往往并非如此：例如，一台机器发生一次故障可能会增加后续故障的可能性。最后，我们将研究的第四个假设是，观察期不取决于事件发生的时间。例如，当事件增加死亡机会时，情况就不是这样了，就像心脏病发作一样。我们将研究这一假设的失败对结果的影响程度。这个项目将为案例序列方法产生新的统计理论，从而使该方法得到更好的理解和更广泛的应用。这一发现将发表在同行评议的期刊上和案例系列网站http://statistics.open.ac.uk/sccs.上

An important statistical problem in many fields is to determine whether one variable, called the exposure, affects the occurrence of an event of interest. For example, does administering a drug cause adverse reactions; do certain production modes increase the chance of faults in a manufacturing process; does the occurrence of one life event affect the likelihood of another. Various statistical methods have been developed to identify associations such as these, and to estimate their strength. This project is concerned with one particular method, the case series method, which has emerged relatively recently.This method differs from others in that only individuals (or items) that have experienced the event of interest need to be sampled. Thus, individuals who have not experienced the event are not needed. This is advantageous when the event of interest is rare. Comparisons are made within each individual's period of observation, with the additional benefit that many of the factors that might distort the association between the exposure and the event, known as confounding factors, are automatically allowed for. This double benefit - reduced study size and good control of confounding factors - comes at a price, namely the need for stronger assumptions than are required by other methods. The purpose of the present project is to study these assumptions, in order better to understand their role, if possible weaken them, and hence widen the range of application of the method.The project is important because the case series method is increasingly being used in a range of fields, particularly in epidemiology. This is because it is relatively cheap, and makes possible studies which otherwise would be impractical or biased (for example, in epidemiology, if the patients most likely to suffer the event also tend to be those that experience the exposure). However, as the method grows in popularity, the temptation to apply it without due regard to the assumptions increases. Relatively little work has been done to check whether the assumptions are really needed, and whether they may be weakened in various ways. We will look at four key assumptions. The first, and probably most restrictive requirement, is that exposures are unaffected by previous events. This assumption fails if, for example, the event of interest is death. Our main objective under this heading will be to develop a case series method that works for events such as deaths.The second key assumption relates to point exposures, that is, exposures that occur at a particular point in time, like a vaccination, or a power surge. For such exposures, a risk function is defined which describes how the chance of occurrence of the outcome event varies after the point exposure. At present, some assumptions are required about the shape of the risk function, and in particular the duration of the increased risk period. We will study more flexible risk functions, which do not require such assumptions.The third assumption we will investigate is that, if an individual experiences several events, then these occur independently of each other. This is often not true in practice: for example, occurrence of one failure in a piece of machinery might increase the chance of subsequent failures. We will develop a test of independence of recurrent events, valid for case series analyses.Finally, the fourth assumption we will study is that the period of observation does not depend on the timing of events. This is not the case when, for example, the event increases the chance of death, as with heart attacks. We will study the extent to which failure of this assumption affects the results.This project will produce new statistical theory for the case series method, leading to better understanding and hence wider use of the method. The findings will be published in peer-reviewed journals and on the case series website at http://statistics.open.ac.uk/sccs.