Bias-adjusted inference in Biostatistics

医疗实践的进步必须以证据为基础。例如，治疗疾病(如肺癌)的新疗法必须在获得许可之前被证明有效，同样，在实施公共卫生战略之前，可改变的暴露(如吸烟)必须与不利的健康后果可靠地联系在一起。这就需要收集、分析和解释数据。在当今竞争激烈的世界里，时间和金钱都供不应求。因此，科学家们面临着压力，要求他们尽可能有效地收集证据，利用现有的数据源、新颖的试验设计和可用的最新技术。然而，不了解数据源的创建过程可能会导致对它们提供的信息的偏见，并导致糟糕的医疗决策。现在有一些这样的例子：临床试验的规模(即患者数量)和范围(例如所提出的问题)传统上是事先确定的。然而，它们越来越多地以顺序方式进行，对审判可能采取的方向没有那么严格的指导方针。例如，晚期癌症患者可以参加随机试验，但如果最初的治疗无效，则允许他们切换到新的治疗方法。或者，在测试单一疗法的多剂量试验的第二年招募的患者，可以接受第一年表现最好的剂量。像这样的适应意味着患者在试验中可以得到高标准的护理，但同时也使有效的治疗能够更快地被确定和获得许可。然而，如果简单地从表面上获得这些试验的数据，它们还可能系统性地高估或低估治疗的真正效果。流行病学的主要目标之一是找到疾病的根本原因，以便有效地治疗它。伦理和实践方面的原因往往意味着临床试验--检验因果假设的最佳方式--并不总是可行的。然后，流行病学家必须依靠观察或回顾收集的数据来找到这些原因。然而，潜在危险因素(例如酒精摄入量)和医疗条件(例如高血压)之间的强烈相关性并不能保证酒精会导致高血压，因为一个单独的未观察到的因素(例如盐分摄入量)实际上可能与两者有关。这就是所谓的“混淆偏见”。很难对混杂的影响进行调整，因为人们永远不能确定所有可能的混杂因素，比如盐摄入量，都已经找到了。当综合针对某一特定研究问题的所有现有医学试验的结果，以便为未来的卫生政策提供信息时，一个隐含的假设是，这些试验形成了所进行的所有研究的具有代表性和不偏不倚的样本。然而，寻找并收录已发表在学术期刊上的研究往往只有在实践中才是可行的。当科学家精心挑选他们最令人兴奋的结果提交发表时，期刊编辑根据研究结果的统计意义优先发布研究结果，这会导致“传播偏见”。其结果是，公共领域的研究结果分布不均，并不代表研究领域的真实位置。我和我的合作者将致力于解决这些问题。我们将共同发展统计理论，用计算机模拟对其进行检验，然后在国际会议上展示我们的结果，以进行进一步的批判性评估。一旦我们对它们的价值有信心，我们将应用我们的新方法来分析和解释现有的患者数据。从长远来看，我们的工作可能会影响未来科学研究的设计方式，减少NHS浪费的资源，最终有利于社会的健康和福祉。

Advancements in medical practice must be evidence based. For example, new treatments for diseases (such as lung cancer) must be proven effective before being licensed and, equally, modifiable exposures (such as smoking) must be reliably linked to adverse health outcomes before public health strategies are implemented. This necessitates the collection, analysis and interpretation of data. In today's competitive world, time and money are in short supply. Scientists are therefore under pressure to collect this evidence as efficiently as possible, by making use of existing data sources, novel trial designs and the latest technology where available. However, failure to understand the process by which data sources are created can lead to a biased picture of the information they provide, and give rise to poor medical decision making. Some examples of this now follow:The size (i.e. the number of patients) and the scope (e.g. the question being asked) of a clinical trial has traditionally been fixed in advance. However, they are increasingly conducted in a sequential fashion, with less rigid guidelines as to the direction the trial may take. For example, patients with advanced cancer could participate in a randomised trial, but be allowed to switch to a new treatment if their initial therapy proves ineffective. Or, patients recruited in the second year of a trial testing a single therapy at multiple doses, could receive the dose which performed the best in year one. Adaptations like these mean patients can be afforded a high standard of care in the trial, but at the same time enable effective treatments to be identified and licenced more quickly. Yet, if the data arising from such trials are simply taken at face value, they can also systematically over- or under-estimate the true effect of the treatment.One of the primary goals of Epidemiology is to find the root causes of a disease, so that it can be treated effectively. Ethical and practical reasons often mean that clinical trials - the best way of testing causal hypotheses - are not always possible. Epidemiologists then must rely on observational or retrospectively collected data to find these causes. However, strong correlations seen between a potential risk factor (e.g. alcohol intake) and a medical condition (e.g. high blood pressure) are no guarantee that alcohol causes high blood pressure, because a separate unobserved factor (e.g. salt intake) may in fact be a relate to both. This is referred to as `confounding bias'. It is difficult to adjust for the effect of confounding, because one is never sure that all possible confounders, like salt intake, have been found. An implicit assumption made when synthesising the results of all the available medical trials addressing a particular research question, in order to inform future health policy, is that they form a representative and unbiased sample of all studies conducted. However, it is often only practically feasible to find and include studies that have been published in academic journals. When scientists cherry-pick their most exciting results to submit for publication, and the journal editors preferentially publish study results based on their statistical significance they induce `dissemination bias'. The result is a skewed distribution of findings in the public domain that does not represent the true position in a research field.My collaborators and I will dedicate our research towards solving these problems. Together we will develop statistical theory, put it to the test using computer simulation, and then present our results at international conferences for further critical appraisal. Once we are confident of their worth, we will apply our new methods in the analysis and interpretation of existing patient data. In the long term our work may influence the way that future scientific studies are designed, reduce wasted resources in the NHS and, ultimately, benefit the health and wellbeing of society.