Bias-adjusted inference in Biostatistics

医疗实践的进步必须以证据为基础。例如，疾病（如肺癌）的新疗法在获得许可之前必须证明是有效的，同样，在实施公共卫生战略之前，必须将可改变的接触（如吸烟）与不良健康结果可靠地联系起来。这就需要收集、分析和解释数据。在当今竞争激烈的世界里，时间和金钱都是供不应求的。因此，科学家们在压力下尽可能有效地收集证据，利用现有的数据来源、新颖的试验设计和最新的技术。然而，不了解创建数据源的过程可能导致对其提供的信息有偏见，并导致不良的医疗决策。临床试验的规模（即患者的数量）和范围（如提出的问题）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，对于试验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一项随机试验，但如果他们的初始治疗被证明无效，他们可以改用一种新的治疗方法。或者，在测试单一疗法多剂量试验的第二年招募的患者，可以在第一年接受效果最好的剂量。像这样的调整意味着患者可以在试验中获得高标准的护理，但同时也使有效的治疗方法能够更快地被确定并获得许可。然而，如果从这些试验中得到的数据仅仅从表面上看，它们也可能系统性地高估或低估治疗的真实效果。流行病学的主要目标之一是找到疾病的根本原因，以便有效治疗。伦理和实际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。流行病学家必须依靠观察或回顾性收集的数据来找到这些原因。然而，在潜在风险因素（如酒精摄入）和医疗状况（如高血压）之间发现的强烈相关性并不能保证酒精会导致高血压，因为另一个未观察到的因素（如盐摄入）实际上可能与两者都有关。这被称为“混淆偏差”。很难调整混杂因素的影响，因为人们永远无法确定所有可能的混杂因素，如盐摄入量，都已被发现。在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，有一个隐含的假设，即它们构成了所进行的所有研究的代表性和无偏见样本。然而，通常只有在实际可行的情况下才能找到并纳入已经在学术期刊上发表的研究。当科学家们挑选出他们最激动人心的研究结果提交发表，而期刊编辑根据研究结果的统计显著性优先发表研究结果时，他们就会产生“传播偏见”。其结果是，公共领域的研究结果分布不均匀，不能代表研究领域的真实立场。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测试，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的资源浪费，最终有益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 bereliably 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. Scientistsare 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 datasources 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 askewed 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.