Bias-adjusted inference in Biostatistics

医疗实践的进步必须以证据为基础。例如，新的疾病治疗方法（如肺癌）在获得许可之前必须证明有效，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，时间和金钱都很短缺。因此，科学家们面临着尽可能有效地收集证据的压力，通过利用现有的数据来源，新颖的试验设计和最新的技术。然而，如果不了解数据源的创建过程，可能会导致对它们提供的信息产生偏见，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（即患者人数）和范围（例如提出的问题）传统上是预先确定的。然而，这些审判越来越多地按顺序进行，对审判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，但如果最初的治疗证明无效，则允许改用新的治疗方法。或者，在试验的第二年招募的患者在多个剂量下测试单一疗法，可以接受第一年表现最好的剂量。像这样的适应性调整意味着患者可以在试验中获得高标准的护理，但同时能够更快地识别和许可有效的治疗方法。然而，如果从这些试验中获得的数据仅仅是表面价值，它们也可能系统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，以便有效地治疗。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。然后，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，在潜在的风险因素（例如酒精摄入量）和医疗条件（例如高血压）之间看到的强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（例如盐摄入量）实际上可能与两者有关。这被称为“混杂偏差”。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。为了告知未来的卫生政策，在综合所有针对特定研究问题的现有医学试验结果时，一个隐含的假设是，它们构成了所有研究的代表性和无偏见的样本。然而，查找和纳入已在学术期刊上发表的研究往往只在实践中可行。当科学家们挑选他们最令人兴奋的结果提交发表时，期刊编辑根据其统计意义优先发表研究结果，这就造成了“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立场。我和我的合作者将致力于解决这些问题的研究。我们将共同发展统计理论，用计算机模拟进行测试，然后在国际会议上展示我们的结果，以供进一步的批判性评估。一旦我们对它们的价值充满信心，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中浪费的资源，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 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.