Bias-adjusted inference in Biostatistics

生物统计学中的偏差调整推理

• 批准号: MR/N501906/1
• 负责人: Jack Bowden
• 金额: $ 22.06万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN501906%2F1
• 关键词: Bias; adjusted; inference; Biostatistics

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.

医学实践的进步必须基于证据。例如，在获得许可之前，必须证明对疾病的新疗法（例如肺癌）有效，并且同样可修改的暴露（例如吸烟）必须在实施公共卫生策略之前与不良健康结果相关。这需要数据的收集，分析和解释。在当今竞争激烈的世界中，时间和金钱供不应求。因此，科学家们在压力下，通过利用现有的数据源，新颖的试验设计和最新技术，以尽可能有效地收集这一证据。但是，未能理解创建数据源的过程可能会导致其提供的信息的偏见，并导致医疗决策不佳。现在有一些例子：传统上，临床试验的大小（即患者数量）和范围（例如，提出的问题）已提前固定。但是，它们越来越多地以依次的方式进行，关于试验可能采取的方向的严格指南较少。例如，晚期癌症患者可能会参加一项随机试验，但如果其初始治疗证明无效，则可以改用新疗法。或者，在试验测试单次疗法的第二年，多种剂量的患者可以接受第一年表现最好的剂量。这样的适应意味着在试验中可以为患者提供高标准的护理，但与此同时，可以更快地识别和许可有效治疗。但是，如果仅仅以这种试验的形式获取了这些试验的数据，那么它们也可以系统地过度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，以便可以有效地治疗。道德和实际原因通常意味着临床试验（检验因果假设的最佳方法）并非总是可能的。然后，流行病学家必须依靠观察性或回顾性收集的数据才能找到这些原因。但是，潜在的危险因素（例如酒精摄入量）与医疗状况（例如高血压）之间看到的很强的相关性并不能保证酒精会导致高血压，因为实际上可能与两者相关。这被称为“混杂偏见”。很难根据混杂的影响进行调整，因为已经找到了所有可能的混杂因素，例如盐的摄入量。但是，通常只能找到并包括在学术期刊上发表的研究。当科学家挑选出他们最令人兴奋的结果以供出版时，杂志编辑优先发布研究结果，基于他们的统计意义，他们会引起“传播偏见”。结果是在公共领域中发现的结果不足，这并不代表研究领域的真实地位。我和我的合作者将致力于解决这些问题。我们将共同开发统计理论，使用计算机模拟对其进行测试，然后在国际会议上提出我们的结果，以进行进一步的批判性评估。一旦我们对它们的价值充满信心，我们将应用新方法在现有患者数据的分析和解释中。从长远来看，我们的工作可能会影响未来的科学研究的设计，减少NHS中浪费的资源，并最终使社会健康和福祉受益。

项目成果

Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression.

• DOI: 10.1093/ije/dyy101
• 发表时间: 2018-08-01
• 期刊: International journal of epidemiology
• 影响因子: 7.7
• 作者: Bowden J;Spiller W;Del Greco M F;Sheehan N;Thompson J;Minelli C;Davey Smith G
• 通讯作者: Davey Smith G

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.

• DOI: 10.1093/ije/dyv080
• 发表时间: 2015-04
• 期刊: International journal of epidemiology
• 影响因子: 7.7
• 作者: Bowden J;Davey Smith G;Burgess S
• 通讯作者: Burgess S

Response to Hartwig and Davies.

• DOI: 10.1093/ije/dyw252
• 发表时间: 2016-10
• 期刊: International journal of epidemiology
• 影响因子: 7.7
• 作者: Bowden J;Burgess S;Davey Smith G
• 通讯作者: Davey Smith G

Unbiased estimation for response adaptive clinical trials.

• DOI: 10.1177/0962280215597716
• 发表时间: 2017-10
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Bowden J;Trippa L
• 通讯作者: Trippa L

Gaining power and precision by using model-based weights in the analysis of late stage cancer trials with substantial treatment switching.

• DOI: 10.1002/sim.6801
• 发表时间: 2016-04-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Bowden J;Seaman S;Huang X;White IR
• 通讯作者: White IR