Bias-adjusted inference in Biostatistics

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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.