Method - Analysis

对试验数据进行适当的统计分析是正确解释试验数据的关键。分析方案制定和传播针对分析挑战的实际解决办法，主要是：·关于患者结果的缺失数据(无处不在，并因当前的大流行而加剧)。我们继续解决如何使丢失的患者结果数据的假设可访问、可辩护，以及如何正确地将这些纳入试验解释的挑战。·确保试验达到他们的目标，仔细考虑(I)患者群体，(I)结果和如何衡量它们，以及(Iii)一旦患者登记参加研究，如何考虑计划外的发展(例如无法耐受治疗，或转向另一种治疗)。这些问题对该股的结核病和癌症试验尤为重要。·使用计算机模拟来有效地了解拟议的试验可能有多大用处，尤其是对试验进展可能出现的一系列情景的适应能力·与艾伦·图灵研究所合作，利用机器学习方面的最新进展，确定可能从某些治疗中特别受益的患者亚群。

Appropriate statistical analysis of trial data is key for their correct interpretation. The analysis programme develops and disseminates practical solutions to analysis challenges, principally: • missing data on patients’ outcomes (ubiquitous, and aggravated by the current pandemic). We continue to address the challenge of how to make accessible, defensible, assumptions about missing patient outcome data, and how to correctly incorporate these in the trial’s interpretation. • ensuring trials meet their objectives, by thinking carefully about the (i) patient population, (i) outcomes and how they will be measured, and (iii) how to take account of unplanned developments once patients are enrolled in a study (such as inability to tolerate treatment, or switching to another treatment). These issues are particularly important for the Unit’s tuberculosis and cancer trials. • using computer simulation to effectively understand how useful proposed trials are likely to be, and in particular how resilient to a range of scenarios that may unfold as the trial progresses• collaborating with the Alan Turing Institute to exploit recent advances in machine learning to identify subgroups of patients who may particularly benefit from certain treatments.