MRC Clinical Trials Unit at UCL, London, UK.
Imperial Clinical Trials Unit, Imperial College London, London, UK.
Trials. 2021 Oct 9;22(1):686. doi: 10.1186/s13063-021-05644-4.
An estimand is a precise description of the treatment effect to be estimated from a trial (the question) and is distinct from the methods of statistical analysis (how the question is to be answered). The potential use of estimands to improve trial research and reporting has been underpinned by the recent publication of the ICH E9(R1) Addendum on the use of estimands in clinical trials in 2019. We set out to assess how well estimands are described in published trial protocols.
We reviewed 50 trial protocols published in October 2020 in Trials and BMJ Open. For each protocol, we determined whether the estimand for the primary outcome was explicitly stated, not stated but inferable (i.e. could be constructed from the information given), or not inferable.
None of the 50 trials explicitly described the estimand for the primary outcome, and in 74% of trials, it was impossible to infer the estimand from the information included in the protocol. The population attribute of the estimand could not be inferred in 36% of trials, the treatment condition attribute in 20%, the population-level summary measure in 34%, and the handling of intercurrent events in 60% (the strategy for handling non-adherence was not inferable in 32% of protocols, and the strategy for handling mortality was not inferable in 80% of the protocols for which it was applicable). Conversely, the outcome attribute was stated for all trials. In 28% of trials, three or more of the five estimand attributes could not be inferred.
The description of estimands in published trial protocols is poor, and in most trials, it is impossible to understand exactly what treatment effect is being estimated. Given the utility of estimands to improve clinical research and reporting, this urgently needs to change.
估计量是从试验中估计治疗效果的精确描述（即问题），与统计分析方法（如何回答问题）不同。最近在 2019 年发布了 ICH E9(R1) 关于临床试验中使用估计量的附录，这为估计量在提高试验研究和报告中的应用提供了依据。我们旨在评估已发表的试验方案中对估计量的描述情况。
我们回顾了 2020 年 10 月发表在 Trials 和 BMJ Open 上的 50 项试验方案。对于每个方案，我们确定主要结局的估计量是否明确说明、未说明但可推断（即可以根据提供的信息构建）或不可推断。
50 项试验均未明确描述主要结局的估计量，在 74%的试验中，无法从方案中包含的信息推断出估计量。在 36%的试验中，估计量的人群属性无法推断，在 20%的试验中，治疗条件属性无法推断，在 34%的试验中，人群水平汇总测量指标无法推断，在 60%的试验中（处理不依从事件的策略在 32%的方案中不可推断，处理死亡率的策略在 80%适用的方案中不可推断）。相反，结局属性在所有试验中都有说明。在 28%的试验中，五个估计量属性中有三个或更多无法推断。
发表的试验方案中对估计量的描述很差，在大多数试验中，无法确切了解正在估计的治疗效果是什么。鉴于估计量在改善临床研究和报告方面的实用性，这种情况急需改变。
