Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
BMC Med Res Methodol. 2019 Jan 7;19(1):4. doi: 10.1186/s12874-018-0646-z.
In a randomised controlled trial, contamination is defined as the receipt of active intervention amongst participants in the control arm. This review assessed the processes leading to contamination, its typical quantity, methods used to mitigate it, and impact of use of cluster randomisation to prevent it on study findings in trials of complex interventions in mental health.
This is a scoping review of trial design approaches and methods of study conduct to address contamination. Studies included were randomised controlled trials of complex interventions in mental health that described the process leading to, amount of, or solution used to counter contamination. The Medline, Embase, and PsycInfo databases were searched for trials published between 2000 and 2015. Risk of bias was assessed using the Jadad score and domains recommended by Cochrane plus some relevant to cluster randomised trials.
Two hundred and thirty-four articles were included in the review. The main processes that led to contamination were health professionals delivering both active and comparator treatments and communication among clinicians and participants from the different trial arms. Twenty-three trials (10%) measured binary treatment receipt in the control arm with median 13% of participants found to be contaminated (IQR 5-33%). The most common design approach for dealing with contamination was the use of cluster randomisation (n = 93). In addition, many researchers used simple trial conduct methods to minimise contamination due to suspected contamination processes, such as organising for each clinician to provide only one treatment and separating trial arms spatially or temporally. There was little evidence for a relationship between cluster randomisation to avoid contamination and size of treatment effect estimate.
There was some evidence of modest levels of treatment contamination with a large range, although a minority of studies reported the amount of contamination. A limitation was that many trials described the problem in little detail. Overall there is a need for greater measurement and reporting of treatment receipt in the control arm of trials. Researchers should be aware of trial conduct methods that can be used to minimise contamination without resorting to cluster randomisation.
在一项随机对照试验中，污染被定义为对照组参与者接受了活性干预。本综述评估了导致污染的过程、其典型数量、用于减轻污染的方法，以及使用集群随机化来防止污染对精神健康复杂干预试验研究结果的影响。
这是一项关于试验设计方法和处理方法的范围综述，旨在解决污染问题。纳入的研究是精神健康领域复杂干预的随机对照试验，描述了导致污染、污染数量或用于对抗污染的解决方案的过程。检索了 2000 年至 2015 年发表的试验的 Medline、Embase 和 PsycInfo 数据库。使用 Jadad 评分和 Cochrane 推荐的一些针对集群随机试验的领域评估偏倚风险。
综述纳入了 234 篇文章。导致污染的主要过程是卫生专业人员同时提供活性治疗和对照治疗，以及来自不同试验组的临床医生和参与者之间的沟通。23 项试验（10%）测量了对照组中二元治疗的接受情况，发现中位数 13%的参与者受到污染（IQR 5-33%）。处理污染的最常见设计方法是使用集群随机化（n=93）。此外，许多研究人员采用了简单的试验处理方法来减少由于可疑污染过程引起的污染，例如组织每个临床医生只提供一种治疗方法，并在空间或时间上分离试验组。几乎没有证据表明集群随机化避免污染与治疗效果估计值的大小之间存在关系。
虽然少数研究报告了污染的数量，但有一些证据表明存在中等程度的治疗污染，范围较大。一个限制是许多试验没有详细描述这个问题。总体而言，需要更加注重在试验的对照组中测量和报告治疗的接受情况。研究人员应该意识到可以用来最小化污染而不依赖集群随机化的试验处理方法。
