Abstract
BACKGROUND: Evidence indicates that trial participants often struggle to understand participant information leaflets (PILs) for clinical trials, including the concept of randomisation. We analysed the language used to describe randomisation in PILs and determine the most understandable and acceptable description through public and participant feedback. METHODS: We collected 280 PILs/informed consent forms and one video animation from clinical research facilities/clinical trial units in Ireland and the UK. We extracted text on how randomisation was described, plus trial characteristics. We conducted content analysis to group the randomisation phrases inductively. We then excluded phrases that appeared more than once or were very similar to others. The final list of randomisation phrases was then presented to an online panel of participants and the public. Panel members were asked to rate each phrase on a 5-point Likert scale in terms of their understanding of the phrase, confidence in their understanding and acceptability of the phrase. RESULTS: Two hundred and eighty PILs and the transcribed text from one video animation represented 229 ongoing or concluded trials. The pragmatic content analysis generated five inductive categories: (1) explanation of why randomisation is required in trials; (2) synonyms for randomisation; (3) comparative randomisation phrases; (4) elaborative phrases for randomisation (5) and phrases that describe the process of randomisation. We had 48 unique phrases, which were shared with 73 participants and members of the public. Phrases that were well understood were not necessarily acceptable. Participants understood, but disliked, comparative phrases that referenced gambling, e.g. toss of a coin, like a lottery, roll of a die. They also disliked phrases that attributed decision-making to computers or automated systems. Participants liked plain language descriptions of what randomisation is and those that did not use comparative phrases. CONCLUSIONS: Potential trial participants are clear on their likes and dislikes when it comes to describing randomisation in PILs. We make five recommendations for practice.