Wolfson Institute of Population Health, Centre for Evaluation and Methods, Queen Mary University of London, London, UK.
The Cancer Research UK and King's College London Cancer Prevention Trials Unit, Kings College London, London, UK.
Clin Trials. 2023 Aug;20(4):425-433. doi: 10.1177/17407745231167369. Epub 2023 Apr 24.
Participants of health research studies such as cancer screening trials usually have better health than the target population. Data-enabled recruitment strategies might be used to help minimise healthy volunteer effects on study power and improve equity.
A computer algorithm was developed to help target trial invitations. It assumes participants are recruited from distinct sites (such as different physical locations or periods in time) that are served by clusters (such as general practitioners in England, or geographical areas), and the population may be split into defined groups (such as age and sex bands). The problem is to decide the number of people to invite from each group, such that all recruitment slots are filled, healthy volunteer effects are accounted for, and equity is achieved through representation in sufficient numbers of all major societal and ethnic groups. A linear programme was formulated for this problem.
The optimisation problem was solved dynamically for invitations to the NHS-Galleri trial (ISRCTN91431511). This multi-cancer screening trial aimed to recruit 140,000 participants from areas in England over 10 months. Public data sources were used for objective function weights, and constraints. Invitations were sent by sampling according to lists generated by the algorithm. To help achieve equity the algorithm tilts the invitation sampling distribution towards groups that are less likely to join. To mitigate healthy volunteer effects, it requires a minimum expected event rate of the primary outcome in the trial.
Our invitation algorithm is a novel data-enabled approach to recruitment that is designed to address healthy volunteer effects and inequity in health research studies. It could be adapted for use in other trials or research studies.
参与健康研究（如癌症筛查试验）的参与者通常比目标人群健康状况更好。可以使用数据驱动的招募策略来帮助尽量减少健康志愿者对研究效力的影响，并提高公平性。
开发了一种计算机算法来帮助确定试验邀请对象。它假设参与者是从不同的地点（如不同的物理位置或时间段）招募而来，这些地点由簇（如英国的全科医生，或地理区域）服务，人群可以分为定义的组（如年龄和性别带）。问题是要决定从每个组中邀请多少人，以便所有的招募名额都被填满，考虑到健康志愿者的影响，并通过在所有主要社会和种族群体中都有足够数量的代表来实现公平。针对这个问题制定了一个线性规划。
针对 NHS-Galleri 试验（ISRCTN91431511）的邀请，动态地解决了优化问题。这项多癌症筛查试验旨在在 10 个月内从英格兰的各个地区招募 140000 名参与者。使用公共数据源来确定目标函数权重和约束条件。根据算法生成的列表进行抽样邀请。为了帮助实现公平性，算法会偏向于那些不太可能参加的群体进行邀请抽样分配。为了减轻健康志愿者的影响，它要求试验中主要结局的预期事件发生率达到最低。
我们的邀请算法是一种新的数据驱动的招募方法，旨在解决健康研究中健康志愿者的影响和不公平问题。它可以适用于其他试验或研究。
