PDF(Pubmed)
Abstract：
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.
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.
摘要：
■癌症筛查试验等健康研究的参与者通常比目标人群的健康状况更好。数据支持的招募策略可用于帮助最大程度地减少健康志愿者对学习能力的影响并提高公平性。
■开发了一种计算机算法来帮助目标试验邀请。假设参与者是从不同的地点（例如不同的物理位置或时间段）招募的，这些地点由集群（例如英格兰的全科医生，或地理区域)，和人口可以分为确定的群体（如年龄和性别范围）。问题是要决定每个小组邀请的人数，这样所有的招聘槽都被填满了，健康的志愿者影响被考虑在内，公平是通过在所有主要社会和族裔群体中有足够数量的代表来实现的。为此问题制定了线性方案。
■针对NHS-Galleri试验（ISRCTN91431511）的邀请，动态解决了优化问题。这项多癌筛查试验旨在在10个月内从英格兰地区招募14万名参与者。公共数据来源用于目标函数权重，和约束。通过根据算法生成的列表进行采样来发送邀请。为了帮助实现公平性，该算法将邀请抽样分布向不太可能加入的组倾斜。为了减轻健康志愿者的影响，它要求试验中主要结局的最低预期事件发生率.
■我们的邀请算法是一种新颖的基于数据的招募方法，旨在解决健康志愿者对健康研究的影响和不平等。它可以适用于其他试验或研究。
■开发了一种计算机算法来帮助目标试验邀请。假设参与者是从不同的地点（例如不同的物理位置或时间段）招募的，这些地点由集群（例如英格兰的全科医生，或地理区域)，和人口可以分为确定的群体（如年龄和性别范围）。问题是要决定每个小组邀请的人数，这样所有的招聘槽都被填满了，健康的志愿者影响被考虑在内，公平是通过在所有主要社会和族裔群体中有足够数量的代表来实现的。为此问题制定了线性方案。
■针对NHS-Galleri试验（ISRCTN91431511）的邀请，动态解决了优化问题。这项多癌筛查试验旨在在10个月内从英格兰地区招募14万名参与者。公共数据来源用于目标函数权重，和约束。通过根据算法生成的列表进行采样来发送邀请。为了帮助实现公平性，该算法将邀请抽样分布向不太可能加入的组倾斜。为了减轻健康志愿者的影响，它要求试验中主要结局的最低预期事件发生率.
■我们的邀请算法是一种新颖的基于数据的招募方法，旨在解决健康志愿者对健康研究的影响和不平等。它可以适用于其他试验或研究。
