A non-parametric approach to predict the recruitment for randomized clinical trials: an example in elderly inpatient settings.

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Alejandro Villasante-Tezanos, Yong-Fang Kuo, Christopher Kurinec, Yisheng Li, Xiaoying Yu
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引用次数: 0

Abstract

Background: Accurate prediction of subject recruitment, which is critical to the success of a study, remains an ongoing challenge. Previous prediction models often rely on parametric assumptions which are not always met or may be difficult to implement. We aim to develop a novel method that is less sensitive to model assumptions and relatively easy to implement.

Methods: We create a weighted resampling-based approach to predict enrollment in year two based on recruitment data from year one of the completed GRIPS and PACE clinical trials. Different weight functions accounted for a range of potential enrollment trajectory patterns. Prediction accuracy was measured by Euclidean distance for enrollment sequence in year two, total enrollment over time, and total weeks to enroll a fixed number of subjects, against the actual year two enrollment data. We compare the performance of the proposed method with an existing Bayesian method.

Results: Weighted resampling using GRIPS data resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, especially when enrollment gaps were filled prior to the weighted resampling. These scenarios also produced more accurate predictions for total enrollment and number of weeks to enroll 50 participants. These same scenarios outperformed an existing Bayesian method for all 3 accuracy measures. In PACE data, using a reduced year 1 enrollment resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, with the weighted resampling scenarios better reflecting the seasonal variation seen in year (1) The reduced enrollment scenarios resulted in closer prediction for total enrollment over 6 and 12 months into year (2) These same scenarios also outperformed an existing Bayesian method for relevant accuracy measures.

Conclusion: The results demonstrate the feasibility and flexibility for a resampling-based, non-parametric approach for prediction of clinical trial recruitment with limited early enrollment data. Application to a wider setting and long-term prediction accuracy require further investigation.

预测随机临床试验招募情况的非参数方法:以老年住院病人为例。
背景:准确预测受试者招募情况对研究的成功与否至关重要,但这仍是一项持续的挑战。以前的预测模型通常依赖于参数假设,而这些假设并不总是能满足要求,或者很难实现。我们的目标是开发一种对模型假设不那么敏感且相对容易实施的新方法:我们根据已完成的 GRIPS 和 PACE 临床试验第一年的招募数据,创建了一种基于加权重采样的方法来预测第二年的注册情况。不同的加权函数考虑了一系列潜在的入组轨迹模式。根据第二年的实际招募数据,对第二年的招募顺序、一段时间内的总招募人数以及招募固定数量受试者的总周数进行欧氏距离测量,从而衡量预测的准确性。我们将拟议方法的性能与现有的贝叶斯方法进行了比较:使用 GRIPS 数据进行加权重采样后,预测结果更为接近,这体现在预测区间对观察到的注册人数的覆盖率更高,与第二年实际注册人数的欧氏距离更小,尤其是在加权重采样之前填补了注册空白的情况下。这些方案还能更准确地预测总注册人数和注册 50 名参与者所需的周数。在所有 3 个准确性指标上,这些方案的表现都优于现有的贝叶斯方法。在 PACE 数据中,减少第 1 年的注册人数使预测更加准确,这表现在预测区间对观察到的注册人数的覆盖范围更大,与第 2 年实际注册人数的欧氏距离更小,加权重采样方案更好地反映了第 1 年的季节性变化,减少注册人数方案使第 2 年 6 个月和 12 个月的总注册人数预测更加准确:研究结果表明,基于重采样的非参数方法在早期招募数据有限的情况下预测临床试验招募的可行性和灵活性。需要进一步研究该方法在更广泛环境中的应用以及长期预测的准确性。
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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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