Department of Biostatistics & Data Science, University of Texas Medical Branch at Galveston (UTMB), Galveston, TX, USA.
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
BMC Med Res Methodol. 2024 Aug 29;24(1):189. doi: 10.1186/s12874-024-02314-2.
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.
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.
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.
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.
准确预测受试者招募情况对于研究的成功至关重要，但这仍然是一个持续存在的挑战。以前的预测模型通常依赖于参数假设，但这些假设并不总是成立，或者实施起来可能很困难。我们旨在开发一种不太依赖模型假设且相对易于实施的新方法。
我们创建了一种基于加权重采样的方法，根据已完成的 GRIPS 和 PACE 临床试验第一年的招募数据来预测第二年的入组情况。不同的权重函数考虑了一系列潜在的入组轨迹模式。预测准确性通过第二年入组序列的欧几里得距离、随时间推移的总入组人数以及招募固定数量受试者所需的总周数来衡量，与第二年的实际入组数据进行比较。我们将所提出的方法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导致了更接近的预测，表现为预测区间更好地覆盖了实际入组情况，并且与第二年的实际入组数据的欧几里得距离更小，尤其是在加权重采样之前填补了入组差距的情况下。这些情况还对总入组人数和招募 50 名参与者所需的周数产生了更准确的预测。对于所有 3 种准确性度量，这些相同的情况均优于现有的贝叶斯方法。在 PACE 数据中，使用减少的第一年入组数据导致了更接近的预测，表现为预测区间更好地覆盖了实际入组情况，并且与第二年的实际入组数据的欧几里得距离更小，加权重采样情况更能反映出第一年中看到的季节性变化。减少入组的情况对第二年 6 个月和 12 个月的总入组人数的预测更接近。这些相同的情况对于相关的准确性度量也优于现有的贝叶斯方法。
结果表明，对于具有有限早期入组数据的临床试验招募预测，基于重采样的非参数方法是可行且灵活的。在更广泛的环境和长期预测准确性方面的应用需要进一步研究。
