Gajewski Byron J, Simon Stephen D, Carlson Susan E
Department of Biostatistics, The University of Kansas Medical Center, Kansas City, USA.
Int J Stat Probab. 2012 Nov 1;1(2):p43. doi: 10.5539/ijsp.v1n2p43.
Many clinical trials fall short of their accrual goals. This can be avoided with accurate accrual prediction tools. Past researchers provide important methodological alternative models for predicting accrual in clinical trials. One model allows for slow accrual at the start of the study, which eventually reaches a threshold. A simpler model assumes a constant rate of accrual. A comparison has been attempted but we wish to point out some important considerations when comparing these two models. In fact, we can examine the reasonableness of a constant accrual assumption (simpler model) which had data 239 days into a three-year study. We can now update that and report accumulated from the full three years of accrual data and we can demonstrate that constant accrual rate assumption was met in this particular study. We will use this report to frame future research in the area of accrual prediction.
许多临床试验未能达到其入组目标。使用准确的入组预测工具可以避免这种情况。过去的研究人员提供了用于预测临床试验入组情况的重要方法替代模型。一种模型允许在研究开始时缓慢入组，最终达到一个阈值。一个更简单的模型假设入组率恒定。已经有人尝试进行比较，但我们希望指出在比较这两种模型时的一些重要考虑因素。事实上，我们可以检验一个恒定入组假设（更简单的模型）在一项为期三年的研究进行到第239天时的数据的合理性。我们现在可以更新该数据，并报告从完整的三年入组数据中积累的数据，并且我们可以证明在这项特定研究中满足恒定入组率假设。我们将利用这份报告为入组预测领域的未来研究提供框架。
