Background: Determining an adequate sample size is essential for developing reliable and generalisable clinical prediction models, yet practical guidance on selecting appropriate methods remains limited. Existing analytical and simulation-based approaches often rely on restrictive assumptions and focus on mean-based criteria. We present and validate pmsims, an R package that uses Gaussian process surrogate modelling to provide a flexible and computationally efficient simulation-based framework for sample size determination across diverse prediction settings. Methods: We conducted a comprehensive simulation study with two aims. First, we compared three search engines implemented in pmsims: a Gaussian process-based adaptive method, a deterministic bisection method, and a hybrid approach, across binary, continuous, and survival outcomes. Second, we benchmarked the best-performing pmsims engine against existing analytical (pmsampsize) and simulation-based (samplesizedev) methods, evaluating recommended sample sizes, computational time, and achieved performance on large independent validation datasets. Results: The Gaussian process-based method consistently produced the most stable sample size estimates, particularly in low-signal, high-dimensional settings. In benchmarking, pmsims achieved performance close to prespecified targets across all outcome types, matching simulation-based approaches and outperforming analytical methods in more challenging scenarios. Conclusions: pmsims provides an efficient and flexible framework for principled sample size planning in clinical prediction modelling, requiring fewer model evaluations than non-adaptive simulation approaches.

翻译：背景：确定充足的样本量对于开发可靠且具有泛化能力的临床预测模型至关重要，然而关于如何选择合适方法的实践指南仍然有限。现有分析和模拟方法通常依赖严格假设且聚焦于均值标准。我们提出并验证了pmsims R包，该包利用高斯过程代理建模提供灵活且计算高效的模拟框架，适用于多样化预测场景下的样本量确定。方法：我们开展了两项综合模拟研究。首先，在二分类、连续和生存结局下比较了pmsims中三种搜索策略：基于高斯过程的适应性方法、确定性二分法及混合方法。其次，将最优pmsims引擎与现有分析（pmsampsize）和模拟（samplesizedev）方法进行基准测试，评估推荐样本量、计算时间及在大规模独立验证数据集上的性能表现。结果：基于高斯过程的方法持续生成最稳定的样本量估计值，尤其在低信号、高维场景中表现突出。基准测试中，pmsims在所有结局类型中均达到预设目标性能，与模拟方法水平相当，并在更具挑战性的场景中优于分析方法。结论：pmsims为临床预测建模中的原则性样本量规划提供了高效灵活的框架，所需模型评估次数低于非自适应模拟方法。