Parametric assumptions such as exponential distribution are commonly used in clinical trial design and analysis. However, violation of distribution assumptions can introduce biases in sample size and power calculations. Piecewise exponential (PWE) hazard model partitions the hazard function into segments each with constant hazards and is easy for interpretation and computation. Due to its piecewise property, PWE can fit a wide range of survival curves and accurately predict the future number of events and analysis time in event-driven clinical trials, thus enabling more flexible and reliable study designs. Compared with other existing approaches, the PWE model provides a superior balance of flexibility and robustness in model fitting and prediction. The proposed PWEXP package is designed for estimating and predicting PWE hazard models for right-censored data. By utilizing well-established criteria such as AIC, BIC, and cross-validation log-likelihood, the PWEXP package chooses the optimal number of change-points and determines the optimal position of change-points. With its particular goodness-of-fit, the PWEXP provides accurate and robust hazard estimation, which can be used for reliable power calculation at study design and timeline prediction at study conduct. The package also offers visualization functions to facilitate the interpretation of survival curve fitting results.

翻译：指数分布等参数假设常用于临床试验设计与分析。然而，违反分布假设可能导致样本量和统计功效计算的偏倚。分段指数风险模型将风险函数划分为若干段，每段具有恒定风险，易于解释和计算。由于其分段特性，PWE能够拟合广泛的生存曲线，并准确预测事件驱动型临床试验中未来事件数量和分析时间，从而实现更灵活可靠的研究设计。与现有其他方法相比，PWE模型在模型拟合和预测的灵活性与稳健性之间实现了更优平衡。本文提出的PWEXP包专用于右删失数据的PWE风险模型估计与预测。通过利用AIC、BIC和交叉验证对数似然等成熟准则，PWEXP包可自动选择最优变点数量并确定变点的最优位置。凭借其良好的拟合优度，PWEXP可提供准确稳健的风险估计，从而在研究设计阶段实现可靠的功效计算，在试验执行阶段实现准确的时间线预测。该包还提供可视化功能，以辅助解释生存曲线拟合结果。