Longitudinal biomarkers are frequently collected in clinical studies due to their strong association with time-to-event outcomes. While considerable progress has been made in methods for jointly modeling longitudinal and survival data, comparatively little attention has been paid to statistical design considerations, particularly sample size and power calculations, in genetic studies. Yet, appropriate sample size estimation is essential for ensuring adequate power and valid inference. Genetic variants may influence event risk through both direct effects and indirect effects mediated by longitudinal biomarkers. In this paper, we derive a closed-form sample size formula for testing the overall effect of a single nucleotide polymorphism within a joint modeling framework. Simulation studies demonstrate that the proposed formula yields accurate and robust performance in finite samples. We illustrate the practical utility of our method using data from the Diabetes Control and Complications Trial.

翻译：纵向生物标志物因其与时间至事件结局的强关联性，在临床研究中被频繁采集。尽管纵向数据与生存数据的联合建模方法已取得显著进展，但遗传研究中的统计设计考量——特别是样本量与功效计算——却相对缺乏关注。然而，恰当的样本量估计对于确保足够功效和有效推断至关重要。遗传变异可能通过直接影响以及由纵向生物标志物介导的间接影响共同作用于事件风险。本文推导了在联合建模框架下检验单核苷酸多态性总体效应的闭式样本量计算公式。模拟研究表明，所提公式在有限样本中能产生准确且稳健的性能。我们通过糖尿病控制与并发症试验的数据展示了该方法的实际应用价值。