1 Department of Mathematics, Tamkang University, New Taipei City, Taiwan.
2 Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Stat Methods Med Res. 2019 Aug;28(8):2247-2257. doi: 10.1177/0962280218761493. Epub 2018 Feb 28.
Semiparametric transformation models, which include the Cox proportional hazards and proportional odds models as special cases, are popular in current practice of survival analysis owing to that, in contrast to parametric models, no assumption on the baseline distribution is required. Although sample size calculations for semiparametric survival analysis with right-censored data are available, no such calculation exits in literature for semiparametric analysis with current status data, where only an examination time and whether the event occurs prior to the examination are observable. We develop sample size calculation for semiparametric two-group comparison or regression analysis with current status data. The proposed formula can be readily implemented with given effect size, power level, covariate group proportions, covariate-specific examination (censoring) time distributions, and proportions of events observed in the control group at a few knot points in the study period. Simulation results show that the proposed sample size calculation is adequate in the sense that it leads to studies with empirical power very close to the planned power level. We illustrate practical applications of the proposal through examples from an animal tumorigenicity study and a cross-sectional survey on osteoporosis status in the elderly.
半参数转换模型，包括 Cox 比例风险和比例优势模型作为特例，由于与参数模型相比，不需要对基线分布进行假设，因此在当前的生存分析实践中很受欢迎。虽然对于右删失数据的半参数生存分析有样本量计算，但对于当前状态数据的半参数分析则没有这样的计算，因为只有检查时间和事件是否在检查前发生是可观察的。我们为当前状态数据的半参数两组比较或回归分析开发了样本量计算。给定效果大小、功率水平、协变量组比例、协变量特定检查（删失）时间分布以及研究期间对照组中观察到的事件比例，建议的公式可以很容易地实现。模拟结果表明，所提出的样本量计算是充分的，因为它导致的研究具有与计划功率水平非常接近的经验功率。我们通过动物致癌性研究和老年人骨质疏松症现状的横断面调查的实例说明了该建议的实际应用。
