Statistics Department, University College Cork, Cork, Ireland.
Stat Med. 2010 Oct 15;29(23):2399-409. doi: 10.1002/sim.3992.
Xenograft trials allow tumor growth in human cell lines to be monitored over time in a mouse model. We consider the problem of inferring the effect of treatment combinations on tumor growth. A piecewise quadratic model with flexible phase change locations is proposed to model the effect of change in therapy over time. Each piece represents a growth phase, with phase changes in response to change in treatment. Piecewise slopes represent phase-specific (log) linear growth rates and curvature parameters represent departure from linear growth. Trial data are analyzed in two stages: (i) subject-specific curve fitting (ii) analysis of slope and curvature estimates across subjects. A least-squares approach with penalty for phase change point location is proposed for curve fitting. In simulation studies, the method is shown to give consistent estimates of slope and curvature parameters under independent and AR (1) measurement error. The piecewise quadratic model is shown to give excellent fit (median R(2)=0.98) to growth data from a six armed xenograft trial on a lung carcinoma cell line.
异种移植试验允许在小鼠模型中随时间监测人细胞系中的肿瘤生长。我们考虑推断治疗组合对肿瘤生长影响的问题。提出了一种具有灵活相变位置的分段二次模型来模拟治疗随时间变化的效果。每个部分代表一个生长阶段，随着治疗的变化而发生相变。分段斜率表示特定阶段的（对数）线性增长率，曲率参数表示与线性生长的偏离。试验数据分两个阶段进行分析：（i）个体特异性曲线拟合；（ii）跨个体的斜率和曲率估计分析。针对曲线拟合提出了一种具有相变点位置惩罚的最小二乘方法。在模拟研究中，该方法在独立和 AR（1）测量误差下表现出对斜率和曲率参数的一致估计。分段二次模型对来自肺癌细胞系的六臂异种移植试验的生长数据具有极好的拟合效果（中位数 R(2)=0.98）。
