Li Liang, Hu Bo, Kattan Michael W
Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave., JJN3, Cleveland, OH, 44195, USA,
Lifetime Data Anal. 2014 Apr;20(2):316-34. doi: 10.1007/s10985-013-9279-z. Epub 2013 Sep 24.
Patients receiving radical prostatectomy are at risk of metastasis or prostate cancer related death, and often need repeated clinical evaluations to determine whether additional adjuvant or salvage therapies are needed. Since the prostate cancer is a slowly progressing disease, and these additional therapies come with significant side effects, it is important for clinical decision making purposes to estimate a patient's risk of cancer metastasis, in the presence of a competing risk by death, under the hypothetical condition that the patient does not receive any additional therapy. In observational studies, patients may receive additional therapy by choice; the time to metastasis without any therapy is often a potential outcome and not always observed. We study the competing risks model of Fine and Gray (J Am Stat Assoc, 94:496-509, 1999) with adjustment for treatment choice by inverse probability censoring weighting (IPCW). The model can be fit using standard software for partial likelihood with double IPCW weights. The proposed methodology is used in a prostate cancer study to predict the post-prostatectomy cumulative incidence probability of cancer metastasis without additional adjuvant or salvage therapies.
接受根治性前列腺切除术的患者有发生转移或前列腺癌相关死亡的风险，并且通常需要反复进行临床评估，以确定是否需要额外的辅助治疗或挽救性治疗。由于前列腺癌是一种进展缓慢的疾病，而这些额外的治疗会带来显著的副作用，因此出于临床决策目的，在假设患者不接受任何额外治疗的情况下，估计患者在存在死亡竞争风险时发生癌症转移的风险非常重要。在观察性研究中，患者可能会自行选择接受额外治疗；未接受任何治疗的转移时间通常是一个潜在结果，并不总是能观察到。我们研究了Fine和Gray的竞争风险模型（《美国统计协会杂志》，94:496 - 509，1999年），并通过逆概率删失加权（IPCW）对治疗选择进行调整。该模型可以使用带有双重IPCW权重的部分似然标准软件进行拟合。所提出的方法用于一项前列腺癌研究，以预测前列腺切除术后在不进行额外辅助治疗或挽救性治疗的情况下癌症转移的累积发病率概率。
