PDF(Pubmed)
Abstract：
OBJECTIVE: To explore the applicable conditions of the Cox-TEL (Cox PH-Taylor expansion adjustment for long-term survival data) method for analysis of survival data that contain cured patients.
METHODS: The simulated survival data method based on Weibull distribution was used to simulate and generate the survival data with different cure rates, censored rates, and cure rate differences. The Cox-TEL method was used for analysis of the generated simulation data, and its performance was evaluated by calculating its type Ⅰ error and power.
RESULTS: Almost all the type Ⅰ error of the hazard ratios (HRs) obtained by the Cox-TEL method under different conditions were slightly greater than 0.05, and this method showed a good test power for estimating the HRs for data with a large sample size and a large difference in proportions (DPs). For the data of cured patients, the type Ⅰ error of the DPs obtained by the Cox-TEL method was well around 0.05, and its test power was robust in most of the scenarios.
CONCLUSIONS: The Cox-TEL method is effective for analyzing data of uncured patients and obtaining reliable HRs for most of the survival data with a sample size, a low censored rates, and a large difference in cure rates. The method is capable of accurately estimating the DPs regardless of the sample size, censored rates, or the cure rates.
METHODS: The simulated survival data method based on Weibull distribution was used to simulate and generate the survival data with different cure rates, censored rates, and cure rate differences. The Cox-TEL method was used for analysis of the generated simulation data, and its performance was evaluated by calculating its type Ⅰ error and power.
RESULTS: Almost all the type Ⅰ error of the hazard ratios (HRs) obtained by the Cox-TEL method under different conditions were slightly greater than 0.05, and this method showed a good test power for estimating the HRs for data with a large sample size and a large difference in proportions (DPs). For the data of cured patients, the type Ⅰ error of the DPs obtained by the Cox-TEL method was well around 0.05, and its test power was robust in most of the scenarios.
CONCLUSIONS: The Cox-TEL method is effective for analyzing data of uncured patients and obtaining reliable HRs for most of the survival data with a sample size, a low censored rates, and a large difference in cure rates. The method is capable of accurately estimating the DPs regardless of the sample size, censored rates, or the cure rates.
摘要：
目的：探讨Cox-TEL（CoxPH-Taylor扩展调整长期生存数据）方法分析包含治愈患者的生存数据的适用条件。
方法：采用基于威布尔分布的模拟生存数据方法，模拟生成不同治愈率的生存数据，审查率，和治愈率差异。Cox-TEL方法用于分析生成的仿真数据，并通过计算其Ⅰ型误差和功率来评估其性能。
结果：Cox-TEL方法在不同条件下得到的风险比(HRs)的Ⅰ类误差几乎都略大于0.05，该方法对样本量大、比例差异(DPs)大的数据估计HRs具有良好的检验能力。对于治愈患者的数据，通过Cox-TEL方法获得的DPs的Ⅰ型误差在0.05左右，其测试能力在大多数情况下都是稳健的。
结论：Cox-TEL方法可有效分析未治愈患者的数据，并在样本量的情况下获得大多数生存数据的可靠HR，低审查率，治愈率差异很大。无论样本大小如何，该方法都能够准确估计DP，审查率，或治愈率。
方法：采用基于威布尔分布的模拟生存数据方法，模拟生成不同治愈率的生存数据，审查率，和治愈率差异。Cox-TEL方法用于分析生成的仿真数据，并通过计算其Ⅰ型误差和功率来评估其性能。
结果：Cox-TEL方法在不同条件下得到的风险比(HRs)的Ⅰ类误差几乎都略大于0.05，该方法对样本量大、比例差异(DPs)大的数据估计HRs具有良好的检验能力。对于治愈患者的数据，通过Cox-TEL方法获得的DPs的Ⅰ型误差在0.05左右，其测试能力在大多数情况下都是稳健的。
结论：Cox-TEL方法可有效分析未治愈患者的数据，并在样本量的情况下获得大多数生存数据的可靠HR，低审查率，治愈率差异很大。无论样本大小如何，该方法都能够准确估计DP，审查率，或治愈率。
