【摘要】： 在腫瘤臨床試驗中,由于病人總體的異質性,有些對處理敏感的患者不會出現(xiàn)任何癌癥癥狀或跡象,則他們被認為是“長期生存者(long term survivors)”或治愈者(cured individuals),即在充足的隨訪時間內,這類個體不會發(fā)生某一特定的終點事件,通常具有較長的刪失生存時間。 長期生存者意味著直到研究結束他們也不發(fā)生或根本就不會發(fā)生終點事件(死亡、疾病的復發(fā)等)。對這種資料的分析,首先要判斷資料中是否有長期生存者存在?如果資料沒有長期生存者,或者沒有證據(jù)證實有長期生存者存在,則采用傳統(tǒng)生存分析方法;若有證據(jù)證實資料中有長期生存者存在,且隨訪充足,采用傳統(tǒng)分析方法不恰當,因為它將長期生存者都作為截尾觀察對象來處理,顯然這是不合理的。盲目采用傳統(tǒng)生存分析方法,有可能造成結果解釋不合理,甚至得到相反的結論。 本文介紹適用于長期生存者資料的統(tǒng)計模型——參數(shù)混合模型,包括指數(shù)混合模型、Weibull混合模型、Burr XII混合模型。這三種模型在分析長期生存者資料中都有廣泛應用。我們利用擬合優(yōu)度檢驗選出擬合實際資料的最佳模型,并用相應的模型作進一步分析。參數(shù)估計采用最大似然法,通過軟件S-PLUS6.0來實現(xiàn)。 通過實例分析證實,對有長期生存者存在的資料,若采用傳統(tǒng)生存分析方法,由于不能很好地利用資料所提供的信息,得出的結論不僅不全面,甚至是相反的;如果采用適于長期生存者資料的參數(shù)混合模型分析,可得到全面、正確的結論,從不同側面提供更多的信息,能有效合理地指導臨床治療,進而彌補和完善了經(jīng)典生存分析方法應用之不足。隨著醫(yī)療技術的不斷提高,越來越多的臨床腫瘤資料屬于長期生存者資料。本文介紹的適用于長期生存者資料的統(tǒng)計分析模型適用性和實用性都很強,值得推薦。
[Abstract]:In cancer clinical trials, because of the overall heterogeneity of patients, some patients who are sensitive to treatment do not show any cancer symptoms or signs, so they are considered to be "long-term survivors (long term survivors)" or healing (cured individuals), That is, in sufficient follow-up time, such individuals do not have a specific end-point event, and usually have a longer censored survival time. Long-term survivors mean that they do not occur or never end-point events (death, relapse of disease, etc.) until the end of the study. In the analysis of this kind of data, we should first determine whether there are long-term survivors in the data. If there is no long-term survival in the data or there is no evidence to confirm the existence of long-term survival, the traditional survival analysis method is used. If there is evidence that there are long-term survivors in the data, and the follow-up is sufficient, the traditional analytical method is not appropriate because it treats the long-term survivors as amputation observation objects, which is obviously unreasonable. Blind use of traditional survival analysis may lead to unreasonable interpretation of the results and even to the opposite conclusion. In this paper, we introduce a statistical model for long-term survivor data-parametric hybrid model, including exponential mixed model and Weibull hybrid model, Burr XII hybrid model. These three models are widely used in the analysis of long-term survival data. We use the goodness-of-fit test to select the best model to fit the actual data, and use the corresponding model to make further analysis. The maximum likelihood method is used to estimate the parameters, and the software S-PLUS6.0 is used to realize the estimation. Through the analysis of examples, it is proved that if the traditional survival analysis method can not make good use of the information provided by the data, the conclusion is not comprehensive, even the opposite, if the traditional survival analysis method is used. If the parameter mixed model is used to analyze the long-term survival data, a comprehensive and correct conclusion can be obtained, and more information can be provided from different sides, which can guide the clinical treatment effectively and reasonably. Furthermore, it makes up for and consummates the deficiency of the classical survival analysis method. With the continuous improvement of medical technology, more and more clinical tumor data belong to long-term survival data. The statistical analysis model for long-term survival data introduced in this paper is of great applicability and practicability, and is worthy of recommendation.
【學位授予單位】：山西醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2008
【分類號】：R311

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