1.生存分析(survival analysis)
將終點時間的出現與否和達到終點所經歷的時間結合起來分析的一類統計分析方法。
兩個主要特點
考慮了每個研究物件出現某一結局所經歷的時間長短
考慮了事件的觀察時間和隨訪時間
2.Kaplan-Meier法
採用乘積極限法(product-limit estimates)來估計生存率,同時還可以對一個一項因素進行檢驗。
通常以生存時間為橫軸,生存率為縱軸,繪製而成的連續型的階梯型曲線,用以說明生存時間與生存率之間的關係。
3.刪失資料的定義
刪失資料( censored data)由於某種原因被截斷了的資料。
按刪失方式分,分為
第一類刪失、
第二類刪失
隨機刪失;
按時間延伸方向分,分為
左刪失、
右刪失
區間刪失。
SPSS繪製Kaplan-Merier生存曲線
1。分析資料
10例肺癌腫瘤小於5cm的患者和12例肺癌腫瘤大於或等於5cm的患者的生存時間(月)
註釋:資料為隨機生成資料,僅為示範用。
思考:標紅的資料是什麼。
2。資料錄入
2。1變數檢視
名稱:status
名稱:group
如圖所示
2。2資料檢視
如圖所示
3.SPSS分析流程
3.1分析---生存函式(S)---Kaplan-Meier
點選後,如圖
將左側資料點進右側相應的位置,如圖
點選定義事件
輸入1後,點選繼續
點選比較因子——-檢驗統計選擇
對數等級
點選
選項---勾選
生存分析表
平均值和中位數生存時間
生存函式
3。結果解釋
根據此表,我們可以得到各組生存時間的均數和中位數
結果顯示兩種腫瘤患者的生存曲線分佈差別無統計學意義。
Graphpad繪製Kaplan-Meier生存曲線
2。開啟軟體——-選擇
Survival選項卡
輸入資料,如圖所示
點選左側
Graphs選項卡
得到figure如圖所示
思考:為什麼小於號會出現正方形方框。
SCI寫作範文
最好的學習寫作的方法是照貓畫虎,大家注意積累,消化吸收和運用,不要畫虎不成反類犬
這裡列舉五個關於Kaplan-Meier curve 的範例,包含
figure legend
figure
results
第一篇:獨中一元
Figure legend
The Kaplan-Meier curve of the RFS between the high-risk and low-risk groups stratified by the median risk score in The Cancer Genome Atlas cohort。
note:recurrence-free survival (RFS)
Figure
Results
To confirm the predictive ability of the lncRNA-signature, validation analysis was
carried out in a group of 337 HCC patients from the TCGA project。
The whole validation group was divided into high-risk and low-risk groups accordingly in the discovery dataset。
The median score (0。0357) of the whole validation group was adopted as the cut-off value。
Survival analysis showed great performance of the risk model in stratifying high-risk and low-risk patients with a log-rank P-value being less than 0。001 (HR = 1。807, 95%CI: 1。329-2。457) (Figure 2C)。
note:使用的TCGA資料庫中的資料,具體操作咱們有機會再嘮
第二篇:梅開二度
Figure
Figure legend
A。The Kaplan–Meier curves for low-risk and high-risk groups。
Results
Kaplan-Meier curve demonstrated that patients in the high-risk group had an increased risk of death compared to the low-risk group。
第三篇:帽子戲法
Figure
Figure legend
Kaplan-Meier curve of the high-risk and low-risk groups。
Results
The Kaplan-Meier curve (Figure 2B) showed that the OS of the high-risk group was significantly shorter than that of the low-risk group (p<0。05)。
第四篇:大四喜
Figure
Figure legend
The Kaplan–Meier curves along with the Wilcoxon test were used to visualize and compare the OS of the low-risk versus high-risk groups in the training cohort (N = 307)
(B)
and the validation cohort (N = 154)
(C)
。 。 Here‘low-risk (57/153)’ refers to that a total of 153 patients in the low-risk group, in which 57 with last clinical status ‘death’, and ‘low-risk (83/154)’ refers to that a total of 154 patients in the high-risk group, in which 83 with last clinical status ‘death’。 It can be concluded that higher risk scores are significantly associated with worse OS (p<0。001)。
第五篇:五子登科
Figure
Figure Legend
Kaplan–Meier analysis of the overall survival of 307 patients stratified by PLOD2 mRNA expression above the median level or below the median level using cBioPortal。
Rsults
Kaplan–Meier curves of overall survival stratified by PLOD2 mRNA levels in this dataset revealed that high PLOD2 expression was significantly associated with decreased overall survival (p = 0。0751)(Fig。 7c)。
note:使用的cBioPortal資料庫中的資料,具體操作咱們下回再嘮。
很多資料庫都可以點點滑鼠直接生成Kaplan–Meier analysis of the overall survival ,比如第五篇中使用的
cBioPortal
資料庫。
比如之前介紹的The Human Protein Atlas資料庫,連結如下