Methods to Analyse Time‐to‐Event Data: The Kaplan‐Meier Survival Curve

D’Arrigo, Graziella; Leonardis, Daniela; Fusaro, Maria; Tripepi, Giovanni; Roumeliotis, Stefanos

doi:10.1155/2021/2290120

Cited by 32 publications

(22 citation statements)

References 14 publications

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“…According to the median m7G-related prognostic lncRNA expression value, all OSCC patients were divided into high m7G-related prognostic lncRNA expression group and low m7G-related prognostic lncRNA expression group, and then, survival analysis function was defined to compare whether there was statistical difference in survival rate between the two groups [ 31 ]. Results were visualized by survival curves [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Zhang

Mei

2022

Applied Bionics and Biomechanics

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Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

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Section: Methodsmentioning

confidence: 99%

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Zhang

Mei

2022

Applied Bionics and Biomechanics

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“…The “survival” R package plotted the Kaplan–Meier analysis of all STAD groups [ 15 ]. In addition, the plotted ROC curves were to determine the signature's specificity, as well as its sensitivity [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of a Metabolic Reprogramming-Associated Risk Model Related to Prognosis, Immune Microenvironment, and Immunotherapy of Stomach Adenocarcinoma

Zhao

Zhang

Sun

2022

Journal of Oncology

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Stomach adenocarcinoma (STAD) is one of the most common malignant digestive tumors. Metabolic reprogramming is an essential feature of tumorigenesis. The roles of metabolic reprogramming in STAD patients were investigated to explore the tumor immune microenvironment (TME) and potential therapeutic strategies. STAD samples’ transcriptomic and clinical data were collected from The Cancer Genome Atlas (TCGA) set and the GSE84437 set. The signature based on the metabolism-related genes (MRGs) was built using the Cox regression model to predict prognosis in STAD. Notably, this MRG-based signature (MRGS) accurately predicted STAD patients’ clinical survival in multiple datasets and could serve as an indicator independently. STAD patients with high scores on the MRGS were eligible for generating a type I/II interferon (IFN) response, according to a complete examination of the link between the MRGS and TME. Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS) analyses revealed that STAD patients with different MRGS scores had different reactions to immunotherapy. Consequently, assessing the pattern of these MRGs increases the understanding of TME features in STAD, hence directing the development of successful immunotherapy regimens.

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“…There are several methods of representing prostate cancer mortality. Actuarial survival curves generated by the Kaplan–Meier method are commonly used [ 12 , 13 ] but they may not adequately reflect changes in mortality rates with time since diagnosis. A second method is to estimate the ultimate probability of death.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Age on Prostate Cancer Survival

Clark

Vesprini

Narod

2022

Cancers

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It is not clear to what extent the age of diagnosis and the attained age impact on cancer mortality rates in men with newly diagnosed prostate cancer. We estimated annual prostate cancer mortality rates and 20-year survival rates according to the age of diagnosis, race, grade and time since diagnosis using data from the Surveillance, Epidemiology and End-Results (SEER) program. We identified 116,796 prostate cancer patients diagnosed between 1992 and 1997 and followed them for 20 years. There were 21,896 deaths from prostate cancer. We calculated actuarial survival rates and annual prostate cancer mortality rates by age of diagnosis and by tumor grade. The risk of a man dying of prostate cancer was 17% for men diagnosed before age 70 and was 21% for those diagnosed after age 70. The mean annual prostate cancer mortality rate calculated over the 20-year period post-diagnosis was 1.5%. The annual rate increased from 0.9% for those diagnosed below age 60 to 2.1% for those diagnosed above age 70. For men with Gleason score ≥ 7 prostate cancer, the annual prostate cancer mortality rate peaked 2–3 years after diagnosis and then declined. For men diagnosed with Gleason score ≤ 6 prostate cancer, the annual prostate cancer mortality rate continued to rise 20 years after diagnosis and peaked after age 85. This suggests that high-grade prostate cancers are aggressive from the outset, but that low-grade prostate cancers may enter a state of dormancy and reactivate as the patient ages.

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Methods to Analyse Time‐to‐Event Data: The Kaplan‐Meier Survival Curve

Cited by 32 publications

References 14 publications

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Identification of a Metabolic Reprogramming-Associated Risk Model Related to Prognosis, Immune Microenvironment, and Immunotherapy of Stomach Adenocarcinoma

The Effect of Age on Prostate Cancer Survival

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