An overview on standard statistical methods for assessing exposure-outcome link in survival analysis (Part II): the Kaplan-Meier analysis and the Cox regression method

2012 ◽  
Vol 24 (3) ◽  
pp. 203-206 ◽  
Author(s):  
Samar Abd ElHafeez ◽  
Claudia Torino ◽  
Graziella D’Arrigo ◽  
Davide Bolignano ◽  
Fabio Provenzano ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Statistical Methods ◽  
Cox Regression ◽  
Regression Method ◽  
Kaplan Meier

scholarly journals Survival Analysis and Determinants of The First Birth Interval in Indonesia

CAUCHY ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 55
Author(s):  
Alfensi Faruk ◽  
Endro Setyo Cahyono ◽  
Ning Eliyati
Keyword(s):  
Survival Analysis ◽  
Educational Level ◽  
Birth Interval ◽  
Cox Regression ◽  
Wealth Index ◽  
First Birth ◽  
Factors Affecting ◽  
Kaplan Meier ◽  
Censored Observations ◽  
Socioeconomic And Demographic Factors

<p class="Abstract">The first birth interval is one of the indicators of women’s fertility rate. Because in most cases the first birth interval contains censored observations, the only appropriate statistical method to handle such data is survival analysis. The main objective of this study is to analyze several socioeconomic and demographic factors that affect the first birth interval in Indonesia using the univariate and multivariate survival analysis, that is Kaplan-Meier method and Cox regression model, respectively. The sample is obtained from 2012 Indonesian Demographic and Health Survey (IDHS) and consists of 28242 ever married women aged 15-49 at the time of interview. The results show that age at the first birth, women's educational level, husband’s educational level, contraceptive knowledge, wealth index, and employment status are the significant factors affecting the first birth interval in Indonesia.</p>

scholarly journals Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

2020 ◽  
Vol 9 (11) ◽  
pp. 3693
Author(s):  
Ching-Fu Weng ◽  
Chi-Jung Huang ◽  
Mei-Hsuan Wu ◽  
Henry Hsin-Chung Lee ◽  
Thai-Yen Ling
Keyword(s):  
Overall Survival ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Early Stage ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Mesenchymal Transition ◽  
Kaplan Meier

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

scholarly journals The what, when and how of orthopaedic registers: an introduction into register-based research

2019 ◽  
Vol 4 (6) ◽  
pp. 337-343 ◽  
Author(s):  
Claus Varnum ◽  
Alma Bečić Pedersen ◽  
Per Hviid Gundtoft ◽  
Søren Overgaard
Keyword(s):  
Survival Analysis ◽  
Selection Bias ◽  
Survival Data ◽  
Cox Regression ◽  
Research Question ◽  
Cox Regression Analysis ◽  
Information Bias ◽  
Kaplan Meier ◽  
Routinely Collected Health Data ◽  
Competing Events

Establishment of orthopaedic registers started in 1975 and many registers have been initiated since. The main purpose of registers is to collect information on patients, implants and procedures in order to monitor and improve the outcome of the specific procedure. Data validity reflects the quality of the registered data and consists of four major aspects: coverage of the register, registration completeness of procedures/patients, registration completeness of variables included in the register and accuracy of registered variables. Survival analysis is often used in register studies to estimate the incidence of an outcome. The most commonly used survival analysis is the Kaplan–Meier survival curves, which present the proportion of patients who have not experienced the defined event (e.g. death or revision of a prosthesis) in relation to the time. Depending on the research question, competing events can be taken into account by using the cumulative incidence function. Cox regression analysis is used to compare survival data for different groups taking differences between groups into account. When interpreting the results from observational register-based studies a number of factors including selection bias, information bias, chance and confounding have to be taken into account. In observational register-based studies selection bias is related to, for example, absence of complete follow-up of the patients, whereas information bias is related to, for example, misclassification of exposure (e.g. risk factor of interest) or/and outcome. The REporting of studies Conducted using Observational Routinely-collected Data guidelines should be used for studies based on routinely-collected health data including orthopaedic registers. Linkage between orthopaedic registers, other clinical quality databases and administrative health registers may be of value when performing orthopaedic register-based research. Cite this article: EFORT Open Rev 2019;4 DOI: 10.1302/2058-5241.4.180097

scholarly journals Kaplan–Meier survival analysis and Cox regression analyses regarding right ventricular septal pacing: Data from Japanese pacemaker cohort

Data in Brief ◽  
2016 ◽  
Vol 8 ◽  
pp. 1303-1307 ◽  
Author(s):  
Akira Mizukami ◽  
Yuya Matsue ◽  
Yoshihisa Naruse ◽  
Shinya Kowase ◽  
Kenji Kurosaki ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Right Ventricular ◽  
Cox Regression ◽  
Regression Analyses ◽  
Kaplan Meier ◽  
Septal Pacing

scholarly journals Association of Hypertension and Hypertriglyceridemia on Incident Hyperuricemia: An 8-year Prospective Cohort Study

2020 ◽  
Author(s):  
Yuan Zhang ◽  
Miaomiao Zhang ◽  
Xiawen Yu ◽  
Fengjiang Wei ◽  
Chen Chen ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cohort Study ◽  
Survival Analysis ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Cox Regression ◽  
Target Organ Damage ◽  
Organ Damage ◽  
Combined Effect ◽  
Kaplan Meier

Abstract BackgroundHypertension and high triglyceride are two of the most important risk factors for hyperuricemia. Epidemiological records show that hypertension and dyslipidemia often coexist and may significantly increase the risk of target organ damage. However, their combined effect on incident hyperuricemia is poorly understood. Thus, we aimed to investigate the separate and combined effect of hypertension and hypertriglyceridemia on the incidence of hyperuricemia.MethodsA prospective cohort study of 6424 hyperuricemia-free participants aged 20 to 94 years between August 2009 and October 2017 was performed at Tianjin General Hospital of China. Participants were categorized into four groups by combining hypertension and hypertriglyceridemia status at baseline. The restricted cubic spline fitting Cox regression model was used to evaluate the relationship between blood pressure and triglyceride and hyperuricemia. Cox regression models were performed to calculate hazard ratios (HRs) and 95% confident intervals (CIs) to estimate baseline factors and their association with the incidence of hyperuricemia. A Kaplan–Meier survival analysis was performed to compare the incidence of hyperuricemia among subjects in each separate and combined hypertension and hypertriglyceridemia group.ResultsDuring the 8-year follow-up period, 1,259 subjects developed hyperuricemia (20.6%). There existed positive relationships between blood pressure and triglyceride levels and hyperuricemia. This risk factor arising from a combination of the two (HR, 3.02; 95% CI: 2.60-3.50) is greater than that from hypertension (HR, 1.48; 95% CI: 1.28-1.71) or hypertriglyceridemia (HR, 1.84; 95% CI: 1.55-2.18) separately. The Kaplan–Meier survival analysis indicated that combined effect of hypertension and hypertriglyceridemia may predict higher onset of hyperuricemia.ConclusionThe combined effect of hypertension and hypertriglyceridemia on the risk of hyperuricemia is much stronger than that by hypertension or hypertriglyceridemia separately. Hypertension combined with hypertriglyceridemia may be an independent and powerful predictor for hyperuricemia.

scholarly journals Survival analysis of renal patients underwent transplantation in Kyrgyz Republic and various countries by 10 years follow-up

2019 ◽  
Vol 3 (Issue 4) ◽  
pp. 243
Author(s):  
Zhanybek Gaibyldaev ◽  
Zhamalbek Ashimov ◽  
Damirbek Abibillaev ◽  
Fuat Kocyigit
Keyword(s):  
Survival Analysis ◽  
Heart Surgery ◽  
Cox Regression ◽  
Statistical Significance ◽  
Survival Function ◽  
Strong Impact ◽  
Kyrgyz Republic ◽  
Cox Regression Analysis ◽  
Kaplan Meier

In our study we conducted survival analysis of 204 patients visited Scientific-Research Institute of Heart Surgery and Organs transplantation and who underwent renal transplantation in Kyrgyzstan and other Eurasian countries between 2005 and 2016 years (age range: 9-71 years, mean: 38.21 (12.74) years, median: 34.0 (0.89) years; gender: 142 male (69.6%)). During follow-up period, mortality event was observed in 16 (7.84%) patients. Survival function probabilities of patients and rational risk factors of survival functions were evaluated by Kaplan-Meier and Cox regression analyses, respectively. According to Kaplan-Meier results survival probabilities calculated for 1st year: 0.96 (0.014), for 3rd year: 0.94 (0.018), for 5th year: 0.86 (0.04), for 7th year: 0.75 (0.10). Among age groups 28-39 age ranges prevailed by 11 patients. Nevertheless, that difference did not show statistical significance: p˃0.322. The intensity of transplantation also analyzed according to years, which revealed increasing in numbers of operations by time. For instance, when in 2006 only two cases were registered in our center, but numbers of transplanted patients reached up to 48 in 2015. The association of mortality states and years of transplantation found significantly by Kaplan-Meier test (Breslow p˂0.001). The survival analysis was compared according to countries and revealed significant results (Breslow p˂0.05). From other factors influencing mortality, sex did not show strong impact on survival by Kaplan-Meier analysis, but significant association was found by Cox regression analysis.

scholarly journals The Neutrophil-to-lymphocyte Ratio at the Time of Admission: A New Prognostic Indicator for Hospital Mortality of Trauma Patients

2021 ◽  
Author(s):  
Hamed Fouladseresht ◽  
Shahram Bolandparvaz ◽  
Hamid Reza Abbasi ◽  
Hossein Abdolrahimzadeh Fard ◽  
Shahram Paydar
Keyword(s):  
Survival Analysis ◽  
Hospital Mortality ◽  
Regression Models ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Neutrophil To Lymphocyte Ratio ◽  
Trauma Patients ◽  
Lymphocyte Ratio ◽  
Kaplan Meier ◽  
Time Of Admission

The elevated neutrophil-to-lymphocyte ratio (NLR) is associated with poor clinical outcomes, especially in pro-inflammatory states such as surgical injuries and severe hemorrhages. Therefore, it was hypothesized whether NLR value at the time of admission could be a prognostic indicator of hospital mortality in trauma patients. This retrospective cohort study was conducted on 865 trauma patients referred to Rajaee Hospital between April 2016 and July 2019. The NLR value was calculated at the time of admission, and receiver operating characteristics (ROC) curve analysis was used to determine the cut-off point value of admission NLR related to hospital mortality of trauma patients. Furthermore, Kaplan-Meier survival analysis and Cox regression models have been applied to determine the effectiveness and prognostic potential of the admission NLR in the hospital mortality of trauma patients. The median age of the trauma patients was 32 years with an interquartile range (IQR) of 23 to 48 years, and most of them were male (83.9%). Also, trauma patients had a median injury severity score (ISS) of 9 (IQR=4-16) and a median Glasgow coma scale (GCS) of 14 (IQR=9-15). The cut-off value for admission NLR was 5.27 (area under the curve: 0.642, 95%CI: 0.559-0.726, p=0.001). In Kaplan-Meier survival analysis, the admission NLR>5.27 was an indicator of hospital mortality in trauma patients (p=0.001). Multivariate Cox regression models demonstrated that trauma patients with an admission NLR>5.27 had a 2.33-fold risk of hospital mortality (hazard ratio=2.33, 95%CI: 1.02-5.38, p=0.041). Furthermore, the admission NLR>5.27 was associated with a higher risk of hospital mortality in trauma patients with age≥65 years, systolic blood pressure≤90 mmHg, blood potassium>4.5 mmol/L, blood sodium>144 mEq/L, blood potential hydrogen (pH)≤7.28, GCS≤8, ISS>24 and blood base excess≤-6.1 mEq/L. The NLR value greater than 5.27 at the time of admission was associated with poorer outcomes, and it can be considered an independent prognostic indicator of hospital mortality in trauma patients.

scholarly journals Expression Status and Prognostic Value of m6A RNA Methylation Regulators in Lung Adenocarcinoma

2020 ◽  
Author(s):  
Rui Wang ◽  
Zian Feng ◽  
Jie Hu ◽  
Xiaodong He ◽  
Zuojun Shen
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
Prediction Efficiency

Abstract Background: N6-methyladenosine (m6A) RNA modification is the most abundant modification method in mRNA, and it plays an important role in the occurrence and development of many cancers. However, data on the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) are still lacking. This paper mainly discusses the role of m6A RNA methylation regulators in LUAD, to identify novel prognostic biomarkers.Methods: The gene expression data of 19 m6A methylation regulator in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm were performed to construct a risk signature and evaluated its prognostic prediction efficiency by using the receiver operating characteristic (ROC) curve. The risk score of each patient was calculated according to the risk signature, and LUAD patients were divided into high-risk group and low-risk group. Kaplan-Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of risk signature. Finally, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the differential signaling pathways and cellular processes between the two groups.Results: The expression of 15 m6A RNA methylation regulators in LUAD tissues was significantly different than that in normal tissues. YTHDF3, YTHDF2, KIAA1429, HNRNPA2B1, RBM15, METTL3, HNRNPC, YTHDF1, IGF2BP2, IGF2BP3, IGF2BP1 were significantly up-regulated in LUAD, and the expressions of FTO, ZC3H13, WTAP, and METL14 were significantly down-regulated. We selected IGF2BP1, HNRNPC, and HNRNPA2B1 to construct the risk signature. ROC curve indicated the area under the curve (AUC) was 0.659, which means the risk signature had a good prediction efficiency. The results of Kaplan-Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD.Conclusions: The m6A RNA methylation regulators IGF2BP1, HNRNPC, and HNRNPA2B1 have a significant correlation with the clinicopathological characteristics of LUAD, which may be a promising prognostic feature and clinical treatment target.

Survival analysis

2013 ◽  
Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry
Keyword(s):  
Survival Analysis ◽  
Cox Regression ◽  
Rank Test ◽  
Log Rank Test ◽  
Kaplan Meier

Chapter 11 covers survival analysis, and includes Kaplan–Meier estimates, the log rank test, Cox regression, and further reading.

Survival analysis

2017 ◽  
Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry ◽  
Raymond R. Balise
Keyword(s):  
Survival Analysis ◽  
Cox Regression ◽  
Logrank Test ◽  
Kaplan Meier ◽  
Hazard Ratios

Chapter 11 covers survival analysis, and includes Kaplan–Meier estimates, and the logrank test. Cox regression is used to do multifactorial analyses with results reported as adjusted hazard ratios. The chapter includes analyses using Stata, SAS, SPSS, and R.

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