ABSTRACT
Durations of any event, such as duration of hospitalization, is usually found to have a highly skewed distribution and incomplete values due to dropouts and limited follow-up. The usual methods of statistical analysis are, therefore, not applicable. The method of survival analysis is a nonparametric method and is designed to overcome these problems. Survival is a generic term and is used for any time-toevent data. The entire survival pattern at different points in time is studied by the Kaplan-Meier method under certain conditions. Logrank method is used to compare survival pattern in two or more groups. Hazard is the rate of occurrence of an event per unit of time and studied by Cox method. The concept of survival and all these methods of survival analysis are briefly discussed in this short note in a non-mathematical format for medical audience.
ABSTRACT
The survival data and the survival analysis are the data and analysis methods used to study the probability of survival. The survival data consist of a period from the juncture of a start event to the juncture of the end event (occurrence event). The period is called the survival period or survival time. In this way, the method of analysing the survival time of subjects and appropriately summarizing the degree of survival is called survival analysis. To understand and analyse survival analysis methods, researchers must be aware of some concepts. Concepts to be aware of in the survival analysis include events, censored data, survival period, survival function, survival curve and so on. This review focuses on the terms and concepts used in the survival analysis. It will also cover the types of survival data that should be collected and prepared when using actual survival analysis method and how to prepare them.
Subject(s)
Methods , Survival AnalysisABSTRACT
Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.
Subject(s)
Methods , Sample Size , Statistics as Topic , Survival AnalysisABSTRACT
Objective@#To analyze the heart rate changes and risk factors, as a result of high altitude.@*Methods@#Retrospective analysis of echocardiographic data of plateau workers at a railway maintenance company from 2006 to 2013. The survival curve method was used to analyze the abnormal rate of the heart. Kaplan-Meier method and Cox proportional hazards regression model were used to analyze the influencing factors.@*Results@#In the first occurrence of cardiac abnormalities, the main types of abnormalities were right atrium enlargement (53.47%) , right ventricle enlargement (17.36%) , and tricuspid regurgitation (16.67%) . Cox regression analysis showed that workplace altitude and first physical examination age are two influencing factors of cardiac abnormalities, and their relative risk was 1.661 and 1.039. At high altitudes (3 600~4 000 m) , nearly 40% of workers heart has not changed. But this adaptation does not observed in the ultra-high altitudes (≥4 000 m) .@*Conclusion@#There are individual differences in human adaptability to high altitude. We should take more stringent measures of health care for older people and those who work at more than 4000m. And we should abide by the rotation system for railways that are suitable for the plateau.
ABSTRACT
Background & objectives: Persistent infections with high-risk (HR) human papillomaviruses such as HPV 16, 18, 31, 33 and 45 have been identified as the major aetiological factor for cervical cancer. The clinical outcome of the disease is often determined by viral factors such as viral load, physical status and oncogene expression. The aim of the present study was to evaluate the impact of such factors on clinical outcome in HPV16 positive, locally advanced cervical cancer cases. Methods: One hundred and thirty two pretreatment cervical tumour biopsies were selected from patients undergoing radiotherapy alone (n=63) or concomitant chemo-radiation (n=69). All the samples were positive for HPV 16. Quantitative real time-PCR was carried out to determine viral load and oncogene expression. Physical status of the virus was determined for all the samples by the ratio of E2copies/E7copies; while in 73 cases, the status was reanalyzed by more sensitive APOT (amplification of papillomavirus oncogene transcripts) assay. Univariate analysis of recurrence free survival was carried out using Kaplan-Meier method and for multivariate analysis the Cox proportional hazard model was used. Results: The median viral load was 19.4 (IQR, 1.9- 69.3), with viral integration observed in 86 per cent cases by combination of the two methodologies. Both univariate and multivariate analyses identified viral physical status as a good predictor of clinical outcome following radiation treatment, with episomal form being associated with increased recurrence free survival. Interpretation & conclusions: The present study results showed that viral physical status might act as an important prognostic factor in cervical cancer.
ABSTRACT
El pronóstico del cáncer se valora determinando el porcentaje de pacientes que sobrevive al menos cinco años después del diagnóstico. Diferentes estudios muestran que, cuando el cáncer de cuello uterino es detectado y atendido en etapa temprana, por lo general se puede curar. El índice de supervivencia de cinco años para el cáncer cervical pre invasivo es del 100 por ciento, para el cáncer invasivo en etapa temprana es del 91% y en todas las etapas combinadas baja al 70%. La presente investigación de tipo retrospectivo correlacional se realizó con el propósito de establecer la asociación de los factores demográficos, clínicos y sociales con el tiempo de supervivencia de las mujeres diagnosticadas con cáncer invasivo de cuello uterino, reportadas en el registro poblacional de cáncer del municipio de Pasto, durante el período de 1998 a 2002 y observadas hasta 31 de diciembre de 2007. Los resultados muestran que el 45% de la población objeto de estudio tuvo una probabilidad de supervivencia a los 5 años después del diagnóstico. En el análisis multivariado se encontró relación significativa con las variables estadio clínico al momento del diagnóstico, procedencia y estado civil. Este estudio será de utilidad para el sector salud del municipio como fundamento para el planteamiento de políticas públicas y programas tendientes a mitigar el impacto de la enfermedad.
Cancer prognosis is determined by the number of patients who survive at least five years after the diagnosis. Different studies show that when the neck of the uterus cancer is detected and treated at an early stage, it is generally curable. The survival rate of five years for pre invasive cervical cancer is 100 per cent, for invasive cancer at an early stage is 91% and in all the combined stages, it lowers to 70%. The present retrospective correlational research was done with the purpose of establishing the association of the demographic, clinical and social factors with the women diagnosed with invasive cervical cancer's survival time, who were reported in the cancer population register in the municipality of Pasto during the period of 1998 to 2002 and observed until december 31, 2007. The results show that 45% of the studied population had a survival probability after five years of diagnosis. In the multivariate analysis, a significant relation among the clinical state at the moment of diagnosis, origin and marital status was found. This study will be useful for the health sector in the municipality of Pasto as a foundation for the approach of public policies and programs which tend to mitigate the disease impact.