Survival analysis (Kaplan-Meier curves): a method to predict the future
نویسندگان
چکیده
منابع مشابه
Survival analysis I: the Kaplan-Meier method.
The Kaplan-Meier (KM) method is used to analyze 'time-to-event' data. The outcome in KM analysis often includes all-cause mortality, but could also include other outcomes such as the occurrence of a cardiovascular event. The purpose of this article is to explain the basic concepts of the KM method, to provide some guidance regarding the presentation of the KM results and to discuss some importa...
متن کاملSurvival Analysis I: The Kaplan-Meier Method
The Kaplan-Meier (KM) method is used to analyze ‘time-toevent’ data. The outcome in KM analysis often includes allcause mortality, but could also include other outcomes such as the occurrence of a cardiovascular event. The purpose of this article is to explain the basic concepts of the KM method, to provide some guidance regarding the presentation of the KM results and to discuss some important...
متن کاملThe Kaplan Meier Estimate in Survival Analysis
The need for analyzing time to event data arises in a number of applied fields, such as epidemiology, public health and medicine [1]. ‘Time to event’ simply means the time from entry into a study until a subject has a particular outcome. The study that involves time to event can be a cohort study on a specific number of patients or participants which should be followed for a particular time per...
متن کاملEnhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves
BACKGROUND The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance ...
متن کاملAn Introduction to Survival Statistics: Kaplan-Meier Analysis
Authors' disclosures of potential conflicts of interest are found at the end of this article. S tudies of how patients respond to treatment over time are fundamentally important to understanding how therapies influence quality of life and progression of disease during survi-vorship. When investigators examine change over time in continuous variables (e.g., patient self-reports of pain, fatigue,...
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ژورنال
عنوان ژورنال: Arquivos Brasileiros de Oftalmologia
سال: 2020
ISSN: 0004-2749
DOI: 10.5935/0004-2749.20200036