Kaplan-meier Plotter
Kaplan-meier Plotter - Related Videos
01:24
Kaplan-Meier Approach
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2025
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
07:13
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Cited by 1 •
2025
This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction tools.
08:02
Combination Radiotherapy in an Orthotopic Mouse Brain Tumor Model
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Cited by 9 •
2012
The purpose of this article is to describe the use of an orthotopic glioblastoma model for chemoradiation studies. This article will go though cell processing, implanting, and radiotherapy of the mouse using an intracranial model.
07:34
Cuff-Free Suture-Based Cervical Model for Secondary Heterotopic Heart Transplantation to Evaluate Donor-Specific Immune Tolerance in Mice
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2026
This protocol presents a cuff-free, suture-based cervical heterotopic heart transplantation technique in mice, designed to enable the reliable placement of a second heterotopic cardiac graft for evaluating donor-specific immune tolerance under physiological conditions with minimal interference from foreign material.
06:50
A Protocol to Infect Caenorhabditis elegans with Salmonella typhimurium
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Cited by 1 •
2014
C. elegans has emerged as a new genetic model to study host-pathogen interactions. Here we describe a protocol to infect C. elegans with Salmonella typhimurium coupled with the double-strand RNAi interference technique to examine the role of host genes in defense against Salmonella infection.
FAQs
The selected expression threshold determines which patients enter the higher- or lower-expression groups. Changing that threshold can alter group membership, the shape of the survival curves, and the apparent strength of association with outcome. Because cutoff selection can influence conclusions, researchers should treat threshold-dependent results as evidence for prioritization rather than definitive biomarker validation.
The Kaplan-Meier estimator summarizes the probability that patients remain alive or disease-free across follow-up time for each expression-defined group. Its resulting curves show how outcomes develop over time rather than reducing the analysis to a single endpoint. This temporal view helps researchers examine whether groups differ consistently or only during particular portions of follow-up.
The log-rank test provides a statistical comparison between survival curves generated for the patient groups. It helps evaluate whether the observed separation in overall or disease-free survival is compatible with a difference between groups. Researchers should interpret its result alongside the plotted curves, cohort characteristics, and cutoff choice instead of treating the test alone as proof of clinical usefulness.
Cohort composition determines which patients and clinical outcomes contribute to the comparison, while the cutoff determines how molecular measurements are converted into groups. Differences in either feature can change the observed association between expression and survival. Consequently, a result from one cancer cohort or threshold may not automatically represent all patients or cancer types.
A typical workflow selects a molecular feature, chooses the relevant cancer type and clinical outcome, sets an expression threshold, and generates survival curves for the resulting patient groups. The analysis then includes a statistical comparison, such as the log-rank test, followed by interpretation of the plot and its limitations. Candidate findings can be prioritized for further validation.
The tool is useful when researchers want to screen candidate molecular features across cancer types and outcomes such as overall or disease-free survival. It supports hypothesis generation and biomarker prioritization by revealing features associated with patient prognosis. Because many features or settings may be examined, researchers should account for multiple testing before assigning strong significance to an apparent association.