Statistical Survival Models
Statistical Survival Models - Related Videos
02:19
Modeling Aeromonas Pathogenesis in C. elegans Using a Survival Assay
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2026
Source: Chen, Y., et al. Evaluating Virulence and Pathogenesis of Aeromonas Infection in a Caenorhabditis elegans Model. J. Vis. Exp. (2018)This video demonstrates a survival assay using C. elegans to model Aeromonas pathogenesis, where oral ingestion of the bacteria leads to intestinal infection and mortality, enabling quantitative assessment of bacterial virulence in a live host system.
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
08:51
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Cited by 2 •
2019
Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.
05:18
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
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Cited by 6 •
2016
Rotifers are microscopic zooplankton used as models in ecotoxicological and aging studies. Here we provide a protocol for powerful and reproducible measurement of survival time in Brachionus rotifers. Synchronization of culture conditions over several generations is of particular importance because maternal condition affects life history of offspring.
09:07
An Optic Nerve Crush Injury Murine Model to Study Retinal Ganglion Cell Survival
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Cited by 64 •
2011
This protocol shows how to retrogradely label retinal ganglion cells, and how to subsequently make an optic nerve crush injury in order to analyze retinal ganglion cell survival and apoptosis. It is an experimental disease model for different types of optic neuropathy, including glaucoma.
FAQs
The survival function summarizes the probability of remaining event-free as follow-up progresses, whereas the hazard function focuses on the event rate among individuals who are still at risk at a given point. Considering both helps distinguish overall event-free probability from the rate at which events occur within the remaining population, improving interpretation of event patterns.
Right-censored observations contribute information up to the time follow-up ends or the subject leaves observation, but they do not indicate that the event occurred at that time. Likelihood-based approaches and risk-set methods incorporate this partial information while preserving the distinction between known event times and incomplete follow-up, reducing inappropriate handling of unobserved outcomes.
Covariates connect explanatory factors with survival or hazard quantities, allowing analysts to examine how those factors relate to the timing and rate of events. This supports comparisons between groups while accounting for measured characteristics and can help estimate covariate effects, describe differences in event patterns, and inform prognosis or population-level planning.
A risk set represents the individuals who remain under observation and are still eligible to experience the event at a particular time. Risk-set methods therefore evaluate each event in relation to the population still at risk, while excluding individuals who are no longer observed. This structure allows incomplete follow-up to contribute appropriately to estimation.
An analysis begins by organizing follow-up times, identifying which observations experienced the event, and marking observations that are right-censored. Analysts then select a likelihood-based or risk-set approach, estimate survival or hazard patterns, and examine covariate relationships. The resulting analysis can compare groups, quantify explanatory effects, or support predictions about event timing.
These models are useful when the timing of failure, relapse, death, or another event matters and some observations remain event-free when follow-up ends. They preserve information about differing observation periods and incomplete outcomes, making them relevant to clinical prognosis, epidemiological research, reliability analysis, and public-health planning.
They can describe how event patterns change over time, compare groups, estimate relationships between covariates and event-related quantities, and support prediction of event patterns. In clinical settings, this can inform prognosis; in epidemiology and public health, the results can contribute to planning, while reliability studies can examine failure behavior.