Median Survival Time
Median Survival Time - Related Videos
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.
01:08
Median
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2023
Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured by arranging the data elements in an increasing or decreasing order. Since this middle value is not affected by the precise numerical values of the outliers or fluctuations, it is insensitive to them. Hence, in cases where a data set may have outliers or the extreme values are not known, the median is a better measure of the central tendency...
01:20
Sign Test for Median of Single Population
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2025
In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
02:52
Cellular Impedance-Based Survival Kinetics Assay to Assess Virus Survival
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2025
This video describes a cellular impedance-based technique to study the survival kinetics of viruses that induce cytopathic effects. The method measures the electrical impedance of virus-infected adherent cells pre-treated with saline.
09:02
Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis
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2021
Reported here is a protocol for the quantification of infectious viral particles using real-time monitoring of electrical impedance of infected cells. A practical application of this method is presented by quantifying influenza A virus decay under different physicochemical parameters mimicking environmental conditions.
FAQs
The Kaplan–Meier curve tracks the estimated proportion of a study population that remains event-free over time. The median is identified at the time point where this estimated survival proportion reaches 50%. Because the curve incorporates censored observations rather than treating them as completed events, it can summarize follow-up data even when some participants remain event-free.
Censored observations provide partial information: a participant has not experienced the defined event by the end of follow-up or has left the study earlier. Survival analysis retains that participant’s event-free time without assigning an unobserved event date. This helps the estimated survival pattern reflect available follow-up while acknowledging that the final event time is unknown.
A median identifies the midpoint of observed or estimated survival experience, so it is less influenced by unusually long survival times than a measure based on an arithmetic average. This makes it an interpretable summary when survival times are skewed. It can therefore describe treatment, disease, or failure-time patterns without requiring the data to be symmetrically distributed.
Researchers can estimate the survival pattern for each patient group, treatment group, or risk-factor category and then compare the time at which each curve reaches 50%. A later median indicates that the midpoint of event-free follow-up occurs later in that group. The comparison can summarize differences in death, disease progression, or treatment failure across groups.
The analysis requires time-to-event information for each participant, including the follow-up duration and whether the defined event occurred. If the event was not observed, the record can be treated as censored at the available follow-up endpoint or departure time. These data support construction of a Kaplan–Meier curve and identification of its 50% survival point.
In clinical research, it can summarize time to death, disease progression, or treatment failure and support comparisons between treatments or patient groups. Epidemiological studies can use the same approach to compare risk-factor categories. Because it accommodates censored follow-up and skewed survival times, the measure provides a practical summary of time-to-event outcomes.