Time-to-event Outcomes
Time-to-event Outcomes - Related Videos
08:24
The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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2023
This protocol aims to investigate the neural activity related to social comparison and social distance during the processing of intertemporal decision outcomes. Indifference points will be measured using event-related potentials as part of the study.
10:34
FRET Microscopy for Real-time Monitoring of Signaling Events in Live Cells Using Unimolecular Biosensors
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Cited by 29 •
2012
Förster resonance energy transfer (FRET) microscopy is a powerful technique for real-time monitoring of signaling events in live cells using various biosensors as reporters. Here we describe how to build a customized epifluorescence FRET imaging system from commercially available components and how to use it for FRET experiments.
14:33
Event-related Potentials and the Oddball Task
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Given the overwhelming amount of information captured by the sensory organs, it is crucial that the brain is able to prioritize the processing of certain stimuli, to spend less effort on what might not be currently important and to attend to what is. One heuristic the brain uses is to ignore stimuli that are frequent or constant in favor of stimuli that are unexpected or unique. Therefore, rare events...
09:00
Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond
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Cited by 13 •
2013
Time-lapse microscopy of fluorescently labeled autophagy markers allows monitoring of the dynamic autophagy response with high temporal resolution. Using specific autophagy and organelle markers in a combination of 3 different colors, we can follow the contribution of a protein to autophagosome formation in a robust spatial and temporal context.
01:28
Integration of Synaptic Events
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2025
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
FAQs
Survival analysis tracks the event-time distribution across follow-up rather than reducing every participant to a single fixed-time result. This allows clinical investigators to retain information from participants observed for different lengths of time and to describe how the chance of remaining event-free changes as follow-up progresses. It is therefore suited to recurrence, hospitalization, and death outcomes.
Right-censored observations contribute the follow-up that was actually observed without being treated as if the event occurred. A participant may reach the observation endpoint without an event or leave early, so the analysis records incomplete event-time information. Incorporating these observations helps avoid discarding participants simply because their full event experience is not known.
Kaplan-Meier curves and Cox proportional-hazards models answer related but different questions. Kaplan-Meier analysis summarizes the survival pattern over time, whereas a Cox model compares hazards between groups. Using both can show the observed time-course and the relative group difference, providing complementary evidence when evaluating a clinical treatment, prognosis, or risk factor.
A clinical time-to-event analysis begins by specifying the event and measuring elapsed follow-up for each participant. Investigators then record whether the event occurred, observation ended without an event, or the participant left early, and apply a survival-analysis method. This workflow makes the endpoint explicit and ensures that unequal follow-up is represented in the results.
These outcomes are especially useful when a study must distinguish whether an event occurred from when it occurred. Two groups could have similar event experiences at one point yet differ in the timing of recurrence or hospitalization. Time-to-event analysis therefore supports treatment evaluation and prognosis by retaining the temporal pattern of clinical outcomes.
For clinical risk-factor research, the outcome can be used to examine how a factor relates to the timing of an event, not merely whether the event appears during the study. Hazard comparisons can help characterize differences between groups, while survival estimates show how those differences unfold over follow-up. This is also relevant to prognostic assessment.