Time-to-event Data
Time-to-event Data - Related Videos
08:33
High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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Cited by 5 •
2010
Event-related potential (ERP) recording is under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and this method often has poor spatial resolution. To foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition.
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Cited by 27 •
2013
Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.
07:59
Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Cited by 1 •
2023
One challenge of analyzing synchronized time-series experiments is that the experiments often differ in the length of recovery from synchrony and the cell-cycle period. Thus, the measurements from different experiments cannot be analyzed in aggregate or readily compared. Here, we describe a method for aligning experiments to allow for phase-specific comparisons.
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.
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Cited by 15 •
2013
A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.
FAQs
Censoring indicates that an observation ended before the defined event was recorded. Statistical analysis must retain that partial timing information rather than treating the observation as if the event never occurred or occurred at a particular later time. Accounting for censoring allows analysts to estimate survival functions and hazard rates while using the available follow-up appropriately.
A survival function describes the pattern of remaining free from the defined event over time, whereas a hazard rate focuses on the event risk at a given point in the timing process. These summaries answer related but different questions: one characterizes survival across follow-up, and the other characterizes how risk changes during that period.
Kaplan–Meier estimation describes survival patterns, including how they change over time. A log-rank test evaluates differences in those patterns between groups. A Cox proportional hazards model examines associations between event timing and predictors. Together, the methods can provide descriptive results, group comparisons, and predictor-based analysis rather than serving as interchangeable procedures.
Predictors allow analysts to examine whether measured characteristics are associated with differences in the timing of an event. The Cox proportional hazards model is therefore useful when the question extends beyond describing survival patterns or comparing groups. Its results support statistical assessment of relationships between predictors and event timing in applications such as clinical research and public health.
Begin by specifying the event and recording each observation’s duration, then identify observations whose follow-up ends before the event occurs. The analysis can proceed with Kaplan–Meier estimates for survival patterns, log-rank tests for group differences, or Cox models for associations with predictors. This sequence links the study question to an appropriate statistical result.
These data are useful when the timing of an outcome matters alongside whether it occurs. Clinical researchers can examine disease recurrence or patient recovery, while public health analysts can study event patterns across groups. Survival functions, hazard rates, and group comparisons help characterize when outcomes arise and assess differences relevant to research questions.
In reliability engineering, equipment failure provides a defined event whose timing can be analyzed statistically. Censoring is relevant when observation ends before failure occurs, while survival functions and hazard rates summarize failure patterns. Group comparisons or Cox models can further assess differences or associations when predictors are included, extending time-to-event analysis beyond biomedical research.