Cox Model
Cox Model - Related Videos
10:26
Imaging Neurons within Thick Brain Sections Using the Golgi-Cox Method
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Cited by 10 •
2017
We present a protocol for using the Golgi-Cox staining method in thick brain sections, in order to visualize neurons with long dendritic trees contained within single tissue samples. Two variants of this protocol are also presented that involve cresyl violet counterstaining, and the freezing of unprocessed brains for long-term storage.
06:53
Visualization of Mitochondrial Respiratory Function using Cytochrome C Oxidase / Succinate Dehydrogenase (COX/SDH) Double-labeling Histochemistry
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Cited by 82 •
2011
The cytochrome c oxidase/sodium dehydrogenase (COX/SDH) double-labeling method allows for direct visualization of mitochondrial respiratory enzyme deficiencies in fresh-frozen tissue sections. This is a straightforward histochemical technique and is useful in investigating mitochondrial diseases, aging, and aging-related disorders.
02:37
Microscopic Imaging of Neurons in a Mouse Brain Section Using Golgi-Cox Metallic Staining
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2025
Source: Louth, E. L., et al. Imaging neurons within thick brain sections using the Golgi-Cox method. J. Vis. Exp. (2017)This video demonstrates imaging neurons in a Golgi-Cox stained thick brain section by setting upper and lower focus boundaries and capturing overlapping images. Multiple locations are imaged to ensure detailed neuronal projections, which are combined to create a 3D representation.
08:43
Visualizing the Effects of a Positive Early Experience, Tactile Stimulation, on Dendritic Morphology and Synaptic Connectivity with Golgi-Cox Staining
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Cited by 17 •
2013
This paper describes the procedures for tactile stimulation of rat pups and subsequent Golgi-Cox staining of neuronal morphology. Tactile stimulation is a positive experience that is administered in the perinatal period by stroking pups with a household duster. Golgi-cox staining is a reliable procedure permitting the visualization of entire neurons.
01:19
The Mantel-Cox Log-Rank Test
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2025
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
FAQs
In the Cox model, the baseline hazard supplies the underlying event-risk pattern, while covariates modify that pattern through the exponential of a weighted covariate combination. This multiplicative structure lets the analysis estimate predictor effects without choosing a particular probability distribution for the baseline hazard. That flexibility is useful when the event-time distribution is not specified in advance.
A hazard ratio summarizes the relative instantaneous event risk associated with a predictor within the model’s covariate structure. Values above or below one indicate higher or lower associated hazard, respectively. Researchers can therefore use hazard ratios to quantify how exposures or prognostic factors relate to event timing and to express comparisons between individuals or groups.
It requires hazard ratios between individuals to remain constant over time. If that condition does not hold, a single estimated hazard ratio may not represent the relationship throughout follow-up. This assumption therefore determines whether reported comparisons and predictor effects provide an appropriate summary of changing event risk across the observation period.
Rather than assigning the baseline hazard a particular distribution, the model leaves its form unspecified and estimates predictor effects through the covariate component. This distinguishes Cox analysis from approaches that require a chosen baseline hazard distribution. The design supports analysis of associations with event timing while preserving the time-to-event structure of the data.
An analysis needs time-to-event information, an indicator distinguishing observed events from censored observations, and the predictors whose relationships with event timing will be studied. The model can then estimate associations, identify prognostic factors, or compare groups. Keeping these elements aligned is essential because the method uses both event timing and censoring in the analysis.
Clinical researchers can examine prognostic factors and compare groups, epidemiologists can quantify associations between exposures and event risk, and reliability studies can analyze events over time. These applications share a need to study when an event occurs rather than only whether it occurs, while retaining observations whose event time is censored.