<unk>  are a sub-class of survival<unk>  in statistics.
For the purposes of this article, consider survival<unk>  to consist of two parts: the underlying<unk> function, describing how<unk> (risk) changes over time, and the effect parameters, describing how<unk> relates to other factors - such as the choice of treatment, in a typical medical example.  The<unk><unk>  assumption is the assumption that effect parameters multiply<unk>  for example, if taking drug X halves your<unk> at time 0, it also halves your<unk> at time 1, or time 0.5, or time 
t
{\displaystyle t}
 for any value of 
t
{\displaystyle t}
.  The effect parameter(s) estimated by any<unk> can be reported as<unk> ratios.
Sir David Cox observed that if the<unk><unk>  assumption holds (or, is assumed to hold) then it is possible to estimate the effect parameter(s) without any consideration of the<unk> function.  This approach to survival data is called application of the Cox<unk>  sometimes abbreviated to Cox<unk> or to<unk> 
Other<unk>  exist.  Another approach to survival data is to assume that the<unk><unk>  assumption holds, but in addition to assume that the<unk> function follows a known form.  For example, assuming the<unk> function to be the Weibull<unk> function gives the Weibull<unk> (in which the survival times follow a Weibull distribution).
The generic term parametric<unk>  can be used to describe<unk>  in which the<unk> function is specified.  The Cox<unk> is sometimes called a semi-parametric<unk> by contrast.
Some authors (e.g. Bender, Augustin and Blettner, Statistics in Medicine 2005) use the term Cox<unk> even when specifying the underlying<unk> function, to acknowledge the debt of the entire field to David Cox.
The term Cox regression<unk> (omitting<unk><unk>  is sometimes used to describe the extension of the Cox<unk> to include time-dependent factors.  However, this usage is potentially ambiguous since the Cox<unk> can itself be described as a regression<unk>
Proportional hazards models
Survival analysis
Linear regression
Bar chart
Multivariate analysis
Likelihood-ratio test
Logistic regression
Analysis of covariance