The<unk>  in survival analysis is the effect of an explanatory variable on the<unk>  or risk of an event.  For a less technical definition than is provided here, consider<unk>  to be an estimate of relative risk and see the explanation on that page.
The instantaneous<unk>  rate is the limit of the number of events per unit time divided by the number at risk as the time interval decreases.
where N(t) is the number at risk at the beginning of an interval.
The<unk>  is the effect on this<unk>  rate of a difference, such as group membership (for example, treatment or control, male or female), as estimated by regression models which treat the log of the<unk>  rate as a function of a baseline<unk>  
h
0
(
t
)
{\displaystyle h_{0}(t)}
 and a linear combination of explanatory variables:
Such models are generally classed proportional<unk> s regression models (they differ in their treatment of 
h
0
(
t
)
{\displaystyle h_{0}(t)}
, the underlying pattern the<unk>  rate over time), and include the Cox semi-parametric proportional<unk> s model, and the exponential, Gompertz and Weibull parametric models.
For two individuals who differ only in the relevant membership (e.g. treatment vs control) their predicted log-hazard will differ additively by the relevant parameter estimate, which is to say that their predicted<unk>  rate will differ by 
e
β
{\displaystyle e^{\beta }}
, i.e. multiplicatively by the anti-log of the estimate. Thus the estimate can be considered a<unk> , that is, the<unk>  between the predicted<unk>  for a member of one group and that for a member of the other group, holding everything else constant.
For a continuous explanatory variable, the same interpretation applies to a unit difference.
Other<unk>  rate models have different formulations and the interpretation of the parameter estimates differs accordingly.
Hazard ratio
Ancillary statistic
Absolute deviation
Attributable risk
P-value
Point prevalence
Arithmetic mean
Median