For a given system, either biological or technological, survival and reliability analysis study the waiting time X for an event to occur (death, recovery, failure of a component) or else the successive sojourn times in successive states (of health or of functioning) are functions of factors called covariates in the medical field and stresses in the technological field. Various regression models have been developed in order to represent how the durations depend on the stresses / covariates, whose properties are those required by the final objective of the experimenter. Three major features are different in survival as compared to reliability. One is the phenomenon of censoring and truncation which is much more present in survival analysis. The second is the treatment of asymptotics, which usually relies on the number of patients tending to infinity in survival analysis, while in reliability it may be the observation time of a unique technological system that tends to infinity. And the third aspect which is different is the control : when testing the reliability of systems, one can control the stresses and thus accelerate the wear while in the biomedical field, covariates are observed except in clinical trials when two treatments are compared. The models most frequently used are hazard based models, like Cox model and its extensions and accelerated models. A competitor of those models is a latent variable model : the Threshold Regression (TR) model. This latter model has the advantage of allowing to treat the factors under consideration in three different ways, according to their role in the waiting time of the onset of the event, whether it is intrinsic, evolving in time, or acting like a catalyst accelerating the process. An example of such an application is proposed.