I am struggling with the interpretation of the AFT model, Cox proportional hazard model and discrete-time hazard model.

*My question is:*

Can the **coefficients in discrete-time hazard model** also be interpreted (e.g. by using `glm` or `glmer` in `R`) in the way described below. That means, "**Positive coefficients imply the survival time is lengthened; hence, the hazard rate is decreasing**" and the other way around? Is there any way to show this mathematically? 

What I read so far (e.g. http://monogan.myweb.uga.edu/teaching/pd/16duration2.pdf and http://stats.stackexchange.com/questions/6026/how-do-i-interpret-expb-in-cox-regression and http://stats.stackexchange.com/questions/45954/interpreting-cox-regression):

**AFT model**

- In case of the AFT (Accelerated Failure Time) model a coefficient of .2 indicates a reduction of survival time by this factor, meaning that in this case the event is experienced five times faster.

- In a proportional hazard model:
1. Positive coefficients imply the hazard rate is increasing; hence, the survival time is shortened.
2. Negative coefficients imply the hazard rate is decreasing; hence, the survival time is lengthened.

**Cox proportional hazard model**

* In the cox model a coefficient indicates an increase in the log hazard rate.
* In an accelerated failure time model:
1. Positive coefficients imply the survival time is lengthened; hence, the hazard rate is decreasing.
2. Negative coefficients imply the survival time is shortened; hence, the hazard rate is increasing

**Discrete-time hazard model**

In the discrete hazard model the regression coefficient reflects the log of the odds-ratio, hence the interpretation as a k-fold increase in risk.