Cox regression vs logistic/simple regression I understand cox regression is used to calculate a risk ratio while logistic regression calculates an odds ratio. Does that mean that in retrospective cohorts I would use cox regression while in prospective cohorts I would use a logistic/simple regression?
Thank you
 A: This is not the main difference.
Cox regression deals with time-to-event data, especially where there is censoring. So, with Cox regression, you are interested in how long it takes for something to happen.
Logistic regression deals with whether something happens at all and does not account for censoring so if you have censored data, you will be looking at "has it happened yet", which may get quite different answers. 
By "simple" regression I'm assuming you mean ordinary least squares. This deals with a continuous dependent variable but does not deal with censoring and it does not give either a risk ratio or an odds ratio. 
A: Logistic regression is used when modelling the probability of an event happening, which will usually mean that the response variable is binary. The model provides odds ratios for the exposure. It can be used in both prospective and retrospective studies. Logistic regression can also be used as a predictive model for classification where the target variable is of two types.
Cox regression is used for modelling time to failure (or more generally time to an event. In epidemiology this is often survival and is often used for prospective studies. The model estimates a hazard ratio, which Is the ratio of hazards in two groups (often treatment/intervention and control). Intuitively, this means is the rate of the event (e.g. death) so a hazard ratio of 2 means the rate of death in one group is twice that of the other. Cox regression make a "proportional hazards" assumption - that the hazard ratio stays constant over time.
