Why are drug trials designed with discrete treatment values? I'm not involved in drug testing, but it is my understanding that most trials are performed using a control and a treatment group (or perhaps a few discrete treatment groups). This data is then used to determine effectiveness of a drug, and hopefully uncover any deleterious side-effects before the drug is released on the population.
I'm wondering why it would not be more sensible to use a continuous distribution of drug dosages, rather than a discrete distribution? For example, give 20% of patients the control, and the give the other 80% a randomised (uniform?) dosage between 0% and ~110% of the expected optimal dosage. It would seem to me that this would provide more information than data that is inherently discretised, as well as potentially making it easier to more accurately fit models like logistic functions for both effects and multiple side-effects that occur at different rates?
The obvious draw-back would be the increased cost of manufacture for the trial, but that would be fairly minimal in comparison to potential benefits, especially for large trials. Also, this method could just as easily be applied to any "treatment" experiment (e.g. in agriculture, metalurgy, etc.). So why aren't more trials conducted like this?
 A: There are several types of clinical trials, including dose-finding trials. I have not personally been involved in clinical trials but as far as I know, the dose administered in the largest phase III trials is typically determined using those earlier trials.
Among other reasons, it is important to obtain data on effectiveness and side-effects at the dosage that will actually be manufactured and prescribed later on. I also suspect that manufacturing is not the main draw-back or cost driver, finding enough qualified patients, managing the trial and getting results in a timely manner would be much bigger issues. Attaining sufficient power with one treatment group is already very costly so you don't want to expand resources on other questions without very good reasons. At this stage, the clock is already ticking on the patent so more conditions means more time to recruit patients and ultimately less profits.
Furthermore, physiologically speaking, the effect of dosage is complex but we do know a few things about it so there would be no point and a lot of ethical difficulties in experimenting blindly with all sorts of doses with a large number of people.
Generally speaking, most trials are part of a long process with far-ranging regulatory, financial and industrial consequences so what would seem to make sense in the context of an isolated study is not necessarily the most sensible thing to do from a business or from a medical perspective.
Even for academic studies with no patented drugs (e.g. psychotherapy), finding enough patients can be a big challenge, compared to simple experimental research with healthy people. Not only are some diseases/afflictions uncommon but there are usually other stringent screening criteria. Adding conditions therefore involves significant costs and delays and is not a decision to be taken lightly.
A: I know nothing about medical trials, but when your treatment variable is a dummy, you can just run a univariate OLS regression/t-test.  When the treatment is continuous you need to pick a functional form or use complicated non-parametrics that many people don't understand.  Alternatively, you can do a multivariate OLS regression on a bunch of dummies, with less power than you'd have if you had everybody in the treatment group getting the same treatment.
So, power and parsimony.
