This forum is about wrong numbers in science, politics and the media. It respects good science and good English.
This discussion is a variant of the smoking/lung cancer discussion to me. The analysis is of the RR (HR, OR, Whatever ratio you wish to use). The RR is inevitably a ratio of bad to basis for each group. In my over simplified model, it is the probability of rolling a six on one throw of the die and comparing that to the chance of rolling a six on a different die.
Continuously lost in the discussion is the survivability ratio, aka, what is the chance in x rolls of the dice that I won't roll a six. Smokers continue to flaunt the huge risks of smoking, leading active productive lives surviving way longer than they are suppose to. The risks associated with Vioxx are microscopic compared to smoking.
The smoking death toll requires great minds to extract from the data. Other great minds can come back and twist the result without too much difficulty because it fiddles with a variable that isn't all that well defined. The unit of life is the period between birth and death. Rosebuds come to mind. There is quite possibly more science behind "carpe diem" than all the sophisticated analysis of epidemiology. Vioxx made it infinitely more likely for a person seize more moments and enjoy them. Apologies in advance, but I will sneer heavily in the direction of anyone suggesting they can accurately measure enjoyment. A Nun on her knees praying can effectively be enjoying life every bit as much as Paris Hilton.
Imagine a collection of atoms of radioactive elements. Any and all - we have a range of half-lives ranging from a few seconds to millenia.
We stick them in a box and watch them decay. At the beginning of the experiment the decay rate, the number of decays per unit time per number of remaining atoms, is higher than it is later in the experiment.
Your average group of patients being watched for some event is like this mixture of different nuclei, they are not a group of the same type of nuclei at constant risk of decay irrespective of time. The analogy is not perfect of course - in humans risk of bad stuff tends to rise with age, radioisotopes don't care how old they are. But in this human experiment we are looking at two groups with one systematic treatment difference, and all the other differences controlled for, as far as possible, through randomisation.
Cohort attrition at later stages is because of the trial being stopped.
It's not ethical to stop a trial in a marketed product for lack of participation, not that there was any such lack anyway. It is ethical to stop it because the drug is harming people. The highest ethic in clinical trials is the good of the participants in that trial - the greater good of humanity takes a back seat (cf. Declaration of Helsinki). You might not like that scientifically, but that's the decision that has been reached and in many places legislated for.
Once you have this kind of result it's **** hard to find volunteers for a repeat of the experiment. Like I said, you are absolutely right with most of your criticisms of statistical abuse in the life sciences. However, as it's "life" and in this case human life, we have to live with a lower degree of certainty than the physicists.
Rutherford said if you need statistics you need better experiments. Unfortunately in drug research it is not ethically possible (or financially possible) to put any question of efficacy or safety beyond all doubt. Beyond reasonable doubt (even at p=0.05) can be hard enough. and the risk/benefit evaluation is by its nature not a simple formula.