The regulatory process overwhelms those incentives anyway. If you can cut down on the 8 to 10 years to market you might see more drug companies pop up chasing a long shot
414 karma · joined September 26, 2016
But they don't just price gouge. Drug companies need to file patents on their drug almost as soon as they sketch they come up with drug. From there they need at least 8 years to get through the FDAs approval process, so the manufacturers don't get nearly as much exclusive time with their IP as other industries. (They started getting around that by slightly tweaking the formula to last longer and calling it XL.) Plus they have to recoup the money they sank into other drugs that never made it to market. Sure, they can write down those losses, but a write-down ain't a refund.
If the government wants to start lowering drug prices, they can start by creating a special patent for the drug industry—call it a research patent—that can get converted to a regular patent when it gets FDA approval, and then give the drug companies the patent for the standard 20 years plus a renewal if they work the patent.
The next step: they can shorten time to market. We have evidence that the FDA errs too conservatively and doesn't approve (or delays) drugs that would have turned out totally safe and helped people. I remember they granted special approval to bring in a meningitis vaccine that Europe had for a while when an east coast school saw an outbreak. The vaccine still hadn't cleared the FDA, but I guess FDA special clearance is better than doing nothing. If it's safe enough for Western Europe, it's probably safe enough for Americans.
Let them know what's going on. Did they pick the microchip? Then you aren't incompetent, you're fixing their issues.
Keep them in the loop. I like akulbes advice of subcontracting that problem out.
A hangup isn't incompetence. Having trouble with obstacles doesn't mean you're incompetent. It means you're pushing yourself. If you never push yourself you don't get better
The ones who got good jobs were good applicants. They would have gotten the Dev job anyway. The ones who weren't strong got swept under the rug.
Your cousin might do better taking a year of CS classes and working on outside projects
We need a central repository like Arxiv where we dump the experiments that didn't work out so that we can quickly compare a "successful" one to ones done before. That gives us a better idea of if the data is just a fluke.
The papers wouldn't have to be super involved. What did you do? What were statistical conclusions. Give an upper-level undergraduate or an early masters student some experience writing up a procedure. Shouldn't take more than a couple hours but it could save a lot of time dealing with publication bias
MBA courses for data science will bore her to tears. If she adds the R and Python and takes a couple graduate level math stats courses before she graduates, she'll know more than almost all the graduates from those programs
Throw in some stats and basic data analysis and I think you have a pretty good training regimen for people who want to go to Whitehall and Parliament
I have two questions
1) how important is Terry Tao and his group to compressed sensing?
2) I'm interested in signal processing and compressed sensing. How can I get into it with a math background instead of an EE background?
Edit sorry I posted this comment three times. I pushed the comment button on my phone and nothing happened so I assumed nothing happened
Someone thought printing that was a good idea?
1—Have a good, somewhat strange, personal story ready to go. I have a few that work depending on the occasion. People like to laugh.
2—Ask more questions. People love to talk about themselves.
3—Spend your time telling personal stories. People don't enjoy talking about ideas and politics.
4—Practice. Your father's colleague doesn't think too much about the perfect thing to say (and he definitely doesn't think about responses while someone else is talking). He knows what to say and he lets it flow
If you have a coin that comes up heads 60% of the time, a frequentist looks at that as, "as the number of times I flip my coin goes towards infinity the proportion of heads I get goes to 60%." A Bayesian thinks, "absent other evidence on the how the coin gets flipped, I'm about 60% sure the outcome of the flip will be heads."
This lets Bayesians talk about the probability of single events, like basketball games, where frequentists can't.
Bayesians also see conditional probabilities everywhere. A conditional probability says, "well, if I know something about the situation, I should include it in my beliefs." Circumstances matter. It doesn't make a ton of sense to talk about the chances I get hit by a car. They very wildly depending on whether I'm standing on the highway or eating in my kitchen.
Another basketball example. The chances that Spurs win changes dramatically if they play the Warriors or if they play the Kings. I might say "they have a 90% chance of winning given they play the Kings, but a 40% chance of winning given they play the Warriors.
I also need a likelihood function. What are the odds I saw my data given my hypothesis is true. If I got hit by a car, what are the chances I was standing in the street? Given the Spurs won, what are the chances they played the Warriors?
We use something called Bayes Rule which allows us to pile on more and more information on something we call a "prior belief," what we thought about our hypothesis before we saw our data. As we pile on data, we expect to change our beliefs. We become more sure of what we thought, maybe we become less sure, maybe we can totally change our minds.
I want to use Bayes' example since I think it's so good. Imagine you came out of Platos cave and saw the sun rise. You'd think, that's weird, I bet that doesn't happen again. The sun goes down, and you spend some time in the dark. The next morning the sun comes up again. Now you're less sure that sun rises are fluke events. Plus you found some people who aren't freaking out about the whole "big ball of fire in the sky" thing. Maybe now you don't expect the sun not to rise tomorrow. Maybe it will, maybe it won't. As you see more and more sun rises, eventually you get to the point where you are extremely confident that the sun rises every morning. You saw more sunrises and updated your beliefs.
We need one last piece of information: a prior. That's that initial belief you're cave-escaping-self had that sun rises are weird and you probably won't see another one. We can estimate them through population data--percentage of games the Spurs won against the Warriors--or we could just make them up. This is just our belief about the truth of our hypothesis; the chance I get hit by a car regardless of where I am.
We take all this put it into Bayes rule, a blender that gives us the probability that our hypothesis is true given we saw our data. We can use this as a new prior too.
One last example. I'm 70% sure the earth is round. I see a picture of the horizon taken from a hot air balloon and I think there's a 90% chance that I would see that if the earth were truly round. Without going into the calculation, I'm now give or take 80% sure the earth is round. I saw data to support my hypothesis and my belief got stronger.
Why do Bayesian analysis? Because someone once published a study that says that frogs can sense earthquakes some time before they occur. That may be true, but I'm skeptical. My skeptical prior would only get moved slightly to become less skeptical, but it would still need more information, a replication of the study by other people, to actually convince me.
If someone wants to join a terrorist organization or something, and the FBI didn't identify them by the time they get to the airport, TSA ain't stopping them. So just let us pass in peace
I know I've gone out for a drive or a walk to clear my head and found myself 90 minutes from home. Maybe this guy took it to a whole new level