3 ms·

It does matter who is responsible for a death. In the same way we don't pick people off the street and harvest their organs, so long as there are 2+ people on t

by Fomite 11y ago

It does matter who is responsible for a death. In the same way we don't pick people off the street and harvest their organs, so long as there are 2+ people on the donor pool who could use them. To follow that: 1. People aren't rational. 2. You're not talking about "Roll this dice and we'll cure all manner of things." You're talking about "Roll this dice and we maybe, if everything goes well and it shows efficacy, might see a treatment for a disease on the market in a few decades". 3. It's not 1 in a billion. It's 1 in X, where X is unknown. Asking a volunteer to take that risk is both easier and more ethical if you've done your best to minimize X.

Houshalter 11y ago
>In the same way we don't pick people off the street and harvest their organs, so long as there are 2+ people on the donor pool who could use them. Well ideally we should do that. If it really saved more people. It could be done entirely voluntarily, through a lottery. And it would be rational for everyone to sign up, since you are more likely to need an organ at some point in your life, then to lose the lottery. The rest of your comment is just a misunderstanding of statistics. Out of decades of hundreds of phase 1 trials, very very few people have died. The risk is known. We also have invented many new drugs that have had many benefits and saved many lives, which can also be measured.
- Fomite 11y ago
  I have a PhD in Epidemiology. Studying risk is what I do for a living. And no, the risk is not known. It's known to be "pretty damned low" for the aggregation of all Phase I trials, but for any given patient, and any given drug, we don't know the risk. Approaching clinical trials in human subjects with caution and care is what has made that risk as low as it is.
  - Houshalter 11y ago
    That's just not how probability works at all. You absolutely can put a number on risk. You can look at past drugs like it that and determine what percent caused harm in humans after passing animal trials. You can narrow it down to drugs that are similar. If you want to get really advanced, which isn't necessary, you could fit a bayesian model to all the data you have. Or use a prediction market to determine the risk. Don't confuse uncertainty with unpredictable. Everything is uncertain, but few things are unpredictable.
    - Fomite 11y ago
      As all of these drugs are novel, the best you can do with "similar drugs" is to come up with a decent prior for a Bayesian risk model. But since each of these drugs is new and untested then no, you don't have a numerical risk. You cannot say "1 in 167,234" or "1 in 1 Billion". You can say "Likely to be very, very small", but if you can conjure the risk of side effects for a drug with no data ever generated on that drug, I'd be impressed.
      - Houshalter 11y ago
        For the sake of argument, say there have been 100 drugs tested in the past. One of them killed someone, the rest were completely harmless. Now the next drug comes along to be tested and you know absolutely nothing about it. You need to estimate the risk, and all you know is the information above. Of course your estimate must be 1%. Unless you have more information to update that probability, 1% is the optimal baseline prior. You seem to be confused about what probability is. It's just a representation of uncertainty.
        Fomite 11y ago
        Your estimate is 1%. It's a good prior. It's not the actual probability of an adverse side effect.
        Houshalter 11y ago
        There is no such thing as "actual probability"! An event either happens 100% probability, or doesn't happen with 100% probability. There is nothing else. If you flip a coin, it doesn't have 50% probability of landing heads. The atoms and their path through the air is predictable, and it is already determined that it will land heads by the time it's left your thumb, and perhaps long before. But you don't know that, so you can only estimate 50%. All probability is just a representation of your own uncertainty. If you bet anything other than 1% risk, over time you will lose money. After thousands of trials, 1% of them will go bad.

by Fomite 11y ago

Houshalter 11y ago

>In the same way we don't pick people off the street and harvest their organs, so long as there are 2+ people on the donor pool who could use them. Well ideally we should do that. If it really saved more people. It could be done entirely voluntarily, through a lottery. And it would be rational for everyone to sign up, since you are more likely to need an organ at some point in your life, then to lose the lottery. The rest of your comment is just a misunderstanding of statistics. Out of decades of hundreds of phase 1 trials, very very few people have died. The risk is known. We also have invented many new drugs that have had many benefits and saved many lives, which can also be measured.

Fomite 11y ago
I have a PhD in Epidemiology. Studying risk is what I do for a living. And no, the risk is not known. It's known to be "pretty damned low" for the aggregation of all Phase I trials, but for any given patient, and any given drug, we don't know the risk. Approaching clinical trials in human subjects with caution and care is what has made that risk as low as it is.
- Houshalter 11y ago
  That's just not how probability works at all. You absolutely can put a number on risk. You can look at past drugs like it that and determine what percent caused harm in humans after passing animal trials. You can narrow it down to drugs that are similar. If you want to get really advanced, which isn't necessary, you could fit a bayesian model to all the data you have. Or use a prediction market to determine the risk. Don't confuse uncertainty with unpredictable. Everything is uncertain, but few things are unpredictable.
  - Fomite 11y ago
    As all of these drugs are novel, the best you can do with "similar drugs" is to come up with a decent prior for a Bayesian risk model. But since each of these drugs is new and untested then no, you don't have a numerical risk. You cannot say "1 in 167,234" or "1 in 1 Billion". You can say "Likely to be very, very small", but if you can conjure the risk of side effects for a drug with no data ever generated on that drug, I'd be impressed.
    - Houshalter 11y ago
      For the sake of argument, say there have been 100 drugs tested in the past. One of them killed someone, the rest were completely harmless. Now the next drug comes along to be tested and you know absolutely nothing about it. You need to estimate the risk, and all you know is the information above. Of course your estimate must be 1%. Unless you have more information to update that probability, 1% is the optimal baseline prior. You seem to be confused about what probability is. It's just a representation of uncertainty.
      - Fomite 11y ago
        Your estimate is 1%. It's a good prior. It's not the actual probability of an adverse side effect.
        Houshalter 11y ago
        There is no such thing as "actual probability"! An event either happens 100% probability, or doesn't happen with 100% probability. There is nothing else. If you flip a coin, it doesn't have 50% probability of landing heads. The atoms and their path through the air is predictable, and it is already determined that it will land heads by the time it's left your thumb, and perhaps long before. But you don't know that, so you can only estimate 50%. All probability is just a representation of your own uncertainty. If you bet anything other than 1% risk, over time you will lose money. After thousands of trials, 1% of them will go bad.