1. Vera, maybe you could write a post about why (and when) all-cause mortality is the ideal measure for these sorts of studies. I see a hint of the answer in John Best's comment and in your post, but it's still elusive to the lay person! If an intervention helps a life-or-death condition but doesn't reduce all-cause mortality, wouldn't we have to argue that the intervention made death from another cause more likely than it would have been without the intervention? (Asked with genuine confusion.)
2. I appreciate how well you keep up with abortion studies, and how you try to tease out the science from the politics. Thank you for keeping the topic in your spotlight.
3. I was listening to Jay Bhattacharya on the Uncommon Knowledge podcast today (the new director of the National Institutes of Health in the U.S.), and I thought he had smart thoughts related to your question about how to make science better. It had to do with the current lack of reproducibility, and how to encourage scientists to do replication studies--i.e. how to encourage this both financially and in terms of career advancement.
Thanks for your thoughtful comments! I posted a bit about your first point earlier today, but will have to think and write more later. This really gets to the heart of why net assessments of mass screenings (and other policies) are so hard.
and for some, severe mental health ramifications which are difficult to factor in. if i had a chronic disease that may or may not end up killing me, it would plague my mind for the rest of my life, i would struggle to enjoy what time i had left, and i might not even die from it. even if i get it treated, i die from something else around about the same time. so all that anguish and expense wouldn't be worth it.
yes, there are a few anecdotes of people whose life was saved but there are many more who suffered from over-treatment. plus, even if you were saved from X, you might not have extended your life as Y would've killed you anyway, this is definitely true on average. the years gained for all that expense and suffering isn't substantial.
until treatments for these diseases become safer and better, in general, i don't think most screening is worth it.
(i think in general we struggle with the fact that we actually do not have as much control over our lifespans or health as we'd like. even with modern medicine our power is still very limited when it comes to certain diseases.)
Thanks for these excellent points. Quality of life costs like pain and anxiety are hard to measure, and get discounted by some of the better analyses (e.g., in favor of focus on all-cause mortality). As you rightly point out, it's so important to grapple with our discomfort with our powerlessness and how it shapes the medical care we get in relation to the outcomes we want.
It suggests using health claims data to select who to screen may meaningfully reduce both late-stage tumors and overdiagnosis. So maybe that will provide an exit in the future to the accuracy-error trade-off that may otherwise leave us with what Stephen Fienberg co-chairing the 2003 NAS polygraph report called an unacceptable choice between too many false positives, or too many false negatives...
1. Vera, maybe you could write a post about why (and when) all-cause mortality is the ideal measure for these sorts of studies. I see a hint of the answer in John Best's comment and in your post, but it's still elusive to the lay person! If an intervention helps a life-or-death condition but doesn't reduce all-cause mortality, wouldn't we have to argue that the intervention made death from another cause more likely than it would have been without the intervention? (Asked with genuine confusion.)
2. I appreciate how well you keep up with abortion studies, and how you try to tease out the science from the politics. Thank you for keeping the topic in your spotlight.
3. I was listening to Jay Bhattacharya on the Uncommon Knowledge podcast today (the new director of the National Institutes of Health in the U.S.), and I thought he had smart thoughts related to your question about how to make science better. It had to do with the current lack of reproducibility, and how to encourage scientists to do replication studies--i.e. how to encourage this both financially and in terms of career advancement.
Thanks for your thoughtful comments! I posted a bit about your first point earlier today, but will have to think and write more later. This really gets to the heart of why net assessments of mass screenings (and other policies) are so hard.
and for some, severe mental health ramifications which are difficult to factor in. if i had a chronic disease that may or may not end up killing me, it would plague my mind for the rest of my life, i would struggle to enjoy what time i had left, and i might not even die from it. even if i get it treated, i die from something else around about the same time. so all that anguish and expense wouldn't be worth it.
yes, there are a few anecdotes of people whose life was saved but there are many more who suffered from over-treatment. plus, even if you were saved from X, you might not have extended your life as Y would've killed you anyway, this is definitely true on average. the years gained for all that expense and suffering isn't substantial.
until treatments for these diseases become safer and better, in general, i don't think most screening is worth it.
(i think in general we struggle with the fact that we actually do not have as much control over our lifespans or health as we'd like. even with modern medicine our power is still very limited when it comes to certain diseases.)
Thanks for these excellent points. Quality of life costs like pain and anxiety are hard to measure, and get discounted by some of the better analyses (e.g., in favor of focus on all-cause mortality). As you rightly point out, it's so important to grapple with our discomfort with our powerlessness and how it shapes the medical care we get in relation to the outcomes we want.
A colleague (Hannes Ullrich) just pointed me to this great paper by Daysal et al on machine learning in health policy: https://meltemdaysal.com/docs/Screening.pdf
It suggests using health claims data to select who to screen may meaningfully reduce both late-stage tumors and overdiagnosis. So maybe that will provide an exit in the future to the accuracy-error trade-off that may otherwise leave us with what Stephen Fienberg co-chairing the 2003 NAS polygraph report called an unacceptable choice between too many false positives, or too many false negatives...