Marius L T
Bio
Participation3
MD, PhD student on healthcare priorities with an emphasis on moral weights and measuring health-related quality of life.
Comments5
Not exactly my field, but I had a quick chat with a conveniently located senior researcher at work. This is a synthesis of our thoughts:
Summary: Might be a bit ambitious, but doable if you are well prepared.
Feasibility: The number and availability of of informants will be crucial. Do you already have contacts or a way in? I frequently see people (including myself) grossly underestimate the time it takes to collect data. How many informants? If you plan to publish the results in a peer-reviewed journal, aiming for more than the minimum (I don't know, 8?) is probably wise.
Transferability: I would imagine some findings would be generalisable to similar countries at least.
Methods: What are your options? Would a survey be possible? Need more people but is maybe easier to analyse. Thematic Analysis is a good pragmatic approach. Because of this pragmatism however, your current description lack some detail on how you will actually do the analysis (except the general steps). For example, how will you relate the data to your concept model? You won't have much time to figure this out during your 10 weeks. Especially if you have never analysed interviews before.
How to conduct interviews: If you know someone who already is doing a similar analysis, see if you can sit in on an interview, working session or supervision session and observe. It helped me the time I wrote a thematic analysis paper (but I will not offer or trust my own skills here)
Pitfalls: Just want to reiterate that data collection sometimes takes much more time than anticipated, and you should expect to spend 80% of your time on analysis.
Thank you for a very interesting read.
It seems like an important crux in your analysis is quantifying the intensity of CH.
I'd like to point out that QALYs as a metric is not mentioned here. In the QALY-paradigm, the utility weights are anchored at 1 = full health and 0 = dead. Importantly, negative utility weights are also theoretically possible. For example, an utility weight of -10.0 would imply that removing one person-year of CH would be equivalent to 11 QALYs (which is equivalent to the absolute prognosis loss for one person with chronic migraine, according to one random report from the Norwegian Medical Products Agency i just dug up). However, current methods for eliciting negative values are imprecise and somewhat arbitrary. I've been thinking about whether developing better metrics within the QALY paradigm can be useful, since it is more widely adopted. CHs would be the perfect example case. Curious to hear if you have any thoughts on this.
"What does 'negative lives' really look like?"
Given its gravity, I find this question underexplored and current tools (QALYs, WELLBYs, YLSSs etc.) either underdeveloped or inadequate. At the same time I think answers should fit into existing frameworks when possible, since these tools often form a cornerstone, or are heavily implied, when doing cause prioritisation.
There are at least two things I would like to know more about:
When does the transition from positive to negative lives happen? What characterizes this shift? Some of the above tools have a defined neutral point, but in practice, evaluations around it are somewhat distorted.
How far in the negative is it possible to be compared to the positive?
I think it should be possible to at least get some empirical anchors tied to commonly used frameworks, preferably more than one.
(And I have some ideas for how to do so, but it doesn't seem like there are many funding opportunities, so I'd probably want $500 000 for myself to pursue them, or maybe just $50 000 to do some small tests)