Thank you for providing this forum.
This is my first time conducting a choice experiment, so some of my questions may be a bit naive.
In this study, I want to explore people's preferences for a certain type of medicine. I design 5 attributes, which are:
Cost(500, 1000, 1500, 3000)
Dosing Frequency(Daily dosing, Once-monthly dosing, Once every two months, Once every six months)
Efficacy(99%, 90%)
Risk of Adverse Effects(Mild, Moderate, Severe)
Route of Way(Tablet, Injection, Implantable rod).
Among them, the injection and the implantable rod would not be paired with a daily dosing regimen, while the tablet would not be paired with a once every six months regimen.
For specific reasons, I was unable to conduct a pilot study, so I added a small prior to each level based on the previous posts in the forum. But this led to a problem. When the model's D-error converged, I reviewed the questionnaire and found some absolutely dominant scenarios. So I further added reject constraints to rule out some undesirable dominant combinations.
As a result, the absolutely dominant options disappeared, but there were still many dominant scenarios.
For example, a scenario like A: ¥3000/month, once-monthly, 90% efficacy, noticeable side effects, implantable rod vs. B: ¥500/month, daily, 99% efficacy, no side effects, tablet — in this case, one alternative is dominant in only one attribute level.
I know that Ngene deliberately designs less balanced questions to explore boundaries, but the problem is that when I carefully reviewed each scenario, I found that scenarios where one alternative is dominant in only one attribute level accounted for 16 out of 48. I wonder if this is too many.
After that, I tried increasing the priors, but the problem persisted. Below is my code:
Code: Select all
Design
;alts = prepA*, prepB*
;rows = 48
;block = 4
;eff = (mnl,d)
?;bdraws = halton(100)
?;rep=10
;alg = mfederov(stop = noimprov(30 mins))
;reject:
prepA.way = 0 and prepA.duration = 0,
prepA.way = 1 and prepA.duration = 0,
prepA.way = 2 and prepA.duration = 3,
prepB.way = 0 and prepB.duration = 0,
prepB.way = 1 and prepB.duration = 0,
prepB.way = 2 and prepB.duration = 3,
prepA.cost <= prepB.cost and
prepA.duration >= prepB.duration and
prepA.efficacy >= prepB.efficacy and
prepA.risk >= prepB.risk,
prepB.cost <= prepA.cost and
prepB.duration >= prepA.duration and
prepB.efficacy >= prepA.efficacy and
prepB.risk >= prepA.risk
;model:
U(prepA) =
b_cost[-0.01] * cost[1,2,3,6](10-14,10-14,10-14,10-14) ? 1 unit=500
+ b_dur.effects[0.01|0.005|0.0025] * duration[3,2,1,0] ? 0:daily dosing; 1:once-monthly dosing; 2:once every two months; 3:once every six months
+ b_eff[0.01] * efficacy[9,10] ? 9:90% efficacy; 10:99% efficacy
+ b_risk.effects[0.01|0.005] * risk[2,1,0] ? 0:severe adverse effect; 1:moderate AE; 2:mild AE
+ b_way.effects[0.01|0.005] * way[2,1,0] ? 0:implantable rod; 1:injection; 2:tablet
/
U(prepB) = b_cost * cost + b_dur.effects * duration + b_eff* efficacy
+ b_risk.effects * risk + b_way.effects * way
$
2. And there is another question: If I plan to use an MXL model for the final analysis, can I just use an MNL design during the Ngene questionnaire design phase? Or, after the imbalance issue is resolved, should I still use rppanel model?
Thank you very much!