by Edel » Thu Apr 20, 2023 8:38 pm
Hi Michiel,
I think I have it now, I would appreciate your review.
I am trying to quantify the weight of attributes that influence a choice to prescribe BP medicines to an intensive target of <120mmHg or a standard target of 120-140mmHg.
My (2) labelled alternatives are; intensive, standard
I have 4 attributes;
Risk of serious CV event (3 levels)
Risk of fall (3 levels)
Digital health support (2 levels, dummy coded)
Peer support (2 levels, dummy coded)
I have 4 scenario variables
Age (3 levels)
baseline BP (2 levels, dummy coded)
Social hx (2 levels, dummy coded)
multimorbidity (3 levels)
6 interactions
i1 older people with higher CV risk will result in less preference for the intensive alternative
i2 older people with higher fall risk will result in less preference for the intensive alternative)
i3 people with baseline BP of 155 and higher CV risk will result in less preference for the intensive alternative
i4 people with higher fall risk, living alone will result in less preference for the intensive alternative
i5 people with higher CV risk and co-morbidities will result in less preference for the intensive alternative
i6 people with higher fall risk and co-morbidities will result in less preference for the intensive alternative
Design
;alts=intensive, standard
;rows=48 ?increased as you suggested
;block=4 ?each participant will be presented with 12 choice tasks
;eff= (mnl,d)
;alg = mfederov
;require:
standard.cvev >= intensive.cvev,
intensive.fall >=standard.fall
;model:
U(intensive) =
cvev[-0.00001] *CVEV[5, 10, 15]+
fall[-0.0001] *FALL[6,8,12]+
dh.dummy[0.0001] * DH[1,0]+
nudg.dummy[0.0001] * NUDG[1,0]+
multi[-0.0001]*MULT[0,1,3]+
age[-0.00001] * AGE[65,75,80]+
cond.dummy[-0.0001] * COND[155,145]+
soc.dummy[0.00001] * SOC[1,0]+
i1[-0.00001]*CVEV *AGE+
i2[-0.00001]*FALL*AGE+
i3[-0.00001]*CVEV*COND.dummy[155]+
i4[-0.0001]*FALL*SOC.dummy[0] +
i5[-0.00001]*CVEV*MULT+
i6[-0.0001]*FALL*MULT
/
U(standard) = reg[0] +
cvev * CVEV+
fall * FALL+
dh.dummy* DH+
nudg.dummy* NUDG
$