Hi,
Would it be possible to have an expert check of my syntax.
Design
;alts = alt1*, alt2*, optout
;rows = 36
;eff = (mnl, d)
;block = 3
;model:
U(alt1) = b1.effects[0.000011|0.00001|-0.00001]*FEV[15,5,-5,0] + b2.effects[0.00001]*IVdays[1,0] + b3.effects[0.000011|0.00001]*Abdo[2,1,0] + b4.effects[0.000012|0.000011|0.00001]*Lexp[15,10,5,0] + b5.effects[0.000011|0.00001]*QoL[2,1,0] + b6.effects[0.000011|0.00001]*Neb[2,1,0] + b7.effects[0.000011|0.00001]*Physio[2,1,0] /
U(alt2) = b1*FEV + b2*IVdays + b3*Abdo + b4*Lexp + b5*QoL + b6*Neb + b7*Physio /
U(optout)= b0[0]
$
My questions are:
Have I correctly specified the opt-out?
My experiment is looking at patients’ preferences for adding a new medication to their existing treatment. It consists of two unlabelled alternatives and an opt-out. The opt-out is to stay on current treatment only (the baseline) – so should be considered as a status-quo. All attributes specify changes in various outcomes from their baseline with existing treatment (specified as ‘0’ in the model).
We are at the pilot stage and I have no information on priors – other than sign. I have received expert advice elsewhere to treat unknown attributes as categorical in the first instance. Since a (potential) secondary objective is to model WTP using FEV as the numeraire – is it reasonable to treat these as categorical in the design stage, and switch to continuous in subsequent analysis?
Many thanks in advance
Rory