Hi,
I conducted the pilot study with 30 participants. I also conducted MNL to obtain the estimates and their standard errors.
However, the signs of 2 of the attributes are not in the direction we assumed. Those attributes are:
1. Prediction of clinical outcomes for a diagnostic test- we got a positive sign for the 'No' level estimate (keeping 'yes' as baseline)
2. Test invasiveness: we got a negative sign for the 'No' level estimate ('yes' is the baseline)
Now, the issue is while designing the Bayesian efficient design, can I change the sign of these priors and use the expected signs in my equation?
What should we normally do in such situations? What might be possible reasons for this to happen?
Here is my code for the Bayesian design, Could you kindly look through it for appropriateness?
Design
;alts = alt1*, alt2* ? the asterisk (*) after an alternative means they are unlabelled. It also ensures that NGENE checks for dominance and balance
;rows = 36
;block = 4
;eff = (mnl, d, mean)
;alg=mfederov
;bdraws = sobol(1000)
;model :
U(alt1) =
b1.dummy[(n,-0.19,0.32)|(n,-0.46,0.31)|(n,-0.13,0.28)]*Fastingtime[2, 3, 4, 1] +
b2.dummy[(n,-0.36, 0.16)]*Invasiveness[2, 1] +
b3.dummy[(n,0.29,0.23)|(n,0.75,0.23)]*Sensitivity[2, 3, 1] + ? The base level must always be at the end of the bracket
b4.dummy[(n,0.62,0.19)|(n,0.96,0.19)]*Specificity[2, 3, 1] +
b5.dummy[(n,0.08,0.16)]*Prediction[2, 1] +
b6.dummy[(n,-0.27,0.24)|(n,-0.48,0.24)|(n,-0.90,0.33)]*Cost[2, 3, 4, 1]
/
U(alt2) = asca[(n,-0.52,0.20)] +
b1.dummy*Fastingtime[2, 3, 4, 1] +
b2.dummy*Invasiveness[2, 1] +
b3.dummy*Sensitivity[2, 3, 1] +
b4.dummy*Specificity[2, 3, 1] +
b5.dummy*Prediction[2, 1] +
b6.dummy*Cost[2, 3, 4, 1]
$
Thank you in advance.
Asmita