Hi listers,
I am analysing a discrete choice experiment in which 100 individuals are asked about their treatment preference for pain relief. Each individual is offered 10 choice sets, each with two alternatives (A and B); each alternative has 3 attributes (frequency of treatment [frequency], duration of pain relief [relief] and cost [cost]). A typical choice set would look like this:
Choice set # 1
Alternative A:
frequency of treatment (per day) = 3
duration of pain relief (in hours) = 8
cost ($) = 1
Alternative B:
frequency of treatment (per day) = 2
duration of pain relief (in hours) = 12
cost ($) = 1.5
I also have individual level characteristics including age, sex, education and income. The data is in long table form; so I have 100 x 10 x 2 = 2,000 observations. The data is organised such that:
id = remains the same for each individual, i.e. for each set of 20 observations
choice = 0/1 dummy for choice made in each choice set
choiceset = same for the two alternatives in each choice set, i.e. there are 1,000 choicesets numbered from 1 to 1,000
I was using the conditional logit model below:
clogit choice frequency relief cost, group(choiceset)
Can anyone suggest whether using an -asclogit- or -mixlogit- be more appropriate in this situation, especially if I wish to take account of individual characteristics (I understand the other way would be to use interaction terms in -clogit-)? Also can you suggest the appropriate syntax for using -asclogit- and -mixlogit- in this situation (when I tried using: [asclogit choice frequency relief cost, casevars (age sex education income) case(id) alternatives(choiceset)], I get the error: variable choiceset has replicate levels for one or more cases).
Any help would be greatly appreciated.
Thank you,
Shehzad
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