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Hello,

For the DCE I am designing I am interested in whether 2 new types of health test will influence uptake compared to 2 tests that are currently used. The current tests are based in hospital but the new tests could be done in community settings. I will therefore have the following attributes:

type of test attribute (4 levels)
- current test 1
- current test 2
- new test 1
- new test 2

location
- hospital
- community setting 1
- community setting 2

It will be a single profile design where people will be shown a single profile and asked whether they would have the test or not (to reflect a realistic choice for patients).

I've been under the impression that it is better to use effects coding as dummy coding can lead to misinterpretation of the estimates - the base level will be implicitly equal to zero and there is no unique interpretation of B0.(Bech & Gryd-Hansen, 2005). I have read Daly et al., 2016 paper on dummy coding vs effects coding, but what I still don't understand is if I use effects coding how I would answer my key research questions which require me to examine uptake differences between current practice and hypothetical future practice, as below:

Type of test:
Current test 1 and new test 1
Current test 1 and new test 2
Current test 2 and new test 1
Current test 2 and new test 2

Location:
Hospital and community setting 1
Hospital and community setting 2
Community setting 1 and community setting 2

I'm new to DCEs so want to make sure I understand how I will interpret the analysis in advance.

Both dummy and effects coding can be used to test your hypotheses.

If you use effects coding and choose the first level as the base level (but you can select any other level as base level), then you would estimate parameters b1, ..., b5 in the model.

type of test attribute (4 levels)
- current test 1 (base = -b1-b2-b3)
- current test 2 (b1)
- new test 1 (b2)
- new test 2 (b3)

location
- hospital (base = -b4-b5)
- community setting 1 (b4)
- community setting 2 (b5)

Based on parameter estimates for b1, ..., b5 and their covariance matrix, you can use the Delta method to conduct the following statistical tests:

Type of test:
Current test 1 and new test 1: H0: -b1-b2-b3 = b2 --> H0: -b1-2*b2-b3 = 0
Current test 1 and new test 2: H0: -b1-b2-b3 = b3 --> H0: -b1-b2-2*b3 = 0
Current test 2 and new test 1: H0: b1 = b2 --> H0: b1-b2 = 0
Current test 2 and new test 2: H0: b1 = b3 --> H0: b1-b3 = 0

Location:
Hospital and community setting 1: H0: -b4-b5 = b4 --> H0: -2*b4-b5 = 0
Hospital and community setting 2: H0: -b4-b5 = b5 --> H0: -b4-2*b5 = 0
Community setting 1 and community setting 2: H0: b4 = b5 --> H0: b4-b5 = 0

If you use dummy coding you could do exactly the same tests but it would be (much) simpler:

type of test attribute (4 levels)
- current test 1 (base = 0)
- current test 2 (b1)
- new test 1 (b2)
- new test 2 (b3)

location
- hospital (base = 0)
- community setting 1 (b4)
- community setting 2 (b5)

Type of test:
Current test 1 and new test 1: H0: 0 = b2 --> H0: b2 = 0
Current test 1 and new test 2: H0: 0 = b3 --> H0: b3 = 0
Current test 2 and new test 1: H0: b1 = b2 --> H0: b1-b2 = 0
Current test 2 and new test 2: H0: b1 = b3 --> H0: b1-b3 = 0

Location:
Hospital and community setting 1: H0: b4 = 0
Hospital and community setting 2: H0: b5 = 0
Community setting 1 and community setting 2: H0: b4-b5 = 0


Michiel

Thanks very much Michiel! Is it personal preference then as to whether dummy or effects coding is used?

Yes mostly personal preference.