ToTo 奇妙の冒險

I met with Marianne, a post doc in the School of Nursing in UNC-CH,
to discuss some remaining problems in her publication. Those
problems caused me confused as well, but my supervisor Mark didn't
have time to participate in our meeting last Friday. Hence, I stop
by Mark's office again today (Monday). There are some useful
conclusion after meeting with Mark.



The first question is whether Likelihood Ratio Test(LRT) is
suitable in comparing two models have the same independent
variables but some of them have differnt attributes. For example,
one variable is continuous in a Model, but how about using a
categorical one in the same model? That's the key point of this
question. The only confused thing is that LRT is only used in
nested model, but I am not sure whether this kind of situation is
the same. Mark told me it is because we can regard the continuse
one is a reduced model and the categorical one is full model in
that there are more variables in categorical one. Based on this
assumption, we can use LRT as usual.



The second question is more complicated. Marianne had already
finish the part of model selection, but just need a LRT to confirm
her final models are the best one. By using LRT, the decision
should be non-significant with large p-value, then we can have no
rejection of the null hypothesis which is reduced (final) model.
However, it is totally conflicted because the result is
significant. After checking the original SAS code, there is no
problem as well. However, Mark said, based on Marianne study
design, she needs to keep two important variables in this model
whatever it is significant or non-significant. After including the
two variables in this model, the conflict was eliminated. But, I
was still wondering whether one of them is highly overlapped with
another one because they are all geographic variables and have
highly similarity. I dropped out a less important one and fit the
model again, the result looked better. From this problem, we can
understand that we need to know more about variables before model
fitting, then we will decrease confusion from that.



The two solutions had already been emailed to Marianne. Hope she
will feel useful.