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.