hello all,
I wonder if someone could help me with the following:
- I am doing some analysis on weight and BMI examining association with
gastrointestinal cancer. Weight and BMI are both continuous variables and I
am also using them as grouped variables, grouping based on the control
distribution. I do logistic regression. To get the p for trend we know that
we fit the grouped variable as continuous. In this case I also have the
original values of the variables, i.e. I have the continuous variable A and
the generated grouped variable B. I tried not only fiting B as continuous,
but also using the original variable A. I know that when the distribution is
skewed you are better off using the grouped variable. In this case, when I
examine weight either using A or B, I get significance for both cases. When
I examine BMI using A I get significance, but using B I get no significance.
Looking at the distribution of the cases and controls together, it is indeed
skewed. So, I'm thinking that the valid results are the ones showing no
significance i.e. those I get from fiting the grouped variable as
continuous. What do you think?
- I have a very large sample so it is expected that the distributions of
weight and BMI will be normal, under a null hypothesis. But, since we know
that nowadays people tend to be more fat than was the case decades ago,
irrespective of medical conditions, is it correct to expect normality under
the null? My point being that if the distribution is skewed already under
the null then maybe we don't have to remedy the skewness in the actual data,
so maybe it's not best to use the grouped variable here instead of the
continuous.
I would appreciate your comments on this, and will compile the answers I
get.
Thank you all
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