Hi everyone,
Can anyone throw some light on the following?
Say we are considering a binary logistic model. For argument's sake, imagine we are looking at the binary outcome 1=women over 40 years old with breast cancer 0=women over 40 years old with no breast cancer. We have two binary independent variables: age 41-49 = 0, age >50 =1 and the number of times a female has carried a pregnancies to a viable gestational age (parity): 0=none, 1= one or more.
We will called the age variable A and the parity variable P.
Say the model includes an interaction and is built like this
Alpha + beta1 * A + beta2*P + beta3*A*P
If beta3 is statistically significant then this means that the odds of having breast cancer for women who have carried at least one pregnancy to viable gestational age compared to the odds of having breast cancer for women who have not carried at least one pregnancy to viable gestational age is significantly different for those women aged between 40-49 years compared to those aged > 50 years.
Now, my question is, if we do establish that beta3 is statistically significant would this justify a stratification of the data, so that we could (for example) have two binary logistic models (one for women aged between 40-49 and one for women aged > 50 years) where the linear predictors are:
Model 1 for women aged between 40-49:
Alpha1 + beta4*P
Model 2 for women aged > 50 years:
Alpha2 + beta5*P
Hence, in the above, we could determine if there was a statistical difference between the levels of parity at each age level separately.
Many thanks for your opinion on this.
Kindest Regards,
Kim
Dr Kim Pearce PhD, CStat, Fellow HEA
Senior Statistician
Haematological Sciences
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