I am a physician and clearly not a statistician (I try to understand why and how to perform the right analysis, but I don't understand the formulas). I am using SPSS v.19 in analyzing my data. I try to analyze if a biomarker is associated with an increased mortality risk in a subpopulation from my study and I don't know if my analysis is correctly performed.

This the result (part of it) for the entire population: enter image description here

This is for the one of the subgroups: enter image description here

This is for the other subgroup: enter image description here

In this situation, I wanted to see if the association with the mortality risk in the second group was truly significant and I performed a Cox survival analysis with an interaction term: Biomarker value*LOT_1: enter image description here

Is this analysis correct? And if it is, from this output I should understand that there is no statistically significant association with mortality for the studied biomarker in the second subgroup (no difference for this association in between the two subgroups)? Thank you.

LE: This is the output including also the main effect of LOT_1. enter image description here

  • $\begingroup$ Did you include the main effect of Lot1 in the final analysis you show? You should. $\endgroup$ – Peter Flom Apr 17 '14 at 11:48
  • $\begingroup$ I performed an analysis including also the main effect for LOT_1. Actually, this analysis made me wonder if my conclusions were correct.. $\endgroup$ – Dimitrie Apr 17 '14 at 11:57

You should almost never include an interaction without both main effects.

In your final table, the interaction and the LOT variable are highly significant and Biomarker is not sig and is also small.

In summary, this means that Biomarker has little effect when LOT1 = 0 (which would be that it is the other lot) but has more of an effect when LOT1 = 1.

(The above assumes that LOT1 is coded 0-1)

  • $\begingroup$ So my conclusion is correct. In the second subgroup (but not in the first) my biomarker is associated with mortality. My concerns were raised by two issues: $\endgroup$ – Dimitrie Apr 17 '14 at 13:30
  • $\begingroup$ 1. In univariate analysis the LOT1 groups weren't associated with mortality and now is highly significant...2. Performing an identical analysis in a different subset of mortality (cardiovascular mortality) yielded the same statistically significant result for the interaction term, although in univariate analysis the biomarker wasn't associated with the outcome - CV mortality (in neither of the two subgroups)... $\endgroup$ – Dimitrie Apr 17 '14 at 13:46
  • $\begingroup$ When there is a large interaction effect, the analysis of main effects alone is not appropriate. You can get all kinds of results. $\endgroup$ – Peter Flom Apr 17 '14 at 14:14

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