# How to interpret the results of logistic regression if the association is not statistically significant?

I have applied binary logistic regression. My independent variable is bullying victimization (0/1), dependent variable is self-cutting (0/1), covariates are age, gender, school performances, depression, family relationship and so on. In model I in adjusted only bullying victimization and self-cutting, i.e. crude odds ratio 2.27 (1.52-3.39). In model II I adjusted age, gender and bullying victimization 2.89 (1.87-4.47), in model III age, gender, bullying victimization and school performance 2.73 (1.74-4.28), and like that.

However, when I adjusted depression, age, gender and bullying victimization it shows not statistically significant association i.e. 1.40 (0.85-2.31). And because of the depression, when I adjusted all the covariates together in my final model, the association between my independent variable (BV) and self-cutting is not statistically significant.

So how to interpret this particular odds ratio and CI 1.40 (0.85-2.31) which was after adjusting depression in the model? I cannot exclude or remove depression as it is my main covariate that is why I didn't build models with stepwise forward and backward, instead I chose subsets.

When the results is statistically significant it is easier to interpret but how to interpret the results when the association is not significant?

Model I 2.27 (1.52-3.39)    <0.001
Model II    2.89 (1.87-4.47)    <0.001
Model III   2.73 (1.74-4.28)    <0.001
Model IV    2.48 (1.58-3.91)     <0.001
Model V     1.92 (1.21-3.03)     0.005
Model VI    2.36 (1.65-4.29)    <0.001
Model VII   3.69 (2.29-5.95)    <0.001
Model VIII  2.10 (1.27-3.34)    0.003
Model IX    1.40 (0.85-2.31)    0.186
Model X     1.62 (0.89-2.93)    0.112


Model I: Crude odds ratio for the association of self-cutting with bullying victimization Model II: Adjusted for age and gender Model III: Adjusted for age, gender, and school performance (mathematics, science, native language (Finnish) and general subjects) Model IV: Adjusted for age, gender, and fear of going to school Model V: Adjusted for age, gender, and social network-related factors (number of friends, feeling lonely) Model VI: Adjusted for age, gender, and family-related factors (parents’ marital status, relationship with parents, relationship with siblings) Model VII: Adjusted for age, gender, and lifestyle risk factors (use of drugs, use of cannabis, smoking, AUDIT-C) Model VIII: Adjusted for age, gender, and A-DES score Model IX: Adjusted for age, gender, and BDI score Model X: Adjusted for all the variables