I performed association study of SNPs with type 2 diabetes and diabetic retinopathy. I am confused whether I need to apply Bonferroni correction after multivariate logistic analysis. Even, for haplotype analysis is it necessary to consider Bonferroni correction? Because I am getting p value less than 0.05 but greater than threshold p value after correction in both the cases. So how to interpret? Any suggestion?
The correction would give you the possibility to control the probability of "rejecting at least one tested hypotheses under H0".
Is this important in your research?
And if so: what level would be sensible? Or: Is 5% a sensible choice?
If you screen several different SNPs for their correlation with diabetes, one can expect getting some small p-values even without assuming that diabetes is linked to any of the SNPs at all. Picking out the smallest p-values will therefore give a wrong impression of the (statistical) significance of the data. So in this case giving a corrected p-value is less misleading.
Thank you, Jochen Wilhelm, for the suggestion. So, what if p is less than 0.05 and greater than corrected threshold p? Can I say the SNP is weakly associated with diabetes?
I wouldn't draw a conclusion based on a p-value at all.
If you designed an experiment to specifically test the diabetes-association of one particular SNP because you had some prior hypothesis why this should play a role, then a large p-value would tell you that your data is insufficient to show that association (your data is inconclusive). If the p-value is small, then this is only the very first step towards an interpretation that should appreciate the rest of the biological knowledge about the role of this SNP in the pathology of diabetes and the estimated effect size.
If you did a screening, p-values (corrected or not) are essentially only a proxy to rate the results according to their "statistical signal-to-noise ratio". A good ratio (= a small p-value) can be the result of a strong signal or the result of a small noise. I would use the results only to generate a ranked list to highlight the "top candidates" (according to thier "statistical signal-to-noise ratio" or p-value). I would see it only as an experiment-internal feature, not to generalize. The aim of a screening is to identify the most promising candidates for further studies. This essentially means: a screening is done to generate hypotheses, not to test them.
And please do not confuse statistical significance (p-values) with effect sizes. A tiny p-value does not (neccesarily) indicate a strong association, and a strong association may give a large p-value. These are two different things only indirectly linked via the model (what cofactors are considered), the sample size (what one can experimentally control) and the variance (what one cannot control).
- Badges
- Science topic
- Similar topics
- Mathematical Sciences
- Statistics
- Statistical Software
- SPSS