IYH Dear Gishnu Harikumar Parvathy

Short answer: Yes, the suggested approach is feasible provided that all necessary conditions for Pearson product-moment correlation coefficient calculations are satisfied.

Your proposed approach aims to quantify the difference in correlation between two groups (females and males) regarding each parameter and assign weights based on the strength of association with survival within each group.

While conceptually intuitive, there are some points worth considering to better interpret the results and validate the chosen methodology

Overall, your procedure appears reasonable, although interpreting interaction effects in correlation coefficients isn't straightforward compared to regression models. Using absolute correlation differences instead could simplify interpretation and make comparisons easier. Also, squaring the correlation coefficients before multiplying them with their respective maximum magnitudes introduces heterogeneity due to different scales used for weight assignment so consider normalizing weights according to the scale of each variable.

Pros:

- Highlights the relative impact of individual parameters on survival disparities between genders.

- Provides a simple framework for comparing strengths of association and ranking relevant factors.

- Encourages model exploration and promotes interactive engagement with data.

Cons:

- Interpretation becomes challenging due to composite scoring schemes involving differences and products of correlation coefficients.

- Assumes equal variances for each gender despite having unequal sample sizes, possibly affecting Type I error rates.

- Relies solely on bivariate correlations without accounting for interactions or higher order terms present in real-world scenarios.

To strengthen your analysis, I have following suggestions:

0. Be sure to validate assumptions concerning normality, independence, homoscedasticity, and outliers.

1. Use partial or semi-partial correlations to account for shared covariances with third variables.

2. Apply multivariate regression models (linear, logistic, Cox) allowing for direct comparison of effect sizes and assessment of confounding factors.

3. Utilize formal statistical testing procedures (eg Williams' Test for Equality of Correlation Coefficients) tailored for interaction contrasts or moderator analysis.

Dear Daniel,

Thanks very very much for your super detailed reply. Indeed, I have same numbers in each case. On my original thought, I had not planned to square the difference between correlations before multiplying then with the highest magnitude among the two, for each parameter. I will think about your other suggestions too.

Thanks again for sharing your advice and insights.

Best,

Gish

You compare correlation coefficients by transforming the correlation coefficient values, or r values, into z scores and comparing these :)

Thank you very much :)