Dear All,

For survival analysis, firstly I ran univariate Cox model between my 71 cases and 180 genes.based on that, 70 genes had p-value less than 0.05. Now I would like to fit a multivariate Cox proportional hazards model among 70 significant genes based on below function for Overal Survival with 70 genes expression values:

```
multiple_cox_green_genes <- coxph(Surv(coxSARC_green_data$OS.days, coxSARC_green_data$OS.status) ~ ENSG00000076662 + ENSG00000204475 + ENSG00000111796 + ENSG00000115607+ENSG00000093072 +ENSG00000172543 +ENSG00000255833 + ENSG00000121807 + ENSG00000227507 + ENSG00000122224 + ENSG00000172215 + ENSG00000073861+ENSG00000137078 + ENSG00000182866 + ENSG00000100351 + ENSG00000104814 + ENSG00000196684 + ENSG00000143185 +ENSG00000122188 + ENSG00000128284 + ENSG00000110448 + ENSG00000176083 + ENSG00000113088 + ENSG00000117091 +ENSG00000186810 + ENSG00000205045 + ENSG00000271503 + ENSG00000116824 + ENSG00000266094 + ENSG00000125910 +ENSG00000153563 + ENSG00000198821 + ENSG00000143184 + ENSG00000005844 + ENSG00000147168 + ENSG00000153283 +ENSG00000009790 + ENSG00000078589 + ENSG00000121380 + ENSG00000141293 + ENSG00000169442 + ENSG00000167984 +ENSG00000026751 + ENSG00000117090 + ENSG00000174946 + ENSG00000213886 + ENSG00000162739 + ENSG00000183918 +ENSG00000124203 + ENSG00000110848 + ENSG00000163519 + ENSG00000161405 + ENSG00000115085 + ENSG00000101082 +ENSG00000113263 + ENSG00000125735 + ENSG00000106560 + ENSG00000163508 + ENSG00000089012 + ENSG00000100385 +ENSG00000125347 + ENSG00000117560 + ENSG00000163564 + ENSG00000172673 + ENSG00000163600 + ENSG00000125637 +ENSG00000172116 + ENSG00000145649 + ENSG00000008516 + ENSG00000183813 + ENSG00000160185 + ENSG00000109684
,data = coxSARC_green_data)
```

but I get below Error and warning:

```
Error in fitter(X, Y, strats, offset, init, control, weights = weights, :
NA/NaN/Inf in foreign function call (arg 6)
In addition: Warning messages:
1: In fitter(X, Y, strats, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
2: In fitter(X, Y, strats, offset, init, control, weights = weights, :
one or more coefficients may be infinite
```

I appreciate it if anybody shares his/her comment with me for solving this Error.

Best Regards

Thanks, Dr. Blighe. For 180 genes firstly I have done a univariate cox model. Now, I have 70 genes with a p-value<0.05. Now for final selection, I ran multivariate cox proportional hazards model which I got below error:

So, I appreciate it if you perform your solution for solving my problem. Best Regards,

The model is still too large, I think. Try a lower p-value threshold.

However, it seems that there are also NA values somewhere in your data?

Ok, If I selected genes with p-value smaller than 0.03, Then 70 genes decrease to 40 genes. and my multivariable cox has no problem with 40 genes. but is this threshold acceptable for the scientific reviewers? could you pls guide me? Best Regards,

The lower p-value threshold, the better. Do the genes make sense?

Do you even need to produce this final model? The final message for your work could, instead, be just: '

we identified 40 genes as statistically significant independent predictors through a Cox proportional hazards survival analysis (p<0.03)'. After this, you could do, e.g., gene enrichment on these genes.Ok. Dr. Blighe, In my problem, can I say that genes with lower P-values are more important than genes with higher values?

Yes, generally, but also check the Hazard Ratios. Can you access those?

yes. After univariate Cox analysis, for each gene I have below information:

"Beta" , "StandardError" , "Z" , "P" , "LRT" , "Wald" , "LogRank" , "HR", "HRlower" , "HRupper"

which items except P-Value can help me for the rank of genes?

You should be looking at these:

Ok, You mean that I should consider "LogRank", "HR", "HRlower" , and "HRupper" for ranking my genes. but how can I take into account them at the same time for ranking task?

Best Regards,

The p-value that you use should be the LogRank p-value. We usually quote variables (genes) something like this:

ESR1, p=0.003 (1.5 [1.2; 1.9])

...for data: