Hi All,

I am wondering how to derive HR, CI and p values for each factor from cox model like follows. Using coxph only gives these values for groups such as BRCA status, TUmor stage ...

THanks.

cox proportional hazard model

4

Entering edit mode

Hi All,

I am wondering how to derive HR, CI and p values for each factor from cox model like follows. Using coxph only gives these values for groups such as BRCA status, TUmor stage ...

THanks.

8

Entering edit mode

**Update 13th March, 2019:**

I posted a related tutorial: Survival analysis with gene expression

**Update 24th September, 2018:**

Note that the actual plot does not match the data, and neither do the stat values. Everything here is purely for display purposes only.

This would have been performed in the realm of survival analysis, looking at overall survival (OS) and progression-free survival (PFS), as you can probably see.

The starting point for the Cox Proportional Hazards Regression (Cox) is data in this format:

```
head(df)
OS Event Group
1 1065 0 group1
2 0 0 group2
3 883 0 group1
4 33 1 group2
5 790 0 group1
6 2517 1 group2
```

The columns are

- OS: overall survival (days, weeks, months, years - just needs to be consistent)
- Event: e.g. death, diagnosis, or some other event
- Group: the categories of interest - can be anything such as ER status, IHC scores for CD20, race, or something else

Cox is run with coxph in R, and it needs to be performed on a survival object, e..g, produced by Surv

As per the table (above), there is a reference level for the category of interest, e.g., *BRCA wild-type*. Thus, we must also choose a reference category against which all other categories will be compared (here *group1* is the reference):

```
df$Group <- factor(df$Group, levels=c("group1","group2","group3","group4"))
df$Group
[1] group1 group2 group1 group2 group1 group2 group3 group2 group3 group2
[11] group1 group4 group4 group3 group4 group4 group2 group3 group1 group3
[21] group4 group4 group4 group1 group3 group3 group2 group1 group3 group4
[31] group1 group1 group4 group2 group3 group3 group4 group3 group2 group4
[41] group4 group3 group3 group4 group4 group4 group3 group2 group2 group1
*et cetera*
Levels: group1 group2 group3 group4
```

Now we can actually generate hazard ratios (including CIs) and P values:

```
coxmodel <- coxph(Surv(time = OS, event = Event) ~ Group, data=df)
summary(coxmodel)
Call:
coxph(formula = Surv(time = OS, event = Event) ~ Group, data = df)
n= 106, number of events= 106
coef exp(coef) se(coef) z Pr(>|z|)
Groupgroup2 0.15929 1.17267 0.29957 0.532 0.595
Groupgroup3 0.03724 1.03794 0.27747 0.134 0.893
Groupgroup4 -0.14772 0.86267 0.28570 -0.517 0.605
exp(coef) exp(-coef) lower .95 upper .95
Groupgroup2 1.1727 0.8528 0.6519 2.109
Groupgroup3 1.0379 0.9634 0.6025 1.788
Groupgroup4 0.8627 1.1592 0.4928 1.510
Concordance= 0.515 (se = 0.032 )
Rsquare= 0.011 (max possible= 0.999 )
Likelihood ratio test= 1.19 on 3 df, p=0.7566
Wald test = 1.18 on 3 df, p=0.7575
Score (logrank) test = 1.19 on 3 df, p=0.7563
```

The P values for each category are given by **Pr(>|z|)**. The HRs are given by **exp(coef)**. and you can probably guess the CIs. Just to be sure, here are the HRs with 2.5% and 97.5% CIs:

```
exp(confint(coxmodel))
2.5 % 97.5 %
Groupgroup2 0.6519067 2.109444
Groupgroup3 0.6025433 1.787944
Groupgroup4 0.4927892 1.510188
```

Finally, you can then actually plot the Kaplan-Meier survival curve for this using a wrapper, km.coxph.plot:

```
km.coxph.plot(formula.s=Surv(time=OS, event = Event) ~ Group, data.s=df, mark.time=TRUE,
x.label="Time (days)", y.label="Overall survival", main.title="",
leg.text=c("Group1","Group2","Group3", "Group4"), leg.pos="topright", leg.bty="n", leg.inset=0,
.col=c("limegreen","royalblue","purple","red1"),
o.text="",
.lty=c(1,1,1,1), .lwd=c(1.75,1.75,1.75,1.75), show.n.risk=TRUE, n.risk.step=500, n.risk.cex=0.8, verbose=FALSE)
mtext(side=3, line=-1, adj=-0.25, "Cox PH survival", cex=3)
mtext(side=3, line=-13, adj=0.95, "HR=2.95 (0.52, 16.62), p=0.2", cex=0.8, col="red")
```

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Thanks, Kevin. It looks cool. But it is for univariable test? How about multivariable, e.g. BRCA status, Tumor stage and Residual tumor?

If you want to further subdivide your cohort, then you could just create new categories, like, for example:

You can also adjust for other factors / covariates in the Cox model, or do interactions, which is an alternative hypothesis to the above but still useful:

I recall having a conversation in this regard last year amongst a group of statisticians (i.e., interaction terms in a Cox model - from what I recall, it's a somewhat unexplored area).

Thanks. But it looks the reference for each category can not be defined? E.g. Stage I as reference in Tumor stage and BRCA wildtype in BRCA status?

Or in above table, the reference was defined individually, which means three testes were performed to generate the table of PFS or OS?

In that table that you posted, the reference levels are '

BRCAwild-type', 'Tumour Stage II', and 'Residual Tumour 0'. They did not have data for Stage I or they just did not consider it for the study.They would have used 6 tests (3 for Os; 3 for PFS) to generate the results in that table.

These would have been the models:

Great, thanks.

Sorry for one more question. df.OS you have put into the models are: BRCA1mutation BRCA2mutation BRCA1methylation,

or:

BRCA1mutation.StageII BRCA1mutation.StageIII and IV BRCA2mutation.StageII BRCA2mutation.StageIII and IV

For the first OS model, the data would be:

For PFS, it would be:

Then, there are 4 more different models:

Hope that this helps.

Thanks. Nice tutorial.