**150**wrote:

I have dataframe `data`

with columns like FollowUpDays, patient_vital_status and high, low information of a gene.

I'm trying to do survival analysis using the Followup information, patient_vital_status and the information of gene. I'm using like below:

```
surv_diff <- survdiff(Surv(FollowUpDays, patient_vital_status) ~ ENSG00000001460,
data = data)
surv_diff
Call:
survdiff(formula = Surv(FollowUpDays, patient_vital_status) ~
ENSG00000001460, data = data)
N Observed Expected (O-E)^2/E (O-E)^2/V
ENSG00000001460=high 332 57 70.5 2.58 5.99
ENSG00000001460=low 264 67 53.5 3.40 5.99
Chisq= 6 on 1 degrees of freedom, p= 0.01
```

From the above I could say that log rank test for difference in survival gives a p-value of p = 0.01, indicating that the Expression groups high and low differ significantly in survival.

To check the median of both the groups which tells us which group is good or bad for prognosis, I used like below:

```
library(survival)
fit <- survfit(Surv(FollowUpDays, patient_vital_status) ~ ENSG00000001460,
data = data)
print(fit)
Call: survfit(formula = Surv(FollowUpDays, patient_vital_status) ~
ENSG00000001460, data = data)
n events median 0.95LCL 0.95UCL
ENSG00000001460=high 332 57 NA 2134 NA
ENSG00000001460=low 264 67 1741 1503 NA
```

From the above I see that median of high group is `NA`

and 0.95UCL is also `NA`

for both the groups.

If the median of one of the group is `NA`

how can I say which group is worse for prognosis? Can anyone tell about these `NA's`

here.

Any help is appreciated. thanq

**20k**• written 5 months ago by Biologist •

**150**