I want to obtain the pvalue of the three curves that are plotted. Here is the code that does a survdiff:

```
fitSurvDiffP <- survdiff(formula = Surv(predictI, trainD[,"Status"]) ~ cond)
```

predictI is the predicted time, which is just a numberic vector. trainD[,"Status"] is the status of the patient and cond is a character vector that specifies either "Low", "Med", or "High" risk.

For some reason, however, the output is p = 0. There are numbers for everything else, but I keep getting p = 0 for all 7 of my graphs.

Here's one of my outputs:

```
Call: survdiff(formula = Surv(predictI, trainD[, "Status"]) ~ cond)
N Observed Expected (O-E)^2/E (O-E)^2/V
cond=High 145 97 20.4 287.449 389.40
cond=Low 53 36 102.4 43.080 112.45
cond=Med 206 128 138.2 0.748 2.15
Chisq= 456 on 2 degrees of freedom, p= 0
```

In my experience it's a misunderstanding of what the code is trying to do. I guess goldname98 wants several different p-values, but the code is just telling him "yup, those three conditions are definitely different".

The issue is even more confusing since from statistical (and common sense) interpretation a value p=0 is incorrect, it cannot exist within the framework of statistics.

But that just adds to the conundrum - we have a statistical test where the result is rounded to an incorrect value...