I have survival fraction data of to cell line in different radiation dose.how can I calculate alpha and beta and how can I fit graph to this data with graphpad prism 6 software .on the other hand how can I determine statistically difference between two cell group?
Hello Saeid,
I don't know if you have found a solution to your question but I think this link will be of relevance to you.
http://www.graphpad.com/support/faqid/2039/
Graphpad support I believe are planning to include the linear quadratic model of radiation cell survival curves in ther next version.
As far as the alpha and beta ratios are concerned the references maybe useful at the end if this link. Or I would recommend anything written by Jack Fowler.
Hope this helps
Regards
Hello,
I used graphpad prism 5 to plot survival curves. I don't know what with graphpad prism 6. May be both are not so different.
With graphpad prism 5, you have to choose the survival button then you choose the kind of graph you want and the way to express your results (as fractions, percent...). After, you enter data as follows:
Entering survival data
Each row represents a distinct subject. (You can optionally use row titles to identify the subjects.) Each column represents a treatment.
· Enter elapsed time (usually in days) as X (Prism does not let you enter dates).
· For each subject, enter a code for Y:
· Enter ‘1’ for a subject if the event (death) occurred at the time entered into X.
· Enter ‘0’ if the data are censored. Data are censored when you simply don’t know what happened to the subject after that time, or do know but can’t use the
We could plot a survival curve for each cell line and compared them as follows:
Interpreting results: Comparing two survival curves
Two methods to compute P value
Prism compares two survival curves by two methods: the log-rank test (also called the Mantel-Cox test) and the Gehan-Breslow-Wilcoxon test. It doesn't ask for your preference, but always reports both.
· The log-rank (Mantel-Cox) test is the more powerful of the two tests if the assumption of proportional hazards is true. Proportional hazards means that the ratio of hazard functions (deaths per time) is the same at all time points. One example of proportional hazards would be if the control group died at twice the rate as treated group at all time points. Prism actually computes the Mantel-Haenszel method, which is nearly identical to the log-rank method (they differ only in how they deal with two subjects with the same time of death).
· The Gehan-Breslow-Wilcoxon method gives more weight to deaths at early time points. This often makes lots of sense, but the results can be misleading when a large fraction of patients are censored at early time points. In contrast, the log-rank test gives equal weight to all time points. The Gehan-Wilcoxon test does not require a consistent hazard ratio, but does require that one group consistently have a higher risk than the other.
You need to choose which P value to report. Ideally, this choice should be made before you collect and analyze your data.
If in doubt, report the log-rank test (which is more standard) and report the Gehan-Wilcoxon results only if you have a strong reason.
Note that Prism cannot perform Cox proportional hazards regression.
If two survival curves cross, then one group has a higher risk at early time points and the other group has a higher risk at late time points. In this case, neither the log-rank nor the Wilcoxon-Gehan test rests will be very helpful.
Interpreting the P value
The P value tests the null hypothesis that the survival curves are identical in the overall populations. In other words, the null hypothesis is that the treatment did not change survival.
The P value answers this question:
If the null hypothesis is true, what is the probability of randomly selecting subjects whose survival curves are as different (or more so) than was actually observed?
Prism always calculates two-tail P values. If you wish to report a one-tail P value, you must have predicted which group would have the longer median survival before collecting any data. Computing the one-tail P value depends on whether your prediction was correct or not.
· If your prediction was correct, the one-tail P value is half the two-tail P value.
· If your prediction was wrong, the one-tail P value equals 1.0 minus half the two-tail P value. This value will be greater than 0.50, and you must conclude that the survival difference is not statistically significant.
For more informations you have the graphpad prism "help" section .
Hope my answer will help you.
Best regards
Anne Marie
Hello Saeid,
I don't know if you have found a solution to your question but I think this link will be of relevance to you.
http://www.graphpad.com/support/faqid/2039/
Graphpad support I believe are planning to include the linear quadratic model of radiation cell survival curves in ther next version.
As far as the alpha and beta ratios are concerned the references maybe useful at the end if this link. Or I would recommend anything written by Jack Fowler.
Hope this helps
Regards
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