What is your 'multivariate analysis of survival?' What is your data set? What is the struggle?
That question is easy to answer! Start by opening JMP and selecting Help > Reliability and Survival. See the chapters about the Fit Parametric Survival and the Fit Proportional Hazards platforms. These methods are documented and the examples are shown through out the descriptions.
To do a survival analysis you need to have an entry date such as the diagnosis date or date of the first treatment. The patients will have been followed and some unfortunately have died, for these the date of the last control will be the date of death. They are called uncensored since they are no longer in the census. It is important that in the follow-up of the patients you do not have lost patients or that these are limited to a low percentage, less than 10%. When you consider doing the analysis you indicate a date on which you have the follow-up of all. The difference between the last control date and the diagnosis date will provide you with survival. In another column you must have for each person the vital state in the last control, for the already deceased JMP by default requires that a 0 and 1 be recorded for the living or censored. If this you already knew sorry but it was important to explain.
Before going on to do the multivariate analysis, I advise first to see what role your prognostic variables have. For this, in the Reliability and Survival submenu select Survival and in Grouping you select the variable you want to analyze, in Y, time to event you select survival and in Censor the column with the vital state in the last control. You do this for each of the forecast variable you have. In this part the numerical variables cannot be analyzed if you have not categorized them before (age over 70 or less, for example).
I suggest doing this before any multivariate analysis because, imagine that you do not find any prognostic variable with statistical significance, in general, no further analysis would be necessary. On the other hand, in case of finding prognostic variables with statistical significance, the univariate analysis helps you to select them in the multivariate model, whose objective is to indicate which of these variables are the really prognostic ones.
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