Hello Community,
I am requesting for assistance, I want to calculate Royston's D index for my cox model but I have failed to come across any procedure that I can use to calculate it.
Thanks in advance.
Please supply a reference for the statistic.
Please also let us know your version of SAS and whether you have a license for the SAS/IML product.
I hope that someone who knows more about survival analysis than I do will chime in, but it sounds like the main steps in this analysis are:
1. Use the PHREG procedure to fit a Cox regression model to the data and obtain the linear predictor.
2. Make a normal Q-Q plot of the linear predictor. Use OLS (PROC REG) to estimate the slope, sigma.
3. D = sqrt(8/pi) * sigma.
I assume you know how to use the ODS OUTPUT statement to capture statistics from a table and put it in a SAS data set.
A note on creating the normal Q-Q plot. You can use PROC UNIVARIATE to compute the Q-Q plot and to output the quantiles. For example, the following gets the coordinates for a Q-Q plot and performs a linear regression:
proc univariate data=sashelp.class;
var weight;
qqplot weight;
ods output qqplot = qq; /* put quantiles and data in QQ data set */
run;
proc reg data=QQ plots(only)=fitplot;
model data=quantile;
run;
Hello Rick,
I have attempted to do it as follow, I request for cross examination to know if am following the procedures well,
Proc phreg data=model;
class Sex(ref='1') AGE(ref='1');
model Years * censor (0)= Sex AGE/ rl
Output Out=Model survival=survest;
run;
proc univariate data=Model;
var survest;
qqplot survest;
ods output qqplot = qq; /* put quantiles and data in QQ data set */
run;
proc reg data=QQ plots(only)=fitplot;
model data=quantile;
run;
/* I got a standard deviation of 0.66196 which I presumed to be the Sigma you talked about and I replaced pi with its standard value of 3.14159265359, then I created the data below containing the desired statistics*/
Data Royston; set QQ;
Roystons_D = sqrt(8/3.14159265359) * 0.66196;
run;
I have some question,
What is the purpose of the following statement in the procedure where I have the following
model data=quantile;
I want to confirm whether I did it in the right way.
I will be glad for some clarifications.
> I request for cross examination to know if am following the procedures well
You appear to have followied my suggestions, but as I said, I am not an expert in survival analysis, and I have never heard of Roysten's D. What I would do is to rerun one of the examples in the references: get the data that they used and make sure you get the same answer.
> What is the purpose of the following statement in the procedure where I have the following: model data=quantile;
The ODS OUTPUT statement sends the data in the Q-Q plot to the 'QQ' data set. If you use PROC PRINT, you will see that the X variable in the Q-Q plot is named 'QUANTILE' and the Y variable is named 'DATA'. Thus the statement MODEL DATA = QUANTILE
asks PROC REG to regress Y onto X, which are the observed rank-order statistics against the corresponding normal quantiles. The slope of the regression line is the parameter estimate that you want. When Y is normally distributed, the slope of the Q-Q plot should be close to the standard deviation of Y.
I know I am 'late' to this conversation, but I was similarly looking to calculate the D-Index and had the advantage of R code created by another statistician to use as I was working through steps. Royston and Sauerbrei's original article has a bit more detail than the description in Austin et. al. and it makes a difference in the final calculations. I'll post the macro I created as soon as I am on the active enough on the community to do so.... (Or if there is too much of a delay, I'll have a colleague do it for me 🙂
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