I have multiple samples within each patient and would like to see what are some ways to analyze that data (other than using a repeated measures Cox regression)   Thanks!   ... View more

I would like to understand what it means (in the clinical sense and from a statistics perspective) when you run a K-M analysis by stratifying on the censoring variable. E.g. Proc lifetest; time PFS*censor (1); strata censor ; run; I am asking this question because the stratifying variable in my analysis is highly correlated with the censor variable. I would like to gather enough talking points to discourage the approach. It would be great to hear your thoughts and opinions.    Thank you! ... View more

I ran a Proc phreg with 2 continuous variables and 3 binary variables. One of the continuous variables (ranges between 10 and 250) is highly significant but the HR is only 1.013. This is from the "Analysis of Maximum Likelihood Estimates" table. Any thoughts on what might cause such results? I have a reasonable sample size of 150 observations. The binary variables have a distribution of <10 yeses and 140 "no"s   ... View more

I ran proc lfietest to generate KM curves using the following code: proc lifetest data= dat; time Survival*censor(1) ; strata group   /test=(logrank); run;   Since I wanted the hazard ratios for this, I ran the following Proc Phreg code: proc phreg data=dat; class group; model Survival*censor(1) =group ; run;   The HR came out to be very high -a seven digit number (3797239) and not significant - pvalue=0.99. Can someone help me understand this?   Thanks so much! ... View more

Hello, I have about 7 independent variables that have a non linear relationship with my 2 potential dependent variables (non linearity is expected and the loess curves I generated confirm the same). The real dependent variable is actually a 4 category ranking (it would be okay to use data for/from only 2 rankings if there is a need to make it binary, as the dataset is quite large). It is believed (and true for the most part) that the "4 category ranking" is affected by the "2 potential dependent variables" and hence they are potential dependent variables. The goal of this analysis is to build a scoring system using the trends displayed by the independent variables. I used proc loess to look at trends and establish various cutoffs for a potential score. However, some of the independent variables interact and it will be nice to take that into account when determining the "score" that combines the 7 independent variables. This score will be used to predict the 2 or 4 category ranking on future data. I have not run non-parametric models before and I am trying to create a formula for the "score". I tried proc gam for the multivariate model (dependent variable=2 categories of the 4 category ranking) but I am not sure if that is the best method. Please offer your ideas and suggestions. Thanks! ... View more

Hi, It will be great if somebody could help me out here. I am trying to create this new variable from my existing dataset. In my current dataset I have variables section1 and section2 and other variables var1_1, var_3, var1_4, etc based on the value of the variables section1 and section2. I am trying to create 2 new variables that contain the value of variables var_i, var_j where "i" and "j" are the values of variables section1 and section2. I don't fully understand how symput works but have the following working code. Somehow hist1 and hist2 only show the value of the corresponding var_k for the very last record in the dataset. And also, how do I convert the macro variable to a variable in the dataset "sections_mod"? data sections_mod; set sections end=eof; if eof then do;     call symput("num1", trim(compress(Section1))); call symput('Hist1', VAR_&num1); hist1= &Hist1;     call symput("num2", trim(compress(Section2))); call symput('Hist2', VAR_&num2); hist2= &Hist2; end ; run; %put _user_; Any thoughts? Thanks, Aarthi ... View more