Hello,    I have a questions regarding assessing the proportionality assumption for Fine and Gray model in SAS. I looked at the available resources and I found a SAS tutorial on this, which is great. https://www.sas.com/content/dam/SAS/en_ca/User%20Group%20Presentations/Vancouver-User-Group/Gondara_CompetingRiskAnalysis_May2015.pdf   However, I was tried to reproduce the results following the same steps in the tutorial, I found that there is one step in that I could not understand. The code looks like this:   /**Checking PH assumption:Export Schoenfeld residuals from PHREG**/   proc phreg data=follic plots(overlay=stratum)=cif covs(aggregate) out=estimates; model dftime*cens(0)=agedecade hgb clinstg chemo / eventcode=1; output out=test ressch=WSR_agedecade WSR_hgb WSR_clinstg WSR_chemo; run;   So, this step was very clear to me. However the following step was not clear: /**Checking PH assumption: Merge estimates with residuals and create an adjusted estimate(beta(t))**/ data schoenfeld_data; merge test(keep=dftime by agedecade2 hgb2 clinstg2 chemo2) estimates; by by; rescaled_WSR_agedecade=agedecade2+agedecade; rescaled_WSR_hgb=hgb2+hgb; rescaled_WSR_clinstg=clinstg2+clinstg; rescaled_WSR_chemo=chemo2+chemo; ldftime=log(dftime+1); label rescaled_WSR_agedecade="beta(t) of age per decade" rescaled_WSR_hgb="beta(t) of haemoglobin" rescaled_WSR_clinstg="beta(t) of stage" rescaled_WSR_chemo="beta(t) of chemotherapy" ldftime="log of time"; run; The  used data set can be accessed through: https://support.sas.com/documentation/onlinedoc/stat/ex_code/143/liftcrsk.html   My questions:    1/What is "by" in the merge statement?  2/What are these new variables in the keep statement?  3/Why do we have double "by by" in the by step? 4/ Is there any other ways to modify the code to calculate the rescaled residuals? 5/ Is there any ways to assess the proportionality assumptions for Fine and Gray model?    Thanks in advance for your help?     ... View more

Dear All,    I have a question that I could not find its answer over the last two days. I have time-to-event data, with outcome miscalssification and known sensitivity and specificity. The outcome was derived from administrative databases using a specific algorithm. However, the algorithm is not accurate. The algorithm may misclassify individuals who had the event as event-free and that did not experience the event as diseased. The probability of this error (the sensitivity and specificity of the algorithm) is known.   My question is related to accounting for outcome misclassification and incorporating the sensitivity and the specificity in the simulation for Cox model (proc phreg).  I have seen some people using multiple imputation with internal validation data, but I do not have access to the validation data (data used to calculate the sensitivity and specificity). Alternatively, I have seen other people used Monte Carlo simulation to generate confidence band for the hazard ratios, but I am not sure how I can incorporate the sensitivity and specificity in the simulation (actually, I am not sure I need to incorporate the sensitivity and specificity in the simulation).    My questions:    1/Do I need to incorporate the sensitivity and specificity in the simulation? And is it possible? 2/ Is there any other approach to deal with the outcome miscalssification in survival analysis? 3/ Is there any SAS macro or documetation that I can refer to?    Thanks in advance for your help and support,    Musfer   ... View more