I want to create 5000 bootstrap samples from patient level data stratified by treatment and with a MAIC weight.   My question are: 1) How to sample using proc surveyselect using a weight, 2) when you use one of the samples in proc lifereg do you use the original weight, or assume that the weight in now incorporated into the sample though the weighting procedure   I use the method pps_wr in proc surveyselect, and using the weight with the size option. data patients;   length treatment $10;   set os;   keep usubjid treatment weight; run;   proc sort data=patients;   by treatment usubjid; run;   proc sql;   create table sampsize as   select treatment, count(*) as _nsize_     from patients     group by treatment; quit;   proc surveyselect data=patients(keep=usubjid treatment weight)   out=patients_samples(keep=sample usubjid treatment weight)   sampsize=sampsize   method=pps_wr   reps=5000(repname=sample)   seed=8675309   outhits noprint;   strata treatment;   size weight; run;   data os_samples;   merge os (in=a keep=usubjid time event weight)   patient_samples(in=b);   by usubjid;   if a and b; run;   proc sort data=os_samples;   by sample usubjid; run;   /*** For this last    /* Note: I'm actually using flexsurv in R at this point */ proc lifereg data=os_samples;   by sample;   class treatment;   model time*event(0) =treatment / distribution = weibull;   weight weight; /* Should this statement be included ? */ run;   So again, should this weight statement be included in samples that were created with the weight, or should it be dropped at this point?   ... View more

I'm trying to develop some skill using proc nlmixed. I like the procedure because you can program ideas directly that you find in papers. I found an example using negative binomial data. the example was taken from the source in the link below, so a test is to match the results. Unfortunaly, my model does not solve for the alpha parameter, even when I give it the starting value given in my solution. However, if I enter alpha as a constant the model gets solved and all the other parameters are the same as in my example. I would like to have a strategy for how to handle a case when a parameter will not converge and I don't know before hand what the answer should be.   link: https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Negative_Binomial_Regression.pdf   data koch36; length Melanoma Area 8 AgeGroup $10 Population AG1 AG2 AG3 AG4 AG5 AG6 8; infile datalines dsd; input Melanoma Area AgeGroup $ Population AG1 AG2 AG3 AG4 AG5 AG6;   logpop=log(Population); datalines;   61,0,<35,2880262,1,0,0,0,0,0 76,0,35-44,564535,0,1,0,0,0,0 98,0,45-54,592983,0,0,1,0,0,0 104,0,54-64,450740,0,0,0,1,0,0 63,0,65-74,270908,0,0,0,0,1,0 80,0,>74,161850,0,0,0,0,0,1 64,1,<35,1074246,1,0,0,0,0,0 75,1,35-44,220407,0,1,0,0,0,0 68,1,45-54,198119,0,0,1,0,0,0 63,1,54-64,134084,0,0,0,1,0,0 45,1,65-74,70708,0,0,0,0,1,0 27,1,>74,34233,0,0,0,0,0,1 ; run;   　 　 proc nlmixed data=koch36 gconv=0; parms alpha=0.27586 intercept=0 a1=0 b1=0 b2=0 b3=0 b4=0 b5=0;   y=Melanoma; t=population; mu=exp(log(t)+Intercept+a1*area+b1*(AgeGroup="35-44")+b2*(AgeGroup="45-54")+b3*(AgeGroup="54-64")+ b4*(AgeGroup="65-74")+b5*(AgeGroup=">74")); llike=lgamma(y+1/alpha)-lgamma(1/alpha)-lgamma(y+1)-log(1+alpha*mu)/alpha-y*log(1+alpha*mu)+y*log(alpha)+y*log(mu); model y ~ general(llike); run; proc nlmixed data=koch36 gconv=0; parms intercept=0 a1=0 b1=0 b2=0 b3=0 b4=0 b5=0; alpha=0.27586;   y=Melanoma; t=population; mu=exp(log(t)+Intercept+a1*area+b1*(AgeGroup="35-44")+b2*(AgeGroup="45-54")+b3*(AgeGroup="54-64")+ b4*(AgeGroup="65-74")+b5*(AgeGroup=">74")); llike=lgamma(y+1/alpha)-lgamma(1/alpha)-lgamma(y+1)-log(1+alpha*mu)/alpha-y*log(1+alpha*mu)+y*log(alpha)+y*log(mu); model y ~ general(llike); run;       ... View more