Questions on under­standing the 4-way RSABE method SAS code [RSABE / ABEL]

posted by WinnieH – Sweden, 2023-01-04 14:33 (34 d 10:33 ago) – Posting: # 23417
Views: 560

Dear all,

I am a new learner and I am trying on understanding the 4-way RSABE method SAS code provided by FDA:
/*Intermediate analysis – ilat*/ 
proc mixed data=scavbe;
  class seq;
  model ilat =seq/ddfm=satterth;
  estimate 'average' intercept 1 seq 0.5 0.5/e cl alpha=0.1;
  ods output CovParms=iout1;
  ods output Estimates=iout2;
  ods output NObs=iout3;
run;
data ilat;
  set iout2;
  pointest=exp(estimate);
  x=estimate**2-stderr**2;
  boundx=(max((abs(lower)),(abs(upper))))**2;
/*Intermediate analysis – dlat*/ 
proc mixed data=scavbe;
  class seq;
  model dlat=seq/ddfm=satterth; estimate 'average' intercept 1 seq 0.5 0.5/e cl alpha=0.1;
  ods output CovParms=dout1;
  ods output Estimates=dout2;
  ods output NObs=dout3;
run;
data dlat;
  merge dout1(keep=estimate) dout2(keep=df);
  s2wr=estimate/2;
  dfd=df;
run;
/*Criterion Bound */ 
data results;
  merge ilat(keep=pointest x boundx) dlat(keep=s2wr dfd);
  theta=((log(1.25))/0.25)**2;
  y=-theta*s2wr;
  boundy=y*dfd/cinv(0.95,dfd);
  sWR=sqrt(s2wr);
  critbound=(x+y)+sqrt(((boundx-x)**2)+((boundy-y)**2));
run;


I found some discussion about the S2wr and it is really helpful to me. About the ilat, my understanding is that the model is a simple linear model and the estimate is the least square mean. My R code example is as follows:
  mod<-lm(ilat~SEQ, dataset)
  result <- confint(emmeans(mod, specs=~1), level = 0.9)
  pointest <- exp(result$emmean)
  x <- (result$emmean)^2- (result$SE)^2
  boundx <-(max(c(abs(result$lower.CL), abs(result$upper.CL))))^2

My question is 1) how to interpret x and critbound=(x+y)+sqrt(((boundx-x)**2)+((boundy-y)**2))? Is there any related explanation about them? 2) Has anyone tried to reproduce this FDA code in R before? I would like to get some hints from you:-)

Thank you very much and wish you a happy new year!

Bests,
Winnie

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