Independent t-test with PROC MIXED

proc ttest data=anorexia (where=(treat in ("Cont","CBT"))); 
  class treat;
  var prewt;
run;

/* equal variance */
proc mixed data=anorexia (where=(treat in ("Cont","CBT"))); 
  class treat;
  model prewt = treat;
run;

/* unequal variance */
proc mixed data=anorexia (where=(treat in ("Cont","CBT"))); 
  class treat;
  model prewt = treat / DDFM=Satterthwaite;
  repeated / group=treat;
run;

Paired t-test with PROC MIXED

proc import datafile="C:\projects\Endocrine\CGMS\data\ex\anorexia.csv" out=anorexia 
            dbms=csv replace; getnames=yes;
run;

proc sql;
  create table cbt_long as
  select  var1 as patient, 0 as time, prewt as y 
  from anorexia (where=(treat="CBT"))  
  union
  select  var1 as patient, 1 as time, postwt as y 
  from anorexia (where=(treat="CBT"))  
;quit;

proc mixed data= cbt_long;
  class patient;
  model y = time / s;
  random patient;
*  repeated / subject=patient type=cs rcorr;
run;

/* Paired t-test with SQL */
proc sql;
  select t(postwt-prewt) as t, prt(postwt-prewt) as p_value
  from anorexia (where=(treat="CBT"))  
;quit;

PROC SQL: functions

COUNT, FREQ, N: number of nonmissing values

NMISS: number of missing values

MIN: smallest value

MAX: largest value

RANGE: range of values

SUM: sum of values

SUMWGT: sum of the WEIGHT variable values(footnote 1)

AVG, MEAN: means or average of values

T: Student's t value for testing the hypothesis that the population mean is zero

PRT: probability of a greater absolute value of Student's t

USS: uncorrected sum of squares

CSS: corrected sum of squares

VAR: variance

STD: standard deviation

STDERR: standard error of the mean

CV: coefficient of variation (percent)

array & output

%let ntime=288;
data series (drop=j);
array c(30) ;
array s(30);
  do time=1 to &ntime; 
    do j=1 to 30;
      c[j]=cos(2*3.141593*j* time/&ntime); s[j]=sin(2*3.141593*j* time/&ntime);
    end;
    output;
  end;
run;