[R-sig-ME] Converting SAS code into R

[Nicholas Mitsakakis]

I have been trying to convert some PROC MIXED SAS code into R, but without
success. The code is:

proc mixed data=rmanova4;class randomization_arm cancer_type site wk;
model chgpf=randomization_arm cancer_type site wk;
repeated  / subject=study_id;
contrast '12 vs 4' randomization_arm 1 -1;
lsmeans randomization_arm / cl pdiff alpha=0.05;
run;quit;

I have tried something like

lme(chgpf ~ Randomization_Arm  + Cancer_Type + site + wk, data=rmanova.data,
            random = ~ 1  | Study_ID,
            correlation = corSymm(form = ~ 1 | Study_ID),
            na.action=na.exclude, method = "REML")

but I am getting different estimate values.

Perhaps I am misunderstanding something basic. Any comment/suggestion would
be greatly appreciated.

I am adding here the output. Part of the output from the SAS code is below:

Least Squares Means
Effect  Randomization_Arm   Estimate    Standard Error  DF  t Value Pr
> |t|    Alpha   Lower   Upper
Randomization_Arm   12 weekly BTA   -4.5441 1.3163  222 -3.45   0.0007
 0.05    -7.1382 -1.9501
Randomization_Arm   4 weekly BTA    -6.4224 1.3143  222 -4.89   <.0001
 0.05    -9.0126 -3.8322

Differences of Least Squares Means
Effect  Randomization_Arm   _Randomization_Arm  Estimate    Standard
Error  DF  t Value Pr > |t|    Alpha   Lower   Upper
Randomization_Arm   12 weekly BTA   4 weekly BTA    1.8783  1.4774
222 1.27    0.2049  0.05    -1.0332 4.7898

The output from the R code is below:

Linear mixed-effects model fit by REML
  Data: rmanova.data
       AIC     BIC    logLik
  6526.315 6586.65 -3250.157

Random effects:
 Formula: ~1 | Study_ID
        (Intercept) Residual
StdDev:    16.51816 12.95417

Correlation Structure: General
 Formula: ~1 | Study_ID
 Parameter estimate(s):
 Correlation:
  1      2      3     2  0.222              3 -0.159  0.225       4
-0.421 -0.042  0.083
Fixed effects:  chgpf ~ Randomization_Arm + Cancer_Type + site + wk
                                  Value Std.Error  DF    t-value
p-value(Intercept)                    5.739240 2.8987216 541
1.9799209  0.0482
Randomization_Arm4 weekly BTA -1.174704 2.3915873 225 -0.4911817  0.6238
Cancer_TypeProsta             -4.459715 2.4711025 225 -1.8047469  0.0725
site                          -1.917902 0.9709655 225 -1.9752530  0.0495
wk                            -1.570707 0.5115174 541 -3.0706809  0.0022
 Correlation:
                              (Intr) R_A4wB Cnc_TP site
Randomization_Arm4 weekly BTA -0.440
Cancer_TypeProsta             -0.314  0.043
site                          -0.598  0.003 -0.064
wk                            -0.421 -0.004  0.032  0.003

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-4.99530346 -0.36507852  0.08308708  0.45937864  3.12730244

Number of Observations: 771
Number of Groups: 229








--
Nicholas Mitsakakis, MSc, PhD, P.Stat.

Senior Biostatistician and Associate Scientist
Children's Hospital of Eastern Ontario Research Institute
Adjunct Lecturer, Dalla Lana School of Public Health, University of Toronto

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