[R] lme vs. SAS proc mixed. Point estimates and SEs are the same, DFs are different

[Peter Dalgaard]

Peter Dalgaard wrote:
> John Sorkin wrote:
>   
>> R 2.3
>> Windows XP
>>
>> I am trying to understand lme. My aim is to run a random effects regression in which the intercept and jweek are random effects. I am comparing output from SAS PROC MIXED with output from R. The point estimates and the SEs are the same, however the DFs and the p values are different. I am clearly doing something wrong in my R code. I would appreciate any suggestions of how I can change the R code to get the same DFs as are provided by SAS.
>>   
>>     
> This has been hashed over a number of times before. In short:
>
> 1) You're not necessarily doing anything wrong
> 2) SAS PROC MIXED is not necessarily doing it right
> 3) lme() is _definitely_ not doing it right in some cases
> 4) both work reasonably in large sample cases (but beware that this is 
> not equivalent to having many observation points)
>
> SAS has an implementation of the method by Kenward and Rogers, which 
> could be the most reliable general DF-calculation method around (I don't 
> trust their Satterthwaite option, though). Getting this or equivalent 
> into lme() has been on the wish list for a while, but it is not a 
> trivial thing to do.
>   

Forgot to say: All DF-based corrections are wrong if you have 
non-normally distributed data (they depend on the 3rd and 4th moment of 
the error distribution(s)), although they can be useful as warning signs 
even in those cases. I also forgot to point to the simulate.lme() 
function which can simulate the LR statistics directly.
>   
>> SAS code:
>> proc mixed data=lipids2;
>>   model ldl=jweek/solution;
>>   random int jweek/type=un subject=patient;
>>   where lastvisit ge 4;
>> run;
>>
>> SAS output:
>>                    Solution for Fixed Effects
>>
>>                          Standard
>> Effect       Estimate       Error      DF    t Value    Pr > |t|
>>
>> Intercept      113.48      7.4539      25      15.22      <.0001
>> jweek         -1.7164      0.5153      24      -3.33      0.0028
>>
>>         Type 3 Tests of Fixed Effects
>>
>>               Num     Den
>> Effect         DF      DF    F Value    Pr > F
>> jweek           1      24      11.09    0.0028
>>
>>
>> R code:
>> LesNew3 <- groupedData(LDL~jweek | Patient, data=as.data.frame(LesData3), FUN=mean)
>> fit3    <- lme(LDL~jweek, data=LesNew3[LesNew3[,"lastvisit"]>=4,], random=~1+jweek)
>> summary(fit3) 
>>
>> R output:
>> Random effects:
>>  Formula: ~1 + jweek | Patient
>>  Structure: General positive-definite, Log-Cholesky parametrization
>>  
>>
>> Fixed effects: LDL ~ jweek 
>>                 Value Std.Error DF   t-value p-value
>> (Intercept) 113.47957  7.453921 65 15.224144  0.0000
>> jweek        -1.71643  0.515361 65 -3.330535  0.0014
>>
>> John Sorkin M.D., Ph.D.
>> Chief, Biostatistics and Informatics
>> University of Maryland School of Medicine Division of Gerontology
>> Baltimore VA Medical Center
>> 10 North Greene Street
>> GRECC (BT/18/GR)
>> Baltimore, MD 21201-1524
>> (Phone) 410-605-7119
>> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>>
>> Confidentiality Statement:
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>>
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>>     
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>