Modelling Parallel bears [🇷 for BE/BA]

posted by yjlee168 Homepage – Kaohsiung, Taiwan, 2010-04-25 21:34 (5480 d 19:31 ago) – Posting: # 5214
Views: 58,108

Dear d_labes,

When I revised bear today, I found that the following two models got the same results (with built-in dataset in bear v2.4.2) for CIs.

lmeCmax_ss<-lme(Cmax_ss ~ drug, random=~1|subj, data=Data,

method="REML" )

Dependent Variable: lnCmax                                             

Linear mixed-effects model fit by REML
 Data: TotalData
        AIC       BIC   logLik
  -14.15488 -9.279372 11.07744

Random effects:
 Formula: ~1 | subj
        (Intercept)   Residual
StdDev:   0.1310857 0.04915712

Fixed effects: lnCmax ~ drug
               Value  Std.Error DF   t-value p-value
(Intercept) 7.352034 0.03882889 25 189.34445  0.0000
drug2       0.008486 0.05392284 25   0.15738  0.8762
 Correlation:
      (Intr)
drug2 -0.72

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-0.83569001 -0.14504896  0.05106144  0.21037957  0.69279954

Number of Observations: 27
Number of Groups: 27

and

lmCmax_ss<- lm(Cmax_ss ~ drug , data=TotalData)

Call:
lm(formula = lnCmax ~ drug, data = TotalData)

Residuals:
     Min       1Q   Median       3Q      Max
-0.33321 -0.05783  0.02036  0.08388  0.27623

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept) 7.352034   0.038829 189.344   <2e-16 ***
drug2       0.008486   0.053923   0.157    0.876   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.14 on 25 degrees of freedom
Multiple R-squared: 0.0009897,  Adjusted R-squared: -0.03897
F-statistic: 0.02477 on 1 and 25 DF,  p-value: 0.8762

Just a quick response. I am studying Helmut's t tests for further revision.

All the best,
-- Yung-jin Lee
bear v2.9.2:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here

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