Bioequivalence and Bioavailability Forum • R code for analyzing classical 2X2 crossover designed bioequivalence data

R code for analyzing classical 2X2 crossover designed bioequivalence data [🇷 for BE/BA]

posted by lizhao – US, 2016-01-22 01:13 (3348 d 05:20 ago) – Posting: # 15841
Views: 27,690

Dear All,

I have a dataset that comes from a classical 2X2 crossover designed bioequivalence study.
I wonder if somebody could share with me the R code that can be used to analyze this dataset???

Should I use lm() or lme() in R (considering subject effects as random?)

Thanks a lot!!!

Complete thread:

R code for analyzing classical 2X2 crossover designed bioequivalence datalizhao 2016-01-22 00:13 [🇷 for BE/BA]
- R code for analyzing classical 2X2 crossover designed bioequivalence data ElMaestro 2016-01-22 00:59
  - R code for analyzing classical 2X2 crossover designed bioequivalence data lizhao 2016-01-22 01:26
    - On random effects and bogus statements ElMaestro 2016-01-22 12:13
      - FDA 'own' SAS code for 2x2 d_labes 2016-01-27 09:57
        
        FDA 'own' SAS code for 2x2 nobody 2016-01-27 10:09
        
        OT: PM to ElMaestro Helmut 2016-01-27 13:39
        On coffee and such... ElMaestro 2016-01-27 13:47
      - Geometric means ± SD Helmut 2016-01-27 13:51
        
        Geometric means ± SD nobody 2016-01-27 15:20
        
        Money makes the world go ’round Helmut 2016-01-27 16:11
        
        Money makes the world go ’round nobody 2016-01-27 16:31
        
        Fixed or random effect kumarnaidu 2016-11-04 14:05
        
        PROC GLM fixes Subject mittyri 2016-11-04 23:00
        
        PROC GLM fixes Subject kumarnaidu 2016-11-05 05:22
        
        PROC GLM fixes Subject mittyri 2016-11-05 10:13
        
        PROC GLM fixes Subject kumarnaidu 2016-11-07 06:24
        
        PROC GLM fixes Subject mittyri 2016-11-07 11:57
        
        PROC GLM fixes Subject kumarnaidu 2016-11-07 12:42
        
        Geometric means ± SD Ben 2016-11-08 21:20
        
        Misunderstanding Helmut 2016-11-09 15:29
        
        Misunderstanding Ben 2016-11-14 19:40
        
        RTFM Helmut 2016-11-14 21:50