Bioequivalence and Bioavailability Forum

First hints for Phoenix [General Statistics]

posted by Helmut – Vienna, Austria, 2014-02-20 21:11 (4080 d 20:27 ago) – Posting: # 12465
Views: 25,800

Hi Lucas,

❝ This is how it would look like according to them: […]

Interesting! I didn’t count, but ~200 subjects is not common in BE studies. It will not look that nice in the real world. The x-axis of the first plot is strange (negative means – I beg your pardon?). The distribution doesn’t look normal but skewed to the left* and the Q-Q plot looks extremely ugly. That’s actually a great example which EDA would suggest to use another transformation (e.g., ℯ^x or xⁿ, where n>1).
However, let’s see what you can get in PHX (I numbered the panels in your plot):

Plotting > XY-Plot (though for the abscissa I would either use the subject’s ID or the predicted model values).
Plotting > Histogram ☑ Show relative Frequency. In the current version (6.3) it is not possible to overlay a normal distribution, maybe in the next release?
Plotting > QQ Plot.
Upper part = trivial. Lower part = strange. In PHX the line in the Q-Q plot is fitted automatically – I don’t think it is possible to extract the parameters and AIC, BIC.
BTW, these values are only useful in comparing different models. I cannot imagine what might its purpose here. AICC is the AIC corrected for small sample sizes. It proves that the statistician is a “push-the-button-type” who does not know what he is doing. Of course you could fit the standard quantiles vs. stud. resid’s by PHX’ linear model.

If the sponsor insists on a similar layout, you have to leave PHX because different types of plots arranged in an array (aka trelis/lattice plots) are not supported. Either export relevant data from PHX and continue the analysis in R or generate the plots in PHX and paste them into a 4-cell table in your preferred text-processor.

AUC and C_max have been shown to follow a log-normal distribution – which is skewed to the right. However, in order to apply parametric methods the residuals (which are estimates of the residual error) must be normal – not the original values. The log-transformation is more a convention than justified. I have never seen values skewed to the left. Which kind of stuff is this?

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Complete thread:

Qualitative analysis of the ANOVA residuals Lucas 2014-02-19 14:27 [General Statistics]
- Qualitative analysis of the ANOVA residuals Helmut 2014-02-19 15:22
  - Qualitative analysis of the ANOVA residuals Lucas 2014-02-19 15:58
    - Qualitative analysis of the ANOVA residuals Helmut 2014-02-19 16:25
      - Qualitative analysis of the ANOVA residuals Lucas 2014-02-19 17:20
      - Qualitative analysis of the ANOVA residuals Lucas 2014-02-20 19:07
        
        First hints for PhoenixHelmut 2014-02-20 20:11
        Phoenix: residuals Helmut 2014-02-21 19:25
        
        Phoenix: residuals BRN 2014-03-04 15:15
        
        Phoenix: residuals Helmut 2014-03-04 16:52
        
        Phoenix: residuals Rosy 2018-10-15 07:03
        
        Software validation Helmut 2018-10-15 11:43
        
        Software validation Rosy 2018-10-18 00:38
        
        Avoid raw residuals Helmut 2018-10-18 17:14
        
        Avoid raw residuals Rosy 2018-10-18 18:05
        
        Phoenix → R Helmut 2014-02-23 02:20
        
        Phoenix → R Lucas 2014-02-25 19:28
- Qualitative analysis of the ANOVA residuals ElMaestro 2014-02-19 15:32
  - Qualitative analysis of the ANOVA residuals Helmut 2014-02-19 16:06
  - Qualitative analysis of the ANOVA residuals Lucas 2014-02-19 16:13
    - Qualitative analysis of the ANOVA residuals ElMaestro 2014-02-19 20:40

First hints for Phoenix [General Sta­tis­tics]

Complete thread:

First hints for Phoenix [General Statistics]