Factors influence in Anova [General Statistics]
❝ […] ANOVA assesses influence of different factors (subjects, treatment, period, sequence) on variation of PK-parameters.
OK; you are talking about a crossover.
❝ In my practice only subjects have always had significant impact on variation.
Anything else would be a big surprise. Subjects are different indeed.
❝ What can be reasons for signficant influence of treatment, period and sequence?
Treatment
Happens if the the CV is smaller + the T/R is more far away from 100% than assumed in study planning; the confidence interval does not include 100%. Reasons: The formulation and/or the study planning (overpowered?)
- If the CI is still within the acceptance range, the difference is statistically significant, but not clinically relevant. No worries. You only burned money.
- If the CI overlaps the AR, think twice about repeating the study in a larger sample size. IMHO, reformulation is the way to go.
- If the CI lies completely outside the AR, bioinequivalence is proven. End of story.
Doesn’t matter at all. Since both R and T are affected to the same degree, true differences will mean out. Try it: Take one of your datasets and multiply all results of the second period by 1,000. The PE and CI of T/R will be exactly the same like in the original dataset. Of course the period effect comes out highly significant. Reasons: Generally unknown (subjects more relaxed in the 2nd period, lunar phase, thunderstorm, broken AC, whatsoever).
Sequence
Better called unequal carryover. It essentially means that subject’s PK response might be different if they receive formulations in the order RT as compared to the order TR. Tricky. If there would be a true sequence effect we cannot get an unbiased estimate of the treatment effect (technically these effects are “confounded”). That’s bad.
- In the past (based on Grizzle 1965) the common approach was to discard all data of the second period and evaluate the data of the first period as a parallel design. Catastrophic loss in power.
- Freeman showed in 1989 that this approach is statistically flawed. Stephen Senn devoted this issue a major part of one of his books. There is no statistical method which can correct for unequal carryover. It can be only avoided by design (i.e., a sufficiently long washout).
- In a large meta-study significant sequence effect showed up at approx. the level of the test (i.e., if you test with an α of 0.1 in 1/10 studies).
A test for carry-over is not considered relevant and no decisions regarding the analysis (e.g. analysis of the first period only) should be made on the basis of such a test. The potential for carry-over can be directly addressed by examination of the pre-treatment plasma concentrations in period 2 (and beyond if applicable).
❝ Is it bad or not?
Define bad.
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Science Quotes
Complete thread:
- Factors influence in Anova BE-proff 2015-11-05 18:31 [General Statistics]
- Factors influence in AnovaHelmut 2015-11-05 19:33
- Factors influence in Anova felipeberlinski 2015-11-05 19:56
- Factors influence in Anova Helmut 2015-11-05 20:27
- What else? ElMaestro 2015-11-05 21:28
- Factors influence in Anova BE-proff 2015-11-05 21:27
- overpowering Helmut 2015-11-06 04:03
- Factors influence in Anova felipeberlinski 2015-11-05 19:56
- Factors influence in AnovaHelmut 2015-11-05 19:33