behaviour By Sarah Janssen NCS 2014 Brugge 1 External supervisor Dr Tom Jacobs Janssen Pharmaceuticals JampJ Internal supervisor Dr Herbert Thijs Uhasselt Introduction A new ID: 569079
Download Presentation The PPT/PDF document "Statistical aspects for the quantificati..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Statistical aspects for the quantification of learning behaviour
By Sarah JanssenNCS 2014, Brugge
1
External supervisor: Dr. Tom Jacobs, Janssen Pharmaceuticals (J&J)
Internal supervisor: Dr. Herbert
Thijs
,
UhasseltSlide2
Introduction
A new animal behaviour
model is setup to asses cognitive functioning in animals:
Animals are injected with PCP (also known as “Angel Dust”)
PCP has a degrading effect on learning
behaviourA good understanding of the effect of PCP on cognitive functioning is importantOptimizing the data analysis That allows to quantify learning behaviourThat allows answering the research question in an unambiguous and efficient way
2Slide3
The objective
To study and quantify the dose effect of PCP on learning
behaviorTo put it explicitly:How does
PCP
affects learning
behavior?Which characteristics of learning behavior are sensitive to the dose effect?How to quantify the dose effect on these characteristics?Which dose levels show a significant effect on learning behaviour?3Slide4
Experimental setup
Male wistar
rats were trained to perform an action: choosing the correct image between two imagesThrough reward mechanismBy the use of an operant conditioning chamber
One training session ends after 48 trials or after 30 minutes maximally
Variable of interest: the proportion of correctly executed trials within one training session
Figure Retrieved from www.campden-inst.com on 12/08/2012, URL: http://www.campden-inst.com/product_detail.asp?ItemID=1975&cat=2
4Slide5
Experimental setup
Data available from two dose-response studies with PCP in identical conditions:
96 animals 5 dose levels: 0mg, 0.25mg, 0.5mg, 0.75mg, 1mgDaily injection with PCP before every sessionSessions were performed daily during a period of 14 days
Dose level
PCP
: 0mg
0.25mg
0.5mg
0.75mg
1mg
Total # of animals
Total # of animals
24
12
24
12
24
96
5Slide6
Exploratory data analysis:
individual profiles per dose level
6Slide7
Variability between and within animals
Profiles start around 0.5Increase up to a level 0.9Increase in a non-linear wayLess steep increase of the profiles at higher dose levels
7
Exploratory data analysis:
average profiles per dose levelSlide8
Part 1: Traditional Multivariate Anova
model8Slide9
The model
Covariates: dose, time and dose*timeResidual errors are assumed to follow a multivariate normal distribution
Pairwise comparisons of the 4 dose levels to the vehicle dose at every time pointWithout and with adjustment for multiple testing via Bonferroni
correction
9Slide10
Results
10Slide11
Results
11Slide12
Conclusion
Flexible modelEasy to understand and apply, also for non-statisticians
Inefficient way to analyze the data:Perform many test (59 comparisons)Analyses becomes conservative when adjusting for multiple testing
Does not answer the research question in a direct, unambiguous way
12Slide13
Part 2: Non-linear mixed effects model
13Slide14
The model
The response variable (proportion)
is assumed to follow a beta distributionThe average proportions
(
μij) are modeled as a Weibull learning curve (Gallistel et al, 2004):14Slide15
The model
15
The Weibull distribution is characterized by a
scale (
L
) and shape (S) parameterAn intercept (I) and an asymptotic level (A) is added:To get a more meaningful interpretation for the scale parameter,
L
is
reparameterized
as
T70
:
T70
: time until proportion 0.7 was reachedSlide16
This way, learning
behavior is characterized by 4 parameters: Intercept (I
)Asymptotic level (A)
Time to reach proportion 0.7 (
T70
)Abruptness (S)16
The modelSlide17
The model
Dose effect is included in the model by
allowing the parameters to change in function of dose levelTo take the
heterogeneity between animals
into account,
random effects were included17Slide18
Results
18
Parameter
Estimate
95% CI
I_int
0.52
(0.50, 0.54)
S_int
1.66
(1.42, 1.94)
A_int
0.93
(0.92, 0.94)
A_slope
0.81
(-0.13, 1.74)
T70_int3.9
(3.4, 4.6)
T70_slope
1.11
(0.86, 1.36)Slide19
Results
19
Parameter
Estimate
98.75% CI
p-value
T70
0.25
/ T70
0
1.14
(0.78, 1.67)
0.3883
T70
0.50
/ T70
0
1.50
(1.10, 2.06)
0.0014
T70
0.75
/ T70
0
1.61
(1.09, 2.36)
0.0022
T70
1
/ T70
0
3.21
(2.29, 4.49)
<0.0001Slide20
Conclusion
Weibull funtion was used to model the learning curves
Parameters have a biological interpretationDirect, unambiguous answer to the research question:
How does PCP affects learning
behaviour
? via T70How strong is the dose effect 3 fold increase of T70 with a unit increase of dose Which does level show a statistical significant effect all, except dose level 0.25Efficient way to analyze the dataRather complex analysis
20Slide21
Thank you for your attention!
21
Thanks to:
Dr. Tom Jacobs, Janssen Pharmaceuticals (J&J)
Dr. Herbert
Thijs, Uhasselt