Supplementary webappendix€¦ · · 2012-12-19when compared directly to a simple linear model....
Transcript of Supplementary webappendix€¦ · · 2012-12-19when compared directly to a simple linear model....
Supplementary webappendixThis webappendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors.
Supplement to: Fleisher AS, Chen K, Quiroz YT, et al. Florbetapir PET analysis of amyloid-β deposition in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional study. Lancet Neurol 2012; published online Nov 6. http://dx.doi.org/10.1016/S1474-4422(12)70227-2
Web Extra Materials: Table S1 Table S1 Regression Model Comparison
Probability of correctness Probability of correctness
Sigmoidal vs 3 Segment piecewise
Difference in AIC Sigmoidal vs Simple Linear Difference in AIC
Mean Cortical
90.4%
9.6% -4.49 98.4% 1.6% -8.27
Precuneus 87.8%
12.2% -3.94 99.9% 0.06% -14.73
Basal Ganglia 77.6%
22.3% -2.49 99.9% 0.1% -13.36
Frontal 91.7%
8.3% -4.80 99.6% 0.4% -11.00
Anterior Cingulate
92.0%
8.0% -4.90 99.5% 0.5% -10.70
Parietal 57.9%
42.1% -0.63
96.3% 3.7% -6.54
Temporal 93.1%
6.9% -5.21 86.3% 13.6% -3.69
Posterior Cingulate
94.2%
5.8% -5.58 95.5% 4.4% -6.14
Table S1: Goodness of fit comparison for three regression models: sigmoidal, three segment piecewise linear, and simple linear. The sigmoidal regression model had higher probabilities of correctly fitting the relationship between mean cortical and regional SUVRs in relation to age than a 3 segment piecewise linear modal, as well as better probabilities when compared directly to a simple linear model. AIC = Akaike's information criterion.
Figure S1: Comparison of regression models for age‐related accumulation of SUVRs compared to age
Figure S1: Three model comparison of mean cortical florbetapir F18 levels compared to age: A) simple linear model, B) 3 segment piecewise linear model, C) sigmoidal model. The goodness‐of‐fit of each individual model was assessed using R‐square testing. The extra sum of squares f‐test was used to compare the goodness‐of‐fit between the simple linear and piecewise linear models. Linear regression models were compared to the sigmoidal model using Akaike's information criterion (AIC) from information theory since the two models are not nested. The relationship between age and mean cortical SUVRs was best represented by a sigmoidal shaped curve. While the fit of a simple linear regression model was satisfactory (R2=0.74 and p=0.0002), a three segment piecewise linear model had an improved fit compared to the simple linear model (p=0.01). This piecewise linear model demonstrated an initial flat slope, followed by an upward inflexion in mean cortical SUVR at age 29 and later plateauing at age 38. The probability of the sigmoidal model properly representing the data was 98.4% versus a 1.6% probability that the simple linear regression model was correct. Similarly, the sigmoidal model supported a three stage progression of cortical fibrillar amyloid associated with age and disease state, but showed a superior goodness‐of‐fit to the data compared to the three segment piecewise linear model (90.4% compared to 9.6%).