# AP Statistics Curriculum 2007 NonParam 2MedianPair

(Difference between revisions)
 Revision as of 19:38, 6 August 2010 (view source)IvoDinov (Talk | contribs) (→Practice Problems: entered a new brain parsing example data)← Older edit Revision as of 20:04, 6 August 2010 (view source)IvoDinov (Talk | contribs) (→Comparison and Validation of Automated Brain Volume Segmentation Methods: fixed figure 6)Newer edit → Line 211: Line 211: === Comparison and Validation of Automated Brain Volume Segmentation Methods=== === Comparison and Validation of Automated Brain Volume Segmentation Methods=== - [[Image:EBook_Logo.jpg|150px|thumbnail|right| [[EBook]] ]] + [[Image:SOCR_EBook_NonParam_SignTest_080610_Fig6.png|250px|thumbnail|right| Brain Parsing]] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04359071 Automated brain volume segmentation] is an important stem in many modern computational brain mapping studies. Suppose we have two separate and competing versions of automated brain parsing (segmentation) algorithms that automatically tessellate (partition) the brain into [http://www.loni.ucla.edu/twiki/bin/view/CCB/CCBPrincipalsMeetings_IndividResearcherProj_ZhuowenTu 57 separate regions of interest (ROI's)]. An important question then is how consistent are these 2 different techniques, across the 57 ROIs. We can use the ROI volume as a measure of the resulting automated brain parcellation and compare the paired differences between the 2 methods across all ROIs. The image shows an example of a brain parcellated into these 57 regions and the table below contains the volumes of the 57 ROIs for the 2 different brain tessellation techniques. Use appropriate [http://socr.ucla.edu/htmls/SOCR_Analyses.html SOCR Analyses] and relevant [http://socr.ucla.edu/htmls/SOCR_Charts.html SOCR Charts] to argue whether or not the 2 different methods are consistent and agree on their ROI labels. [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04359071 Automated brain volume segmentation] is an important stem in many modern computational brain mapping studies. Suppose we have two separate and competing versions of automated brain parsing (segmentation) algorithms that automatically tessellate (partition) the brain into [http://www.loni.ucla.edu/twiki/bin/view/CCB/CCBPrincipalsMeetings_IndividResearcherProj_ZhuowenTu 57 separate regions of interest (ROI's)]. An important question then is how consistent are these 2 different techniques, across the 57 ROIs. We can use the ROI volume as a measure of the resulting automated brain parcellation and compare the paired differences between the 2 methods across all ROIs. The image shows an example of a brain parcellated into these 57 regions and the table below contains the volumes of the 57 ROIs for the 2 different brain tessellation techniques. Use appropriate [http://socr.ucla.edu/htmls/SOCR_Analyses.html SOCR Analyses] and relevant [http://socr.ucla.edu/htmls/SOCR_Charts.html SOCR Charts] to argue whether or not the 2 different methods are consistent and agree on their ROI labels.

## General Advance-Placement (AP) Statistics Curriculum - Differences of Medians of Two Paired Samples

Distribution-free (or non-parametric) statistical methods provide alternative to the (standard) parametric tests that we saw earlier, and are applicable when the distribution of the data is unknown.

## Motivational Clinical Example

Whitley and Ball reported on the relative risk of mortality from 16 studies of septic patients. The outcome measure of interest was whether the patients developed complications of acute renal failure. The relative risk calculated in each study compared the risk of dying between patients with and without renal failure. A relative risk of 1.0 means no effect, and relative risk $\not= 1$ suggests beneficial or detrimental effect of developing acute renal failure in sepsis. The main goal of the study was to determine whether developing acute renal failure as a complication of sepsis impacts patient mortality from the cumulative evidence in these 16 studies. The data of this study is included below.

 Study Relative Risk Sign (Relative Risk - 1) 1 0.75 - 2 2.03 + 3 2.29 + 4 2.11 + 5 0.80 - 6 1.50 + 7 0.79 - 8 1.01 + 9 1.23 + 10 1.48 + 11 2.45 + 12 1.02 + 13 1.03 + 14 1.30 + 15 1.54 + 16 1.27 +

We see the clear analogy of this study design to the Paired or One-Sample studies we saw before. However, if we were to plot these data (Relative Risk), we can see that their distribution is hardly symmetric, unimodal and bell-shaped (i.e., not Normal). Therefore, we cannot use the Paired T-test to test a Null-Hypothesis that the mean Relative Risk is 1 using this parametric test.

## The Sign-Test

The Sign Test is a non-parametric alternative to the One-Sample and Paired T-Test. The Sign Test has no requirements for the data to be Normally distributed. It assigns a positive (+) or negative (-) sign to each observation according to whether it is greater or less than some hypothesized value. It measures the difference between the $\pm$ signs and how distinct the difference is from our expectations to observe by chance alone. For example, if there were no effects of developing acute renal failure on the outcome from sepsis, about half of the 16 studies above would be expected to have a relative risk less than 1.0 (a "-" sign) and the remaining 8 would be expected to have a relative risk greater than 1.0 (a "+" sign). In the actual data, 3 studies had "-" signs and the remaining 13 studies had "+" signs. Intuitively, this difference of 10 appears large to be simply due to random variation. If so, the effect of developing acute renal failure would be significant on the outcome from sepsis.

### Calculations

Suppose N+ is the number of "+" signs and we fix a significance level of α = 0.05. And consider the following two hypotheses:

Ho:N + = 8 (equivalent to N = 8): The effect of developing acute renal failure is not significant on the outcome from sepsis.
$H_1: N_+ \not=8$: The effect of developing acute renal failure is significant on the outcome from sepsis.

Define the following test-statistics

Bs = max(N + ,N), where N + and N are the number of positive and negative signs, respectively.

Then the distribution of $B_s \sim Binomial(n=16, p=8/16=0.5)$.

For our data, Bs = max(N + ,N) = max13,3 = 13 and the probability that such binomial variable exceeds 13 is P(Bin(16,0.5,13)) = 0.010635. Therefore, we can reject the null hypothesis Ho and regard as the significant effect of developing acute renal failure on the outcome from sepsis.

### The Sign Test Using SOCR Analyses

It is much quicker to use SOCR Analyses to compute the statistical significance of the sign test. This SOCR Sign Test Activity may also be helpful in understanding how to use the sign test method in SOCR.

### Example

A set of 12 identical twins is given psychological tests to determine whether the first born of the set tends to be more aggressive than the second born. Each twin is scored according to aggressiveness; a higher score indicates greater aggressiveness. Because of the natural pairing in a set of twins these data can be considered paired.

 Twin-Index 1st Born 2nd Born Sign 1 86 88 - 2 71 77 - 3 77 76 + 4 68 64 + 5 91 96 - 6 72 72 0 (Drop) 7 77 65 + 8 91 90 + 9 70 65 + 10 71 80 - 11 88 81 + 12 87 72 +

We first plot the data using the SOCR Line Chart. Visually there does not seem to be a strong effect of the order of birth on baby's aggression.

Next, we can use the SOCR Sign Test Analysis to quantitatively evaluate the evidence to reject the null hypothesis that there is no birth-order effect on baby's aggressiveness.

Clearly the p-value reported is 0.274, and our data can not reject the null hypothesis.

## The Wilcoxon Signed Rank Test

Like the Sign Test and the T-Test, the Wilcoxon Signed Rank Test involves comparisons of differences between measurements. It requires that the data are measured at an interval level of measurement, but does not require assumptions about the form of the distribution of the measurements. It should therefore be used whenever the distributional assumptions of the T-Test are not satisfied.

### Example

Whitley and Ball reported data on the central venous oxygen saturation (SvO2 (%)) from 10 consecutive patients at 2 time points; at admission and 6 hours after admission to the intensive care unit (ICU). The null hypothesis is that there is no effect of 6 hours of ICU treatment on SvO2. Under the null hypothesis, the mean of the differences between SvO2 at admission and that at 6 hours after admission should be zero.

 Patient On Admission At 6 Hours Difference Rank 2 59.1 56.7 -2.4 1 7 58.2 60.7 2.5 2 9 56.0 59.5 3.5 3 10 65.3 59.8 -5.5 4 3 56.1 61.9 5.8 5 5 60.6 67.7 7.1 6 6 37.8 50.0 12.2 7 1 39.7 52.9 13.2 8 4 57.7 71.4 13.7 9 8 33.6 51.3 17.7 10

Clearly, we can reject the null-hypothesis at α = 0.05, as the one- and two-sided alternative hypotheses p-values for the Wilcoxon Signed Rank Test reported by the SOCR Analysis are respectively

One-Sided p-value = 0.011
Two-Sided p-value = 0.022

### Result interpretation

The Results tab of the SOCR Wilcoxon signed-rank test applet contains the following output (this is for the data data above, and will change for other datasets, accordingly):

Variable 2 = 6_Hrs_Later
Results of Two Paired Sample Wilcoxon Signed Rank Test:
Wilcoxon Signed-Rank Statistic = 5.000
E(W+), Wilcoxon Signed-Rank Score = 27.500
Var(W+), Variance of Score = 96.250
Wilcoxon Signed-Rank Z-Score = -2.293
One-Sided P-Value = .011
Two-Sided P-Value = .022

Where:

DataCase[] combo = QSortAlgorithm.rankList(diffListAbs);
double wStat = 0;
int lenList = combo.length;
for (int i = 0; i < lenList; i++)
if ( combo[i].getSign() ) wStat = wStat + combo[i].getRank();
• E(W+), Wilcoxon Signed-Rank Score = expectation of the Wilcoxon Signed-Rank Statistic, see page 414 Rice book (Mathematical Statistics and Data Analysis, 2dn Edition, by John Rice, Duxbury 1995.)
• Var(W+), Variance of Score = variance of the Wilcoxon Signed-Rank Statistic, see page 414 Rice book (Mathematical Statistics and Data Analysis, 2dn Edition, by John Rice, Duxbury 1995.)
• Wilcoxon Signed-Rank Z-Score = ${WStat - E(W+) \over \sqrt{Var(W+)}}$
• One-Sided P-Value = the one-sided (uni-directional) probability value expressing the strength of the evidence in the data to reject the null hypothesis that the two populations have the same medians (based on Gaussian, standard Normal, distribution).
• Two-Sided P-Value = the double-sided (non-directional) probability value expressing the strength of the evidence in the data to reject the null hypothesis that the two populations have the same medians (based on Gaussian, standard Normal, distribution).

## Practice Problems

### Treatment in Rats

Suppose 10 randomly selected rats were chosen to see if they could be trained to escape a maze. The rats were released and timed (seconds) before and after 2 weeks of training (N means the rat did not complete the maze-test). Do the data provide evidence to suggest that the escape time of rats is different after 2 weeks of training? Test using α = 0.05.

 Rat Before After Sign 1 100 50 + 2 38 12 + 3 N 45 + 4 122 62 + 5 95 90 + 6 116 100 + 7 56 75 - 8 135 52 + 9 104 44 + 10 N 50 +

### Comparison and Validation of Automated Brain Volume Segmentation Methods

Brain Parsing

Automated brain volume segmentation is an important stem in many modern computational brain mapping studies. Suppose we have two separate and competing versions of automated brain parsing (segmentation) algorithms that automatically tessellate (partition) the brain into 57 separate regions of interest (ROI's). An important question then is how consistent are these 2 different techniques, across the 57 ROIs. We can use the ROI volume as a measure of the resulting automated brain parcellation and compare the paired differences between the 2 methods across all ROIs. The image shows an example of a brain parcellated into these 57 regions and the table below contains the volumes of the 57 ROIs for the 2 different brain tessellation techniques. Use appropriate SOCR Analyses and relevant SOCR Charts to argue whether or not the 2 different methods are consistent and agree on their ROI labels.

Index Volume_Intensity ROI_Name Method1 Method2
1 0 Background 9236455 9241667
2 21 L_superior_frontal_gyrus 78874 78693
3 22 R_superior_frontal_gyrus 69575 74391
4 23 L_middle_frontal_gyrus 67336 68872
5 24 R_middle_frontal_gyrus 68344 67024
6 25 L_inferior_frontal_gyrus 31912 21479
7 26 R_inferior_frontal_gyrus 26264 29035
8 27 L_precentral_gyrus 28942 33584
9 28 R_precentral_gyrus 35192 30537
10 29 L_middle_orbitofrontal_gyrus 10141 11608
11 30 R_middle_orbitofrontal_gyrus 9142 11850
12 31 L_lateral_orbitofrontal_gyrus 7164 5382
13 32 R_lateral_orbitofrontal_gyrus 5964 4947
14 33 L_gyrus_rectus 3840 1995
15 34 R_gyrus_rectus 2672 2994
16 41 L_postcentral_gyrus 24586 27672
17 42 R_postcentral_gyrus 21736 28159
18 43 L_superior_parietal_gyrus 25791 27500
19 44 R_superior_parietal_gyrus 28850 32674
20 45 L_supramarginal_gyrus 16445 22373
21 46 R_supramarginal_gyrus 11893 11018
22 47 L_angular_gyrus 20740 22245
23 48 R_angular_gyrus 20247 17793
24 49 L_precuneus 14491 12983
25 50 R_precuneus 15589 16323
26 61 L_superior_occipital_gyrus 6842 6106
27 62 R_superior_occipital_gyrus 5673 6539
28 63 L_middle_occipital_gyrus 15011 19085
29 64 R_middle_occipital_gyrus 19063 25747
30 65 L_inferior_occipital_gyrus 10411 8675
31 66 R_inferior_occipital_gyrus 12142 12277
32 67 L_cuneus 6935 9700
33 68 R_cuneus 7491 11765
34 81 L_superior_temporal_gyrus 29962 34934
35 82 R_superior_temporal_gyrus 30630 28788
36 83 L_middle_temporal_gyrus 27558 19633
37 84 R_middle_temporal_gyrus 26314 25301
38 85 L_inferior_temporal_gyrus 24817 24885
39 86 R_inferior_temporal_gyrus 25088 20661
40 87 L_parahippocampal_gyrus 6761 6977
41 88 R_parahippocampal_gyrus 6529 7964
42 89 L_lingual_gyrus 16752 14748
43 90 R_lingual_gyrus 20914 18500
44 91 L_fusiform_gyrus 16565 15020
45 92 R_fusiform_gyrus 14409 17311
46 101 L_insular_cortex 10779 9814
47 102 R_insular_cortex 8222 5599
48 121 L_cingulate_gyrus 14662 12490
49 122 R_cingulate_gyrus 16595 14489
50 161 L_caudate 1906 1608
51 162 R_caudate 2353 1997
52 163 L_putamen 3015 2622
53 164 R_putamen 2177 3758
54 165 L_hippocampus 3791 4454
55 166 R_hippocampus 3596 4673
56 181 cerebellum 174045 158617
57 182 brainstem 32567 28225