Osteoporosis AS Final March 2020
Packages
Install papaja for anova report
Dataset
Data cleaning and new variables
Clean the names
Remove the names column
check
normal osteopenia osteoporosis
56 46 25 Results
Table 1
Demographics
| normal (n=56) |
osteopenia (n=46) |
osteoporosis (n=25) |
Overall (n=127) |
|
|---|---|---|---|---|
| age | ||||
| Mean (SD) | 69.9 (9.10) | 70.0 (8.85) | 72.4 (8.45) | 70.4 (8.87) |
| Median [Min, Max] | 69.0 [52.0, 86.0] | 70.0 [54.0, 87.0] | 75.0 [57.0, 91.0] | 70.0 [52.0, 91.0] |
| height | ||||
| Mean (SD) | 160 (5.49) | 160 (6.27) | 159 (6.14) | 160 (5.87) |
| Median [Min, Max] | 161 [150, 169] | 161 [145, 172] | 158 [151, 172] | 161 [145, 172] |
| weight | ||||
| Mean (SD) | 80.2 (16.7) | 69.4 (11.1) | 64.0 (13.9) | 73.1 (15.7) |
| Median [Min, Max] | 78.5 [48.0, 119] | 70.0 [48.0, 100] | 60.0 [45.0, 100] | 70.0 [45.0, 119] |
| bmi | ||||
| Mean (SD) | 31.4 (6.28) | 27.1 (4.30) | 25.0 (4.65) | 28.6 (5.90) |
| Median [Min, Max] | 30.8 [18.5, 42.2] | 26.3 [19.2, 38.2] | 24.0 [18.5, 35.4] | 27.1 [18.5, 42.2] |
Measurements
| normal (n=56) |
osteopenia (n=46) |
osteoporosis (n=25) |
Overall (n=127) |
|
|---|---|---|---|---|
| md_vol_all | ||||
| Mean (SD) | 20.7 (4.45) | 18.2 (3.43) | 17.6 (3.91) | 19.2 (4.21) |
| Median [Min, Max] | 20.3 [10.9, 31.3] | 17.9 [10.2, 27.8] | 16.6 [12.9, 29.7] | 18.9 [10.2, 31.3] |
| md_vol_small | ||||
| Mean (SD) | 9.08 (2.15) | 7.86 (1.56) | 8.12 (2.12) | 8.45 (2.02) |
| Median [Min, Max] | 8.93 [4.22, 14.1] | 7.84 [4.19, 11.9] | 8.01 [5.04, 15.0] | 8.22 [4.19, 15.0] |
| md_vol_forame | ||||
| Mean (SD) | 2.89 (1.35) | 2.30 (0.983) | 2.32 (1.17) | 2.57 (1.22) |
| Median [Min, Max] | 2.67 [0.730, 6.36] | 2.15 [0.370, 4.75] | 1.97 [1.11, 6.53] | 2.24 [0.370, 6.53] |
| mx_vol | ||||
| Mean (SD) | 4.63 (1.31) | 3.96 (1.41) | 3.73 (1.25) | 4.21 (1.38) |
| Median [Min, Max] | 4.55 [2.36, 8.45] | 3.61 [1.94, 7.78] | 3.23 [2.02, 6.35] | 4.08 [1.94, 8.45] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x1_viss | ||||
| Mean (SD) | 163 (40.6) | 144 (35.6) | 140 (41.8) | 152 (40.1) |
| Median [Min, Max] | 161 [56.4, 238] | 145 [68.3, 211] | 134 [71.5, 253] | 149 [56.4, 253] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x1_trab | ||||
| Mean (SD) | 73.0 (33.2) | 68.2 (26.1) | 70.8 (34.1) | 70.8 (30.8) |
| Median [Min, Max] | 72.1 [2.11, 155] | 66.3 [15.2, 127] | 71.8 [12.0, 126] | 71.1 [2.11, 155] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x1_cor | ||||
| Mean (SD) | 89.8 (23.2) | 75.6 (20.0) | 69.5 (23.6) | 80.7 (23.5) |
| Median [Min, Max] | 84.3 [43.9, 151] | 71.6 [30.8, 127] | 65.0 [30.2, 139] | 79.6 [30.2, 151] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x1_baz_viss | ||||
| Mean (SD) | 117 (17.5) | 107 (15.5) | 102 (15.7) | 111 (17.4) |
| Median [Min, Max] | 118 [86.0, 150] | 106 [77.0, 140] | 103 [75.8, 142] | 109 [75.8, 150] |
| Missing | 9 (16.1%) | 8 (17.4%) | 7 (28.0%) | 24 (18.9%) |
| x1_baz_trab | ||||
| Mean (SD) | 50.7 (12.6) | 50.8 (8.38) | 56.4 (12.3) | 51.7 (11.3) |
| Median [Min, Max] | 51.5 [23.1, 75.5] | 51.1 [32.8, 70.7] | 58.6 [36.1, 76.9] | 51.5 [23.1, 76.9] |
| Missing | 9 (16.1%) | 8 (17.4%) | 7 (28.0%) | 24 (18.9%) |
| x1_bas_cor | ||||
| Mean (SD) | 66.2 (19.8) | 55.9 (13.7) | 45.9 (16.2) | 58.8 (18.6) |
| Median [Min, Max] | 65.7 [28.0, 118] | 57.1 [33.7, 84.9] | 46.2 [14.3, 77.9] | 57.3 [14.3, 118] |
| Missing | 9 (16.1%) | 8 (17.4%) | 7 (28.0%) | 24 (18.9%) |
| x1_cort_viss | ||||
| Mean (SD) | 0.569 (0.136) | 0.537 (0.112) | 0.515 (0.168) | 0.547 (0.135) |
| Median [Min, Max] | 0.565 [0.330, 0.970] | 0.535 [0.260, 0.800] | 0.490 [0.210, 0.860] | 0.540 [0.210, 0.970] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x2_viss | ||||
| Mean (SD) | 149 (43.3) | 133 (35.6) | 128 (42.6) | 139 (41.2) |
| Median [Min, Max] | 146 [49.9, 243] | 129 [68.2, 218] | 123 [73.8, 227] | 136 [49.9, 243] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x2_trab | ||||
| Mean (SD) | 68.5 (36.3) | 62.9 (26.6) | 64.0 (33.4) | 65.6 (32.4) |
| Median [Min, Max] | 66.4 [3.11, 162] | 63.4 [11.8, 116] | 63.6 [15.3, 142] | 63.9 [3.11, 162] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x2_cor | ||||
| Mean (SD) | 80.6 (24.0) | 70.3 (19.9) | 63.6 (20.0) | 73.6 (22.7) |
| Median [Min, Max] | 78.2 [46.8, 143] | 67.4 [31.5, 126] | 61.5 [22.4, 117] | 69.3 [22.4, 143] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x2_baz_viss | ||||
| Mean (SD) | 113 (18.7) | 106 (15.2) | 102 (17.6) | 109 (17.5) |
| Median [Min, Max] | 109 [76.0, 159] | 106 [83.2, 139] | 105 [70.3, 138] | 107 [70.3, 159] |
| Missing | 12 (21.4%) | 11 (23.9%) | 11 (44.0%) | 34 (26.8%) |
| x2_baz_trab | ||||
| Mean (SD) | 52.0 (12.5) | 50.8 (10.0) | 55.7 (12.9) | 52.1 (11.7) |
| Median [Min, Max] | 52.7 [21.3, 81.3] | 50.0 [36.1, 77.0] | 52.8 [36.9, 82.4] | 52.2 [21.3, 82.4] |
| Missing | 12 (21.4%) | 11 (23.9%) | 11 (44.0%) | 34 (26.8%) |
| x2_baz_cor | ||||
| Mean (SD) | 60.5 (19.4) | 55.3 (14.1) | 46.3 (14.8) | 56.4 (17.4) |
| Median [Min, Max] | 58.3 [25.0, 127] | 53.0 [29.1, 82.5] | 45.3 [20.1, 73.4] | 53.2 [20.1, 127] |
| Missing | 12 (21.4%) | 11 (23.9%) | 11 (44.0%) | 34 (26.8%) |
| x2_cor_viss | ||||
| Mean (SD) | 0.564 (0.159) | 0.541 (0.130) | 0.520 (0.146) | 0.547 (0.146) |
| Median [Min, Max] | 0.555 [0.290, 0.950] | 0.530 [0.300, 0.850] | 0.515 [0.230, 0.800] | 0.540 [0.230, 0.950] |
| Missing | 0 (0%) | 0 (0%) | 1 (4.0%) | 1 (0.8%) |
| x3_viss | ||||
| Mean (SD) | 124 (47.4) | 110 (37.6) | 103 (40.4) | 115 (43.3) |
| Median [Min, Max] | 121 [37.3, 246] | 99.6 [48.4, 221] | 102 [40.0, 186] | 108 [37.3, 246] |
| x3_trab | ||||
| Mean (SD) | 66.9 (40.2) | 55.8 (28.3) | 53.5 (30.5) | 60.2 (34.7) |
| Median [Min, Max] | 60.1 [6.55, 181] | 52.2 [8.15, 125] | 56.5 [5.88, 136] | 56.3 [5.88, 181] |
| x3_cor | ||||
| Mean (SD) | 57.4 (20.2) | 54.2 (20.0) | 49.2 (18.4) | 54.6 (19.8) |
| Median [Min, Max] | 57.0 [25.9, 108] | 50.3 [19.1, 118] | 45.8 [16.8, 95.7] | 52.9 [16.8, 118] |
| x3_baz_viss | ||||
| Mean (SD) | 104 (14.6) | 96.2 (16.4) | 100 (17.2) | 101 (15.9) |
| Median [Min, Max] | 105 [75.6, 133] | 93.7 [67.3, 126] | 98.1 [77.0, 134] | 98.6 [67.3, 134] |
| Missing | 25 (44.6%) | 23 (50.0%) | 15 (60.0%) | 63 (49.6%) |
| x3_baz_trab | ||||
| Mean (SD) | 58.4 (13.2) | 52.7 (8.55) | 56.7 (8.37) | 56.1 (11.2) |
| Median [Min, Max] | 58.2 [30.5, 82.8] | 53.2 [36.9, 67.9] | 53.7 [44.6, 68.8] | 54.9 [30.5, 82.8] |
| Missing | 25 (44.6%) | 23 (50.0%) | 15 (60.0%) | 63 (49.6%) |
| x3_baz_cor | ||||
| Mean (SD) | 45.8 (13.1) | 43.5 (15.3) | 43.6 (15.1) | 44.6 (14.0) |
| Median [Min, Max] | 44.6 [24.1, 76.7] | 37.4 [16.8, 81.0] | 39.6 [25.2, 69.2] | 42.2 [16.8, 81.0] |
| Missing | 25 (44.6%) | 23 (50.0%) | 15 (60.0%) | 63 (49.6%) |
| x3_cor_viss | ||||
| Mean (SD) | 0.498 (0.169) | 0.511 (0.152) | 0.506 (0.164) | 0.505 (0.161) |
| Median [Min, Max] | 0.485 [0.220, 0.890] | 0.485 [0.210, 0.850] | 0.490 [0.230, 0.880] | 0.490 [0.210, 0.890] |
| x4_viss | ||||
| Mean (SD) | 123 (45.4) | 111 (41.7) | 104 (40.2) | 115 (43.5) |
| Median [Min, Max] | 121 [34.9, 232] | 99.1 [32.4, 223] | 96.3 [42.0, 206] | 109 [32.4, 232] |
| x4_trab | ||||
| Mean (SD) | 66.9 (37.3) | 58.5 (32.2) | 55.4 (33.6) | 61.6 (34.9) |
| Median [Min, Max] | 61.8 [5.58, 169] | 54.0 [6.57, 147] | 53.1 [8.24, 160] | 56.5 [5.58, 169] |
| x4_cor | ||||
| Mean (SD) | 56.0 (20.5) | 52.0 (20.3) | 48.3 (19.7) | 53.0 (20.4) |
| Median [Min, Max] | 55.7 [19.8, 111] | 49.6 [18.9, 116] | 46.4 [7.35, 89.2] | 50.7 [7.35, 116] |
| x4_baz_viss | ||||
| Mean (SD) | 105 (16.1) | 100 (17.3) | 96.8 (14.2) | 102 (16.4) |
| Median [Min, Max] | 103 [75.3, 135] | 97.4 [67.7, 134] | 96.8 [74.6, 124] | 100 [67.7, 135] |
| Missing | 26 (46.4%) | 25 (54.3%) | 16 (64.0%) | 67 (52.8%) |
| x4_baz_trab | ||||
| Mean (SD) | 61.3 (11.0) | 57.6 (9.95) | 59.7 (9.83) | 59.7 (10.4) |
| Median [Min, Max] | 59.3 [39.1, 83.0] | 56.4 [41.3, 78.3] | 59.3 [49.4, 76.3] | 57.6 [39.1, 83.0] |
| Missing | 26 (46.4%) | 25 (54.3%) | 16 (64.0%) | 67 (52.8%) |
| x4_baz_cor | ||||
| Mean (SD) | 44.1 (15.1) | 42.0 (13.3) | 37.1 (12.7) | 42.3 (14.1) |
| Median [Min, Max] | 44.3 [19.6, 80.7] | 37.8 [26.3, 77.3] | 33.6 [24.0, 64.7] | 39.7 [19.6, 80.7] |
| Missing | 26 (46.4%) | 25 (54.3%) | 16 (64.0%) | 67 (52.8%) |
| x4_cor_viss | ||||
| Mean (SD) | 0.486 (0.166) | 0.495 (0.155) | 0.494 (0.194) | 0.491 (0.167) |
| Median [Min, Max] | 0.480 [0.210, 0.920] | 0.470 [0.240, 0.800] | 0.480 [0.100, 0.840] | 0.480 [0.100, 0.920] |
Grey values
| normal (n=56) |
osteopenia (n=46) |
osteoporosis (n=25) |
Overall (n=127) |
|
|---|---|---|---|---|
| c1_axial | ||||
| Mean (SD) | 78.3 (95.0) | 25.1 (107) | -20.3 (88.8) | 39.6 (105) |
| Median [Min, Max] | 69.7 [-204, 252] | 40.2 [-216, 196] | -15.4 [-194, 195] | 47.7 [-216, 252] |
| c1sagital | ||||
| Mean (SD) | 71.3 (90.8) | 26.4 (105) | -14.3 (78.6) | 38.1 (99.1) |
| Median [Min, Max] | 73.7 [-152, 252] | 33.8 [-212, 229] | -9.10 [-168, 149] | 47.2 [-212, 252] |
| c2_axial | ||||
| Mean (SD) | 173 (96.2) | 132 (100) | 82.6 (81.6) | 140 (100) |
| Median [Min, Max] | 179 [-64.3, 424] | 148 [-109, 289] | 81.9 [-72.6, 249] | 147 [-109, 424] |
| Missing | 1 (1.8%) | 0 (0%) | 0 (0%) | 1 (0.8%) |
| c2sagital | ||||
| Mean (SD) | 163 (104) | 122 (102) | 83.5 (74.1) | 132 (102) |
| Median [Min, Max] | 159 [-69.4, 471] | 128 [-132, 330] | 63.6 [-66.9, 212] | 132 [-132, 471] |
| Missing | 1 (1.8%) | 0 (0%) | 0 (0%) | 1 (0.8%) |
Inferential analysis
By groups
Measurements by area and dx
Plot
ANOVA Mean comparison measurement by area
Anova table
Call:
glm(formula = value ~ dx * zone * clinical_variable_2 + ID +
age + md_vol_all + mx_vol, data = measurements_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-70.659 -16.272 -1.083 13.597 95.448
Coefficients:
Estimate Std. Error t value
(Intercept) 11.855113 8.129928 1.458
dxOsteopenia -4.459232 4.705341 -0.948
dxOsteoporosis -8.262887 5.874036 -1.407
zoneCanine/PreMol -9.206429 4.441809 -2.073
zone1st Molar -32.405536 4.441809 -7.296
zone2d Molar -33.819821 4.441809 -7.614
clinical_variable_2Trabeculae -16.823571 4.441809 -3.788
ID 0.003039 0.020779 0.146
age -0.017276 0.085019 -0.203
md_vol_all 3.661650 0.207957 17.608
mx_vol 0.663376 0.625226 1.061
dxOsteopenia:zoneCanine/PreMol 3.884255 6.614256 0.587
dxOsteoporosis:zoneCanine/PreMol 3.172516 8.232078 0.385
dxOsteopenia:zone1st Molar 11.054666 6.614256 1.671
dxOsteoporosis:zone1st Molar 11.030428 8.171122 1.350
dxOsteopenia:zone2d Molar 10.254821 6.614256 1.550
dxOsteoporosis:zone2d Molar 11.612214 8.171122 1.421
dxOsteopenia:clinical_variable_2Trabeculae 9.428137 6.614256 1.425
dxOsteoporosis:clinical_variable_2Trabeculae 17.404876 8.232078 2.114
zoneCanine/PreMol:clinical_variable_2Trabeculae 4.745179 6.281666 0.755
zone1st Molar:clinical_variable_2Trabeculae 26.296071 6.281666 4.186
zone2d Molar:clinical_variable_2Trabeculae 27.781250 6.281666 4.423
dxOsteopenia:zoneCanine/PreMol:clinical_variable_2Trabeculae -4.721048 9.353971 -0.505
dxOsteoporosis:zoneCanine/PreMol:clinical_variable_2Trabeculae -4.750831 11.641917 -0.408
dxOsteopenia:zone1st Molar:clinical_variable_2Trabeculae -17.342811 9.353971 -1.854
dxOsteoporosis:zone1st Molar:clinical_variable_2Trabeculae -22.564042 11.555633 -1.953
dxOsteopenia:zone2d Molar:clinical_variable_2Trabeculae -13.871250 9.353971 -1.483
dxOsteoporosis:zone2d Molar:clinical_variable_2Trabeculae -21.208388 11.555633 -1.835
Pr(>|t|)
(Intercept) 0.145105
dxOsteopenia 0.343519
dxOsteoporosis 0.159841
zoneCanine/PreMol 0.038465 *
zone1st Molar 6.15e-13 ***
zone2d Molar 6.26e-14 ***
clinical_variable_2Trabeculae 0.000161 ***
ID 0.883748
age 0.839020
md_vol_all < 2e-16 ***
mx_vol 0.288944
dxOsteopenia:zoneCanine/PreMol 0.557168
dxOsteoporosis:zoneCanine/PreMol 0.700037
dxOsteopenia:zone1st Molar 0.094975 .
dxOsteoporosis:zone1st Molar 0.177352
dxOsteopenia:zone2d Molar 0.121367
dxOsteoporosis:zone2d Molar 0.155599
dxOsteopenia:clinical_variable_2Trabeculae 0.154354
dxOsteoporosis:clinical_variable_2Trabeculae 0.034745 *
zoneCanine/PreMol:clinical_variable_2Trabeculae 0.450191
zone1st Molar:clinical_variable_2Trabeculae 3.09e-05 ***
zone2d Molar:clinical_variable_2Trabeculae 1.08e-05 ***
dxOsteopenia:zoneCanine/PreMol:clinical_variable_2Trabeculae 0.613876
dxOsteoporosis:zoneCanine/PreMol:clinical_variable_2Trabeculae 0.683305
dxOsteopenia:zone1st Molar:clinical_variable_2Trabeculae 0.064032 .
dxOsteoporosis:zone1st Molar:clinical_variable_2Trabeculae 0.051147 .
dxOsteopenia:zone2d Molar:clinical_variable_2Trabeculae 0.138417
dxOsteoporosis:zone2d Molar:clinical_variable_2Trabeculae 0.066761 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 552.4306)
Null deviance: 870298 on 1003 degrees of freedom
Residual deviance: 539172 on 976 degrees of freedom
(12 observations deleted due to missingness)
AIC: 9218.4
Number of Fisher Scoring iterations: 2Just to check, omit in final report
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
==========================================================================================
Dependent variable:
---------------------------
value
------------------------------------------------------------------------------------------
dxOsteopenia -4.459
(4.705)
dxOsteoporosis -8.263
(5.874)
zoneCanine/PreMol -9.206**
(4.442)
zone1st Molar -32.406***
(4.442)
zone2d Molar -33.820***
(4.442)
clinical_variable_2Trabeculae -16.824***
(4.442)
ID 0.003
(0.021)
age -0.017
(0.085)
md_vol_all 3.662***
(0.208)
mx_vol 0.663
(0.625)
dxOsteopenia:zoneCanine/PreMol 3.884
(6.614)
dxOsteoporosis:zoneCanine/PreMol 3.173
(8.232)
dxOsteopenia:zone1st Molar 11.055*
(6.614)
dxOsteoporosis:zone1st Molar 11.030
(8.171)
dxOsteopenia:zone2d Molar 10.255
(6.614)
dxOsteoporosis:zone2d Molar 11.612
(8.171)
dxOsteopenia:clinical_variable_2Trabeculae 9.428
(6.614)
dxOsteoporosis:clinical_variable_2Trabeculae 17.405**
(8.232)
zoneCanine/PreMol:clinical_variable_2Trabeculae 4.745
(6.282)
zone1st Molar:clinical_variable_2Trabeculae 26.296***
(6.282)
zone2d Molar:clinical_variable_2Trabeculae 27.781***
(6.282)
dxOsteopenia:zoneCanine/PreMol:clinical_variable_2Trabeculae -4.721
(9.354)
dxOsteoporosis:zoneCanine/PreMol:clinical_variable_2Trabeculae -4.751
(11.642)
dxOsteopenia:zone1st Molar:clinical_variable_2Trabeculae -17.343*
(9.354)
dxOsteoporosis:zone1st Molar:clinical_variable_2Trabeculae -22.564*
(11.556)
dxOsteopenia:zone2d Molar:clinical_variable_2Trabeculae -13.871
(9.354)
dxOsteoporosis:zone2d Molar:clinical_variable_2Trabeculae -21.208*
(11.556)
Constant 11.855
(8.130)
------------------------------------------------------------------------------------------
Observations 1,004
Log Likelihood -4,581.208
Akaike Inf. Crit. 9,218.416
==========================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01Alternatives to show
Delete the anova model
Grey values by area and dx
ANOVA Grey values by area
Anova table
Call:
glm(formula = value ~ dx * zone * clinical_variable_2 + ID +
age + md_vol_all + mx_vol, data = grey_values)
Deviance Residuals:
Min 1Q Median 3Q Max
-261.064 -59.198 7.922 61.953 272.553
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 235.95358 43.42485 5.434 8.75e-08 ***
dxOsteopenia -56.19337 18.59976 -3.021 0.002651 **
dxOsteoporosis -108.37027 22.95502 -4.721 3.07e-06 ***
zoneC2 94.49332 17.53299 5.389 1.10e-07 ***
clinical_variable_2Sagital -7.14442 17.45371 -0.409 0.682474
ID 0.07478 0.01924 3.887 0.000116 ***
age -2.31864 0.47242 -4.908 1.26e-06 ***
md_vol_all 0.92331 1.15628 0.799 0.424956
mx_vol -8.96808 3.47460 -2.581 0.010142 *
dxOsteopenia:zoneC2 12.45929 26.04347 0.478 0.632578
dxOsteoporosis:zoneC2 3.85712 31.90946 0.121 0.903838
dxOsteopenia:clinical_variable_2Sagital 8.29138 25.99014 0.319 0.749848
dxOsteoporosis:clinical_variable_2Sagital 11.68631 31.86595 0.367 0.713977
zoneC2:clinical_variable_2Sagital -3.31945 24.79520 -0.134 0.893557
dxOsteopenia:zoneC2:clinical_variable_2Sagital -8.55446 36.83088 -0.232 0.816431
dxOsteoporosis:zoneC2:clinical_variable_2Sagital 1.24445 45.12667 0.028 0.978011
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 8529.686)
Null deviance: 6333853 on 501 degrees of freedom
Residual deviance: 4145427 on 486 degrees of freedom
(6 observations deleted due to missingness)
AIC: 5986.1
Number of Fisher Scoring iterations: 2Just to check, omit in final report
Check values 49, 252 and 460
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
Regression analysis - Grey Values
============================================================================
Dependent variable
---------------------------
Relative Grey Values
----------------------------------------------------------------------------
Osteopenia -56.193***
(18.600)
Osteoporosis -108.370***
(22.955)
C2 94.493***
(17.533)
Sagital -7.144
(17.454)
ID 0.075***
(0.019)
Osteopenia:C2 -2.319***
(0.472)
Osteoporsis:C2 0.923
(1.156)
Osteopenia:Sagital -8.968**
(3.475)
Osteoporosis:Sagital 12.459
(26.043)
Osteopenia:Sagital 3.857
(31.909)
Osteoporosis:Sagital 8.291
(25.990)
Constant 11.686
(31.866)
zoneC2:clinical_variable_2Sagital -3.319
(24.795)
dxOsteopenia:zoneC2:clinical_variable_2Sagital -8.554
(36.831)
dxOsteoporosis:zoneC2:clinical_variable_2Sagital 1.244
(45.127)
Constant 235.954***
(43.425)
----------------------------------------------------------------------------
Observations 502
Log Likelihood -2,977.055
Akaike Inf. Crit. 5,986.110
============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01Alternatives to show
Delete the anova model
Correlation
Measurements
Grey values
Reliability analysis: are the measurements reliable?
See
Koo, Terry, and Mae Li. 2016. “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.” Journal of Chiropractic Medicine 15 (March). doi:10.1016/j.jcm.2016.02.012.
Shrout, P.E., and J.L. Fleiss. 1979. “Intraclass Correlation: Uses in Assessing Rater Reliability.” Psychological Bulletin 86: 420–28.
For x1_viss
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 29
Raters = 2
ICC(A,1) = 0.898
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(28,29) = 18.5 , p = 4.52e-12
95%-Confidence Interval for ICC Population Values:
0.797 < ICC < 0.951For x1_trab
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 29
Raters = 2
ICC(A,1) = 0.999
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(28,28.9) = 2744 , p = 9.63e-43
95%-Confidence Interval for ICC Population Values:
0.998 < ICC < 1For x1_baz_viss
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 25
Raters = 2
ICC(A,1) = 0.997
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(24,18.6) = 669 , p = 1.05e-22
95%-Confidence Interval for ICC Population Values:
0.992 < ICC < 0.999For x1_baz_trab
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 25
Raters = 2
ICC(A,1) = 0.986
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(24,24.9) = 140 , p = 5.04e-21
95%-Confidence Interval for ICC Population Values:
0.969 < ICC < 0.994For c1_axial
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 10
Raters = 2
ICC(A,1) = 0.968
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(9,9.02) = 56.4 , p = 7.47e-07
95%-Confidence Interval for ICC Population Values:
0.878 < ICC < 0.992For c1_sagital
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 9
Raters = 2
ICC(A,1) = 0.972
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(8,8.49) = 77.5 , p = 4.43e-07
95%-Confidence Interval for ICC Population Values:
0.887 < ICC < 0.994Every measurement has almost perfect reliability ### BLand Altman plots for grey values
NULL
NULL
---
title: "Osteoporosis AS Final March 2020"
output:
  html_notebook:
    theme: cerulean
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      warning = FALSE,
                      message = FALSE)
```

# Packages

```{r}
if (!require("pacman")) install.packages("pacman")
```

```{r, warning=FALSE, message=FALSE, results='hide'}
pacman::p_load(tidyverse,  # for data visualization and wrangling 
               ggpval,     # for p values in plots
               car,        # for regression
               broom,      # for model evaluation
               pubh,       # for glm_coefs
               sjPlot,     # plot_model
               tidymodels, # for modelling with broom
               rstatix,    # for anova in the pipe
               emmeans,    # plotting
               BlandAltmanLeh, # BA Plot
               stargazer,   # beautiful reports
               ggsignif,    # for significance lines in ggplot2 

               ordinal, 
               ggpubr,     # for data visualization enhancements
               janitor,    # for data cleaning 
               visdat,     # to vizualise missing data 
               table1,     # to create summary tables
               tableone)   # to create summary tables with p values
```

Install papaja for anova report
```{r}
#devtools::install_github("crsh/papaja")
if (!require("papaja")) install.packages("papaja")
library(papaja)
```


# Dataset
```{r, results='hide'}
df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vT5jtQKSkP1h0pNGzFvJ-B3HiQuvIxAKXSfzxZVQSiM7wr6Ub61xAs4t13O0ya0BZ6ziZ-anWt5Fcsf/pub?gid=1936031181&single=true&output=csv")
```
## Data cleaning and new variables

Clean the names
```{r}
df <- janitor::clean_names(df) # standarize all names from columns, more easier to work with
```

 Remove the names column
 
```{r}
df <- select(df, -vards_uzvards)
```


```{r new var dx}
df <- df %>%
  mutate(dx = case_when(
    dxa_l2_l4 < -2.5 ~ "osteoporosis",
    dxa_l2_l4 > -1 ~ "normal",
    TRUE ~ "osteopenia"
  ))
```
check
```{r}
table(df$dx)
```


# Results

## Table 1

### Demographics
```{r}
table1::table1(~ age + 
              height + 
              weight + 
              bmi 
               | dx, 
               data = df)
```
### Measurements
```{r}
table1::table1(
  ~ md_vol_all +
    md_vol_small +
    md_vol_forame +
    mx_vol +
    x1_viss +
    x1_trab +
    x1_cor +
    x1_baz_viss +
    x1_baz_trab +
    x1_bas_cor +
    x1_cort_viss +
    x2_viss +
    x2_trab +
    x2_cor +
    x2_baz_viss +
    x2_baz_trab +
    x2_baz_cor +
    x2_cor_viss +
    x3_viss +
    x3_trab +
    x3_cor +
    x3_baz_viss +
    x3_baz_trab +
    x3_baz_cor +
    x3_cor_viss +
    x4_viss +
    x4_trab +
    x4_cor +
    x4_baz_viss +
    x4_baz_trab +
    x4_baz_cor +
    x4_cor_viss
  | dx,
  data = df
)
```



### Grey values

```{r}
table1::table1( ~  c1_axial +
                  c1sagital +
                  c2_axial +
                  c2sagital
                | dx,
                data = df)
```


# Inferential analysis      
## By groups
### Measurements by area and dx

```{r}
measurements_data <- df %>%
  tibble::rowid_to_column(., "ID") %>% 
  pivot_longer(x1_viss:x4_cor_viss,
               # columns to merge
               names_to = "clinical_variable",
               #name of the new colum with the names
               values_to = "value") %>%  #name of the new column with values
  select(clinical_variable,
         value,
         dx,
         dxa_l2_l4,
         ID, 
         age, 
         md_vol_all, 
         mx_vol) %>%
  filter(!str_detect(clinical_variable, "cor_viss")) %>%  # we filter OUT (!)
  filter(!str_detect(clinical_variable, "2$")) %>%  # again
  filter(clinical_variable != "x1_cort_viss") %>%  # again
  filter(!str_detect(clinical_variable, "_bas_")) %>%
  filter(!str_detect(clinical_variable, "_baz_")) %>%
  filter(!str_detect(clinical_variable, "viss$")) %>%  # omit all viss values
  separate(clinical_variable,
           into = c("zone", "clinical_variable_2")) %>%
  mutate(zone = fct_recode(
    zone,
    "Incisor" = "x1",
    "Canine/PreMol" = "x2",
    "1st Molar" = "x3",
    "2d Molar" = "x4"
  )) %>%
  mutate(
    dx = fct_recode(
      dx,
      "Normal" = "normal",
      "Osteopenia" = "osteopenia",
      "Osteoporosis" = "osteoporosis"
    )
  ) %>%
  mutate(clinical_variable_2 = fct_recode(
    clinical_variable_2,
    "Cortical" = "cor",
    "Trabeculae" = "trab"
  ))
```

Plot


```{r}
measurements_data %>%
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  # facet_grid(clinical_variable_2 ~ zone) + # facetting (rows ~ columns)
  labs(
    title = "Measurements (mm), all bone",
    # subtitle = "this is the subtitle",
    y = "mm",
    x = "Bone status",
    color = "Bone status"
  ) +
  # scale_color_manual(labels = c("T999", "T888", "2"), values = c("green", "yellow", "red"))
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```
```{r}
measurements_data %>%
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  facet_grid(. ~ zone) + # facetting (rows ~ columns)
  labs(
    title = "Measurements (mm), by area",
    # subtitle = "this is the subtitle",
    y = "mm",
    x = "Bone status",
    color = "Bone status"
  ) +
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```
```{r}
measurements_data %>%
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  facet_grid(. ~ clinical_variable_2) + # facetting (rows ~ columns)
  labs(
    title = "Measurements (mm), by cortical or trabeculae",
    # subtitle = "this is the subtitle",
    y = "mm",
    x = "Bone status",
    color = "Bone status"
  ) +
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```



```{r}
measurements_data %>%
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  facet_grid(clinical_variable_2 ~ zone) + # facetting (rows ~ columns)
  labs(
    title = "Measurements (mm), all bone",
    # subtitle = "this is the subtitle",
    y = "mm",
    x = "Bone status",
    color = "Bone status"
  ) +
  # scale_color_manual(labels = c("T999", "T888", "2"), values = c("green", "yellow", "red"))
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```
### ANOVA Mean comparison measurement by area 

```{r}
anova_measurements <- glm(value ~
                            dx * zone * clinical_variable_2 +
                            ID + age +  md_vol_all + mx_vol,
                          data = measurements_data)

```
Anova table
```{r}
summary(anova_measurements)
```


Just to check, omit in final report
```{r}
# plot(anova_measurements)
```



```{r}
stargazer(anova_measurements, type = "text", summary = FALSE)
```



Alternatives to show
```{r}

pacman::p_load(beeswarm)
apa_beeplot(
  data = measurements_data
  , id = "ID"
  , dv = "value"
  , factors = c("dx", "clinical_variable_2", "zone")
)

```

Delete the anova model

```{r}
rm(anova_measurements)

```





## Grey values by area and dx

```{r}
grey_values <- df %>%
  pivot_longer(c1_axial:c1_sagital_2,
               # columns to merge
               names_to = "clinical_variable",
               #name of the new colum with the names
               values_to = "value") %>%  #name of the new column with values
  tibble::rowid_to_column(., "ID") %>% 
# select only some columns
  select(clinical_variable,
         value,
         dx,
         dxa_l2_l4, 
         ID, 
         age, 
         md_vol_all, 
         mx_vol) %>%
# normalize names
  mutate(clinical_variable = case_when(
    clinical_variable == "c1sagital" ~ "c1_sagital", 
    clinical_variable == "c2sagital" ~ "c2_sagital", 
    TRUE ~ as.character(.$clinical_variable)
  )) %>% 
# filter out second measurements
  filter(!str_detect(clinical_variable, "2$")) %>%  # again
# separate clinical variable in two
  separate(clinical_variable,
           into = c("zone", "clinical_variable_2")) %>%
  mutate(zone = fct_recode(
    zone,
    "C1" = "c1",
    "C2" = "c2"
  )) %>%
  mutate(
    dx = fct_recode(
      dx,
      "Normal" = "normal",
      "Osteopenia" = "osteopenia",
      "Osteoporosis" = "osteoporosis"
    )
  ) %>%
  mutate(clinical_variable_2 = fct_recode(
    clinical_variable_2,
    "Axial" = "axial",
    "Sagital" = "sagital"
  ))

```

```{r}
grey_values %>% 
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  # facet_grid(clinical_variable_2 ~ zone) + # facetting (rows ~ columns)
  labs(
    title = "Relative Grey Values",
    # subtitle = "this is the subtitle",
    y = "Relative Grey Value",
    x = "Bone status",
    color = "Bone status"
  ) +
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```
```{r}
grey_values %>% 
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  facet_grid(. ~ zone) + # facetting (rows ~ columns)
  labs(
    title = "Relative Grey Values",
    # subtitle = "this is the subtitle",
    y = "Relative Grey Value",
    x = "Bone status",
    color = "Bone status"
  ) +
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```
```{r}
grey_values %>% 
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  facet_grid(. ~ clinical_variable_2) + # facetting (rows ~ columns)
  labs(
    title = "Relative Grey Values",
    # subtitle = "this is the subtitle",
    y = "Relative Grey Value",
    x = "Bone status",
    color = "Bone status"
  ) +
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```


```{r}
grey_values %>% 
  ggplot(aes(x = dx,
             y = value,
             color = dx)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.05) +
  facet_grid(clinical_variable_2 ~ zone) + # facetting (rows ~ columns)
  labs(
    title = "Relative Grey Values",
    # subtitle = "this is the subtitle",
    y = "Relative Grey Value",
    x = "Bone status",
    color = "Bone status"
  ) +
  ggpubr::theme_pubclean() + # the color scheme, try others
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )
```

### ANOVA Grey values by area 

```{r}
anova_measurements <- glm(value ~
                            dx * zone * clinical_variable_2 +
                            ID + age + md_vol_all + mx_vol,
                          data = grey_values)

```
Anova table
```{r}
summary(anova_measurements)
```


Just to check, omit in final report
```{r}
# plot(anova_measurements)
```
Check values 49, 252 and 460

```{r}
stargazer(anova_measurements, type = "text", summary = FALSE, 
          title            = "Regression analysis - Grey Values",
          covariate.labels = c("Osteopenia", 
                               "Osteoporosis", 
                               "C2", 
                               "Sagital", 
                               "ID", 
                               "Osteopenia:C2", 
                               "Osteoporsis:C2", 
                               "Osteopenia:Sagital", 
                               "Osteoporosis:Sagital", 
                               "Osteopenia:Sagital", 
                               "Osteoporosis:Sagital", 
                               "Constant"),
          dep.var.caption  = "Dependent variable",
          dep.var.labels   = "Relative Grey Values")
```



Alternatives to show
```{r}

pacman::p_load(beeswarm)
apa_beeplot(
  data = grey_values
  , id = "ID"
  , dv = "value"
  , factors = c("dx", "clinical_variable_2", "zone")
)

```

Delete the anova model

```{r}
rm(anova_measurements)

```



## Correlation


### Measurements
```{r}
measurements_data %>% 
  ggplot(aes(x = dxa_l2_l4, 
             y = value, 
             color = dx)) + 
  geom_point(alpha = .7) + 
  facet_grid(clinical_variable_2 ~ zone) + 
  ggpubr::theme_pubclean() + 
  labs(
    title = "Measurements vs DXA", 
    y = "Measurement (mm)", 
    x = "DXA L2 L4", 
    color = "Bone status"
  ) 
```

### Grey values

```{r}
grey_values %>% 
  ggplot(aes(x = dxa_l2_l4, 
             y = value, 
             color = dx)) + 
  geom_point(alpha = .7) + 
  facet_grid(clinical_variable_2 ~ zone) + 
  ggpubr::theme_pubclean() + 
  labs(
    title = "Relative grey values vs DXA", 
    y = "Relative grey value", 
    x = "DXA L2 L4", 
    color = "Bone status"
  ) 
```
# Reliability analysis: are the measurements reliable?



See 

Koo, Terry, and Mae Li. 2016. “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.” Journal of Chiropractic Medicine 15 (March). doi:10.1016/j.jcm.2016.02.012.

Shrout, P.E., and J.L. Fleiss. 1979. “Intraclass Correlation: Uses in Assessing Rater Reliability.” Psychological Bulletin 86: 420–28.

```{r, warning=FALSE}
pacman::p_load(irr) # package to calculate the ICC

```

```{r}
agreement <- df %>%
  select(
    "x1_viss",
    "x1_viss2",
    "x1_trab",
    "x1_trab2",
    "x1_baz_viss",
    "x1_baz_viss2",
    "x1_baz_trab",
    "x1_baz_trab_2",
    "c1_axial",
    "c1_axial_2",
    "c1sagital",
    "c1_sagital_2"
  )
```

### For x1_viss
```{r}

agreement %>%
  select("x1_viss",
         "x1_viss2") %>%
  filter(x1_viss2 > 0) %>%
  irr::icc(.,
           model = "twoway",
           type = "agreement",
           unit = "single")
```
### For x1_trab
```{r}
  
agreement %>%
  select(    "x1_trab",
    "x1_trab2") %>%
  filter(x1_trab2 > 0) %>%
  irr::icc(.,
           model = "twoway",
           type = "agreement",
           unit = "single")


```
### For x1_baz_viss
```{r}
agreement %>%
  select("x1_baz_viss",
    "x1_baz_viss2") %>%
  filter(x1_baz_viss2 > 0) %>%
  irr::icc(.,
           model = "twoway",
           type = "agreement",
           unit = "single")

```
### For x1_baz_trab
```{r}

agreement %>%
  select("x1_baz_trab",
    "x1_baz_trab_2") %>%
  filter(x1_baz_trab_2 > 0) %>%
  irr::icc(.,
           model = "twoway",
           type = "agreement",
           unit = "single")

```

### For c1_axial
```{r}

agreement %>%
  select("c1_axial",
    "c1_axial_2") %>%
  filter(c1_axial_2 > 0) %>%
  irr::icc(.,
           model = "twoway",
           type = "agreement",
           unit = "single")

```

### For c1_sagital
```{r}
agreement %>%
  select("c1sagital",
    "c1_sagital_2") %>%
  filter(c1_sagital_2 > 0) %>%
  irr::icc(.,
           model = "twoway",
           type = "agreement",
           unit = "single")
```
```{r}
rm(agreement)
```

Every measurement has almost perfect reliability
### BLand Altman plots for grey values

```{r}
BlandAltmanLeh::bland.altman.plot(df$c1_axial, 
                                  df$c1_axial_2, 
                                  main="C1 axial")
```
```{r}
BlandAltmanLeh::bland.altman.plot(df$c1sagital, 
                                  df$c1_sagital_2, 
                                  main="C1 Sagital")
```

