Smartphone ALLFTD Renewal
Mark Sanderson-cimino
2024-08-16
LME models (Project 1: Familial)
Sample description for model 1 (familial)
LME model 1: Mutation pos, baseline CDR 0 or .5
Presents graphs first, then a print out of the models. Note the failure to converge/warnings of several models.
Removed condition requiring age<=45. Using original CDR+NACC-FTLD sum of boxes.
## [1] "y.plot = baseline_gonogo"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -474.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.9368 -0.0330 -0.0090 0.0061 3.4949
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.050737 0.22525
## monthssincebaseline 0.000102 0.01010 0.14
## Residual 0.003667 0.06056
## Number of obs: 315, groups: unique_id, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.9615111 0.2373767 48.5717351 -4.051 0.000183
## y.plot -0.0605132 0.0312129 50.5530135 -1.939 0.058127
## monthssincebaseline 0.0015001 0.0017248 35.4089733 0.870 0.390322
## baseline_age 0.0007501 0.0027227 48.8007409 0.275 0.784103
## educ 0.0267250 0.0138372 49.2535999 1.931 0.059199
## y.plot:monthssincebaseline 0.0008837 0.0017735 34.4391876 0.498 0.621472
##
## (Intercept) ***
## y.plot .
## monthssincebaseline
## baseline_age
## educ .
## y.plot:monthssincebaseline
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.193
## mnthssncbsl 0.027 -0.003
## baseline_ag -0.374 0.236 0.007
## educ -0.846 0.069 -0.021 -0.162
## y.plt:mnths -0.002 0.075 -0.054 0.012 -0.005
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -494.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.1878 -0.0349 -0.0077 0.0088 3.5877
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.320e-02 0.207857
## monthssincebaseline 8.974e-05 0.009473 0.29
## Residual 3.536e-03 0.059465
## Number of obs: 317, groups: unique_id, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.727717 0.228528 48.034736 -3.184 0.00255 **
## y.plot -0.082219 0.036962 53.379565 -2.224 0.03037 *
## monthssincebaseline 0.001745 0.001589 36.367393 1.099 0.27916
## baseline_age -0.003807 0.002911 50.137817 -1.308 0.19694
## educ 0.023019 0.012535 48.715905 1.836 0.07241 .
## y.plot:monthssincebaseline -0.002951 0.001646 37.170535 -1.792 0.08123 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.454
## mnthssncbsl 0.031 0.028
## baseline_ag -0.467 0.597 0.042
## educ -0.815 0.133 -0.036 -0.118
## y.plt:mnths 0.016 0.140 -0.040 -0.010 -0.010
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -527.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.2761 -0.0356 -0.0073 0.0122 3.6129
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.388e-02 0.209481
## monthssincebaseline 9.168e-05 0.009575 0.12
## Residual 3.468e-03 0.058890
## Number of obs: 337, groups: unique_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.660507 0.225013 56.438008 -2.935 0.004811 **
## y.plot -0.135391 0.034340 58.621849 -3.943 0.000218 ***
## monthssincebaseline 0.001351 0.001560 40.248697 0.866 0.391808
## baseline_age -0.006129 0.002977 56.993202 -2.059 0.044069 *
## educ 0.027972 0.012257 56.526326 2.282 0.026262 *
## y.plot:monthssincebaseline -0.001179 0.001582 41.111202 -0.746 0.460211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.421
## mnthssncbsl 0.014 0.011
## baseline_ag -0.477 0.611 0.015
## educ -0.786 0.048 -0.017 -0.155
## y.plt:mnths 0.010 0.032 -0.045 -0.010 -0.004
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -461.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.8705 -0.0411 -0.0069 0.0183 3.4766
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 3.477e-02 0.186463
## monthssincebaseline 8.745e-05 0.009351 0.32
## Residual 3.741e-03 0.061165
## Number of obs: 294, groups: unique_id, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.858912 0.186974 40.944166 -4.594 4.11e-05 ***
## y.plot -0.094518 0.027515 46.403799 -3.435 0.00126 **
## monthssincebaseline 0.002183 0.001636 31.896114 1.334 0.19171
## baseline_age -0.003337 0.002404 44.007779 -1.388 0.17209
## educ 0.029477 0.011818 42.979765 2.494 0.01655 *
## y.plot:monthssincebaseline -0.004965 0.001682 32.355292 -2.952 0.00584 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.068
## mnthssncbsl 0.057 0.024
## baseline_ag -0.259 0.420 0.032
## educ -0.821 -0.169 -0.043 -0.321
## y.plt:mnths 0.004 0.169 -0.076 -0.032 0.015
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -512.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.1820 -0.0386 -0.0044 0.0120 3.5821
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.560e-02 0.21355
## monthssincebaseline 9.526e-05 0.00976 0.18
## Residual 3.523e-03 0.05935
## Number of obs: 331, groups: unique_id, 59
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.946013 0.211326 52.285275 -4.477 4.14e-05 ***
## y.plot -0.104430 0.032851 53.914199 -3.179 0.00245 **
## monthssincebaseline 0.001363 0.001602 38.771641 0.851 0.40014
## baseline_age -0.003491 0.002784 52.646498 -1.254 0.21540
## educ 0.037316 0.013087 52.897751 2.851 0.00620 **
## y.plot:monthssincebaseline -0.000608 0.001593 39.143630 -0.382 0.70482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.087
## mnthssncbsl 0.030 0.019
## baseline_ag -0.295 0.508 0.022
## educ -0.808 -0.216 -0.028 -0.310
## y.plt:mnths 0.011 0.102 -0.021 0.012 -0.017
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -525.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.2668 -0.0410 -0.0045 0.0123 3.6122
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.524e-02 0.212690
## monthssincebaseline 9.238e-05 0.009612 0.15
## Residual 3.473e-03 0.058930
## Number of obs: 337, groups: unique_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.8634930 0.2117020 55.1526351 -4.079 0.000147
## y.plot -0.1264186 0.0344857 56.3742944 -3.666 0.000547
## monthssincebaseline 0.0013042 0.0015636 40.2386482 0.834 0.409162
## baseline_age -0.0053537 0.0029414 55.4003320 -1.820 0.074143
## educ 0.0378315 0.0125631 55.6579835 3.011 0.003907
## y.plot:monthssincebaseline -0.0009371 0.0015860 40.7713688 -0.591 0.557874
##
## (Intercept) ***
## y.plot ***
## monthssincebaseline
## baseline_age .
## educ **
## y.plot:monthssincebaseline
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.217
## mnthssncbsl 0.023 0.017
## baseline_ag -0.369 0.583 0.021
## educ -0.779 -0.160 -0.026 -0.281
## y.plt:mnths 0.007 0.075 -0.036 0.006 -0.011
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -540
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.3608 -0.0371 -0.0093 0.0136 3.6318
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.572e-02 0.213813
## monthssincebaseline 8.718e-05 0.009337 0.04
## Residual 3.417e-03 0.058451
## Number of obs: 343, groups: unique_id, 63
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.845893 0.215032 58.059956 -3.934 0.000226 ***
## y.plot -0.113123 0.034135 58.336671 -3.314 0.001584 **
## monthssincebaseline 0.001418 0.001525 40.156910 0.930 0.358141
## baseline_age -0.004231 0.002918 58.141757 -1.450 0.152387
## educ 0.033182 0.012479 58.127136 2.659 0.010106 *
## y.plot:monthssincebaseline -0.002308 0.001561 40.264744 -1.478 0.147060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.262
## mnthssncbsl 0.003 0.005
## baseline_ag -0.391 0.600 0.005
## educ -0.790 -0.114 -0.007 -0.241
## y.plt:mnths 0.002 -0.003 -0.046 -0.002 -0.001Sample description for model 2 (familial)
Model 2 same as model 1, but in a sample with baseline CDR+NACC-FTLD global scores >= 0.5 ( no zeros). Sample size was too small to look at 1+. Removed condition requiring age<=45. Using original CDR+NACC-FTLD sum of boxes.
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3
## 39 33 24
## [1] "Gentic breakdown"
##
## C9 MAPT
## 46 19
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 1 2
## 21 6 2LME model 2: Mutation pos baseline CDR 0.5+ or greater
Presents graphs first, then a print out of the models. Note the failure to converge/warnings of several models.
Removed condition requiring age<=45. Using original CDR+NACC-FTLD sum of boxes.
## [1] "y.plot = baseline_gonogo"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 26.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2664 -0.0479 -0.0005 0.0728 1.6413
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2619936 0.51185
## monthssincebaseline 0.0008618 0.02936 -0.45
## Residual 0.0135121 0.11624
## Number of obs: 74, groups: unique_id, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.138083 0.607486 12.602706 -3.520 0.003940 **
## y.plot 0.103590 0.115171 12.014413 0.899 0.386090
## monthssincebaseline 0.010141 0.009475 7.679127 1.070 0.316967
## baseline_age 0.031612 0.011529 12.590982 2.742 0.017206 *
## educ 0.032394 0.005837 8.603580 5.550 0.000419 ***
## y.plot:monthssincebaseline 0.005436 0.011268 7.091907 0.482 0.644039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.308
## mnthssncbsl -0.071 -0.020
## baseline_ag -0.957 0.350 0.005
## educ -0.043 -0.104 -0.047 -0.164
## y.plt:mnths -0.030 -0.262 0.012 0.028 -0.005
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 13.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5818 -0.1197 -0.0195 0.1068 1.5924
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.1315387 0.36268
## monthssincebaseline 0.0003762 0.01939 -0.78
## Residual 0.0167830 0.12955
## Number of obs: 58, groups: unique_id, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.419048 0.746065 7.438535 -3.242 0.0131 *
## y.plot -0.057734 0.141364 9.587839 -0.408 0.6919
## monthssincebaseline 0.009048 0.006821 8.258514 1.326 0.2202
## baseline_age 0.015642 0.007997 7.910469 1.956 0.0866 .
## educ 0.100753 0.036766 7.574910 2.740 0.0268 *
## y.plot:monthssincebaseline -0.016290 0.008691 8.451684 -1.874 0.0958 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.149
## mnthssncbsl -0.345 0.127
## baseline_ag -0.546 0.144 0.065
## educ -0.830 0.059 0.256 0.009
## y.plt:mnths -0.070 -0.646 -0.172 0.202 -0.029
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 14.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5657 -0.0708 -0.0021 0.0633 1.8293
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2326814 0.48237
## monthssincebaseline 0.0004349 0.02086 -0.46
## Residual 0.0121876 0.11040
## Number of obs: 83, groups: unique_id, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.916773 0.703820 14.458749 -2.723 0.01611 *
## y.plot 0.071903 0.175989 15.594514 0.409 0.68841
## monthssincebaseline 0.007203 0.006288 10.115109 1.146 0.27837
## baseline_age 0.027302 0.011805 14.376039 2.313 0.03602 *
## educ 0.032175 0.008707 13.055527 3.695 0.00268 **
## y.plot:monthssincebaseline -0.013726 0.005138 8.997698 -2.672 0.02556 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.656
## mnthssncbsl -0.116 0.030
## baseline_ag -0.958 0.541 0.056
## educ -0.563 0.742 0.027 0.355
## y.plt:mnths -0.043 -0.184 0.127 0.015 0.110
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 16.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2798 -0.0260 0.0394 0.1167 1.4651
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2175510 0.4664
## monthssincebaseline 0.0006052 0.0246 -0.99
## Residual 0.0186318 0.1365
## Number of obs: 50, groups: unique_id, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.405299 0.523551 2.952328 -2.684 0.0761 .
## y.plot -0.351193 0.154669 8.097992 -2.271 0.0525 .
## monthssincebaseline 0.010386 0.008529 6.920641 1.218 0.2632
## baseline_age -0.020581 0.006509 1.583353 -3.162 0.1166
## educ 0.146356 0.024440 2.517151 5.988 0.0151 *
## y.plot:monthssincebaseline -0.014877 0.008027 6.394034 -1.853 0.1102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.116
## mnthssncbsl -0.488 -0.179
## baseline_ag -0.560 0.315 0.003
## educ -0.736 -0.026 0.276 -0.067
## y.plt:mnths -0.184 -0.859 0.190 0.145 0.050
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 19.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5234 -0.0530 -0.0055 0.0484 1.7383
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2214142 0.47055
## monthssincebaseline 0.0007709 0.02777 -0.06
## Residual 0.0124671 0.11166
## Number of obs: 80, groups: unique_id, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.258924 0.721032 13.891360 -1.746 0.102876
## y.plot -0.144088 0.130585 13.861228 -1.103 0.288640
## monthssincebaseline 0.011548 0.009157 8.824481 1.261 0.239585
## baseline_age 0.017107 0.013235 13.872231 1.293 0.217287
## educ 0.026346 0.006101 14.009936 4.318 0.000707 ***
## y.plot:monthssincebaseline -0.006741 0.011404 9.381514 -0.591 0.568421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.643
## mnthssncbsl -0.022 -0.002
## baseline_ag -0.971 0.620 0.010
## educ -0.223 0.222 0.003 0.045
## y.plt:mnths -0.013 -0.054 0.236 0.008 0.018
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 16.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5834 -0.0534 0.0009 0.0796 1.8051
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2162493 0.46503
## monthssincebaseline 0.0005228 0.02286 -0.21
## Residual 0.0121974 0.11044
## Number of obs: 83, groups: unique_id, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.338274 0.791248 14.657175 -1.691 0.11192
## y.plot -0.112156 0.155981 15.072272 -0.719 0.48312
## monthssincebaseline 0.008899 0.007100 9.719164 1.253 0.23939
## baseline_age 0.018881 0.013804 14.565561 1.368 0.19212
## educ 0.025564 0.007193 14.618616 3.554 0.00299 **
## y.plot:monthssincebaseline -0.012526 0.006625 9.678272 -1.891 0.08895 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.739
## mnthssncbsl -0.064 0.030
## baseline_ag -0.974 0.684 0.038
## educ -0.498 0.588 0.019 0.335
## y.plt:mnths -0.030 -0.069 0.136 0.015 0.084
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 13.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5763 -0.0596 0.0039 0.0767 1.7863
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2210089 0.47012
## monthssincebaseline 0.0004016 0.02004 -0.33
## Residual 0.0122362 0.11062
## Number of obs: 83, groups: unique_id, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.367152 0.730505 14.845160 -1.872 0.081112 .
## y.plot -0.094869 0.146366 15.328260 -0.648 0.526470
## monthssincebaseline 0.008026 0.006192 9.343472 1.296 0.226002
## baseline_age 0.018579 0.012957 14.698522 1.434 0.172534
## educ 0.027715 0.006557 12.988324 4.227 0.000991 ***
## y.plot:monthssincebaseline -0.016176 0.006120 9.417826 -2.643 0.025808 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.682
## mnthssncbsl -0.100 0.044
## baseline_ag -0.971 0.631 0.059
## educ -0.414 0.486 0.016 0.240
## y.plt:mnths -0.070 -0.109 0.124 0.051 0.128LME models (Project 2: Sporadic)
Sample description for model 1 (Sporadic)
LME model 1: Mutation- baseline CDR <=0.5
Presents graphs first, then a print out of the models. Note the failure to converge/warnings of several models.
Removed age 45 restriction. using OG FTLD CDR
## [1] "y.plot = baseline_gonogo"## boundary (singular) fit: see help('isSingular')## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -195.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1367 -0.0633 -0.0145 0.0496 4.2753
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 7.807e-02 0.279403
## monthssincebaseline 1.202e-06 0.001096 1.00
## Residual 1.180e-02 0.108617
## Number of obs: 239, groups: unique_id, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.329e-01 3.214e-01 4.019e+01 -1.658 0.1051
## y.plot -9.175e-02 5.994e-02 3.616e+01 -1.531 0.1345
## monthssincebaseline 6.802e-04 5.481e-04 1.004e+02 1.241 0.2175
## baseline_age 8.182e-03 3.747e-03 3.916e+01 2.183 0.0351 *
## educ -2.082e-02 1.367e-02 4.037e+01 -1.523 0.1355
## y.plot:monthssincebaseline -2.419e-04 4.328e-04 3.662e+01 -0.559 0.5797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.214
## mnthssncbsl 0.057 -0.046
## baseline_ag -0.688 0.377 -0.008
## educ -0.764 -0.057 -0.030 0.080
## y.plt:mnths 0.011 0.398 0.324 0.075 -0.098
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## [1] "y.plot = baseline_strp"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.305282 (tol = 0.002, component 1)
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -874.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2507 -0.0339 0.0009 0.0281 5.3383
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.179e-02 0.2485774
## monthssincebaseline 6.966e-07 0.0008346 0.04
## Residual 2.998e-04 0.0173154
## Number of obs: 237, groups: unique_id, 43
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.6278734 0.2904688 42.2922694 -2.162 0.0364 *
## y.plot -0.0232481 0.0563005 42.3153900 -0.413 0.6817
## monthssincebaseline 0.0002856 0.0002044 29.5490400 1.397 0.1728
## baseline_age 0.0070122 0.0040684 42.3149487 1.724 0.0921 .
## educ -0.0137389 0.0117082 42.2796047 -1.173 0.2472
## y.plot:monthssincebaseline -0.0001846 0.0001981 33.4886884 -0.932 0.3580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.472
## mnthssncbsl -0.003 0.003
## baseline_ag -0.722 0.724 0.005
## educ -0.670 -0.094 0.002 -0.014
## y.plt:mnths -0.004 0.011 -0.016 0.004 0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.305282 (tol = 0.002, component 1)## [1] "y.plot = baseline_flk"## boundary (singular) fit: see help('isSingular')
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -242.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2946 -0.0729 -0.0183 0.0597 4.5455
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.737e-02 0.25955
## monthssincebaseline 1.166e-06 0.00108 1.00
## Residual 1.071e-02 0.10347
## Number of obs: 265, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.717e-01 2.901e-01 4.803e+01 -1.626 0.1106
## y.plot -1.169e-01 4.747e-02 4.518e+01 -2.463 0.0177 *
## monthssincebaseline 4.165e-04 4.899e-04 9.750e+01 0.850 0.3974
## baseline_age 4.383e-03 3.893e-03 4.628e+01 1.126 0.2660
## educ -1.327e-02 1.274e-02 4.862e+01 -1.042 0.3027
## y.plot:monthssincebaseline 1.553e-04 5.346e-04 1.336e+02 0.290 0.7719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.272
## mnthssncbsl -0.031 0.109
## baseline_ag -0.658 0.639 0.130
## educ -0.701 -0.233 -0.042 -0.058
## y.plt:mnths -0.025 0.098 -0.212 -0.019 0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## [1] "y.plot = baseline_nback"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.353007 (tol = 0.002, component 1)
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -772.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0315 -0.0516 0.0018 0.0450 5.0650
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.677e-02 0.2583969
## monthssincebaseline 6.576e-07 0.0008109 0.02
## Residual 3.322e-04 0.0182273
## Number of obs: 212, groups: unique_id, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.6166726 0.3047950 35.7600766 -2.023 0.0506 .
## y.plot -0.0661497 0.0479367 35.7497729 -1.380 0.1762
## monthssincebaseline 0.0003433 0.0002136 23.6524531 1.607 0.1213
## baseline_age 0.0072418 0.0037119 35.7603992 1.951 0.0589 .
## educ -0.0144726 0.0128279 35.7687825 -1.128 0.2667
## y.plot:monthssincebaseline -0.0003489 0.0002061 31.0891987 -1.693 0.1005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.241
## mnthssncbsl 0.000 0.000
## baseline_ag -0.691 0.429 0.000
## educ -0.747 -0.060 -0.001 0.057
## y.plt:mnths 0.000 -0.004 -0.062 0.001 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.353007 (tol = 0.002, component 1)## [1] "y.plot = baseline_humi"## boundary (singular) fit: see help('isSingular')
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -228.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2937 -0.0696 -0.0062 0.0435 4.3660
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.426e-02 0.2535004
## monthssincebaseline 5.720e-07 0.0007563 1.00
## Residual 1.124e-02 0.1060204
## Number of obs: 256, groups: unique_id, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.134e-01 3.050e-01 4.631e+01 -1.356 0.1818
## y.plot -1.448e-01 5.553e-02 4.708e+01 -2.608 0.0122 *
## monthssincebaseline 7.462e-04 4.715e-04 9.196e+01 1.583 0.1169
## baseline_age 3.123e-03 4.331e-03 4.700e+01 0.721 0.4744
## educ -1.081e-02 1.323e-02 4.619e+01 -0.817 0.4181
## y.plot:monthssincebaseline -7.671e-04 5.227e-04 1.072e+02 -1.467 0.1452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.334
## mnthssncbsl 0.023 -0.039
## baseline_ag -0.683 0.704 -0.016
## educ -0.624 -0.311 0.008 -0.130
## y.plt:mnths 0.023 0.030 0.108 -0.043 0.008
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## [1] "y.plot = baseline_test3meanZ"## boundary (singular) fit: see help('isSingular')
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -245.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3751 -0.0624 -0.0146 0.0509 4.4768
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.078e-02 0.2465271
## monthssincebaseline 8.458e-07 0.0009197 1.00
## Residual 1.078e-02 0.1038103
## Number of obs: 265, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.282e-01 2.913e-01 4.602e+01 -1.127 0.26570
## y.plot -1.737e-01 5.526e-02 4.554e+01 -3.143 0.00294 **
## monthssincebaseline 6.517e-04 4.654e-04 9.038e+01 1.400 0.16480
## baseline_age 6.337e-04 4.361e-03 4.564e+01 0.145 0.88511
## educ -8.581e-03 1.264e-02 4.612e+01 -0.679 0.50075
## y.plot:monthssincebaseline -4.310e-04 5.293e-04 1.137e+02 -0.814 0.41716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.377
## mnthssncbsl -0.001 0.006
## baseline_ag -0.684 0.751 0.033
## educ -0.596 -0.324 -0.004 -0.164
## y.plt:mnths 0.017 0.029 0.010 -0.057 0.035
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## [1] "y.plot = baseline_test5meanZ"## boundary (singular) fit: see help('isSingular')
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -271.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5829 -0.0566 -0.0101 0.0273 4.6054
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 7.182e-02 0.2679850
## monthssincebaseline 8.766e-07 0.0009363 1.00
## Residual 1.009e-02 0.1004674
## Number of obs: 282, groups: unique_id, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.394e-01 3.024e-01 5.028e+01 -1.453 0.153
## y.plot -8.497e-02 5.529e-02 4.899e+01 -1.537 0.131
## monthssincebaseline 6.333e-04 4.445e-04 1.122e+02 1.425 0.157
## baseline_age 5.047e-03 4.154e-03 5.002e+01 1.215 0.230
## educ -1.709e-02 1.259e-02 5.041e+01 -1.357 0.181
## y.plot:monthssincebaseline -5.685e-04 5.122e-04 1.544e+02 -1.110 0.269
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.432
## mnthssncbsl -0.014 0.017
## baseline_ag -0.707 0.719 0.051
## educ -0.661 -0.160 0.003 -0.047
## y.plt:mnths 0.000 0.077 -0.016 -0.021 0.018
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Sample description for model 2 (Sporadic)
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3 4 5 6
## 86 72 51 3 4 1
## [1] "Gentic breakdown"
##
## NONE
## 118
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 1
## 28 54LME model 2: Mutation-, baseline CDR 0.5 or greater
Presents graphs first, then a print out of the models. Note the failure to converge/warnings of several models.
Models do not converge. We see a trend for the interaction of Nback and time.
## [1] "y.plot = baseline_gonogo"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 118.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.03520 -0.08189 -0.01522 0.08050 2.74173
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.796840 0.89266
## monthssincebaseline 0.003582 0.05985 0.20
## Residual 0.030254 0.17394
## Number of obs: 155, groups: unique_id, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.010297 1.673995 24.636151 0.006 0.995142
## y.plot -0.496994 0.126818 24.513640 -3.919 0.000627 ***
## monthssincebaseline 0.023245 0.015897 12.294760 1.462 0.168768
## baseline_age -0.005358 0.022834 24.454921 -0.235 0.816448
## educ 0.064297 0.069825 24.037996 0.921 0.366293
## y.plot:monthssincebaseline 0.003969 0.013417 12.648985 0.296 0.772177
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.048
## mnthssncbsl 0.034 0.032
## baseline_ag -0.728 -0.055 -0.013
## educ -0.496 0.038 -0.013 -0.225
## y.plt:mnths -0.028 0.104 0.065 0.026 0.011
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -174.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.35672 -0.04215 -0.00046 0.02026 3.10289
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.4114780 0.64147
## monthssincebaseline 0.0024660 0.04966 0.37
## Residual 0.0009318 0.03053
## Number of obs: 87, groups: unique_id, 13
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.65046 2.19724 8.53566 -1.206 0.26008
## y.plot -0.37128 0.17832 9.05125 -2.082 0.06687 .
## monthssincebaseline 0.01648 0.01663 9.40675 0.990 0.34673
## baseline_age 0.03624 0.02424 7.80132 1.495 0.17415
## educ 0.03046 0.08226 7.17583 0.370 0.72190
## y.plot:monthssincebaseline -0.04771 0.01420 8.60720 -3.359 0.00895 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.182
## mnthssncbsl 0.082 0.132
## baseline_ag -0.795 0.190 -0.028
## educ -0.726 -0.467 -0.056 0.170
## y.plt:mnths -0.001 0.273 0.243 0.041 -0.028
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 137.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10396 -0.08419 -0.00910 0.05452 2.81571
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.856466 0.92545
## monthssincebaseline 0.003269 0.05718 0.27
## Residual 0.028787 0.16967
## Number of obs: 170, groups: unique_id, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.7717371 1.5310082 30.1458272 0.504 0.618
## y.plot -0.7012102 0.1274994 30.8309559 -5.500 5.21e-06
## monthssincebaseline 0.0239861 0.0142070 14.0460928 1.688 0.113
## baseline_age -0.0235200 0.0220285 28.3183106 -1.068 0.295
## educ 0.0961095 0.0661424 27.8531915 1.453 0.157
## y.plot:monthssincebaseline 0.0001947 0.0111136 14.6064651 0.018 0.986
##
## (Intercept)
## y.plot ***
## monthssincebaseline
## baseline_age
## educ
## y.plot:monthssincebaseline
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.021
## mnthssncbsl 0.049 0.060
## baseline_ag -0.713 0.220 -0.017
## educ -0.442 -0.207 -0.020 -0.305
## y.plt:mnths -0.019 0.163 0.126 0.024 0.004
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -172.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.33028 -0.06867 0.00231 0.01858 3.06869
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.3255404 0.57056
## monthssincebaseline 0.0054735 0.07398 0.74
## Residual 0.0009472 0.03078
## Number of obs: 85, groups: unique_id, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.490938 1.812552 6.508624 0.271 0.795
## y.plot -0.112679 0.173405 9.575193 -0.650 0.531
## monthssincebaseline 0.031321 0.023706 10.473968 1.321 0.215
## baseline_age 0.010776 0.020196 5.768623 0.534 0.614
## educ -0.066520 0.053079 6.695627 -1.253 0.252
## y.plot:monthssincebaseline -0.009601 0.022253 10.031777 -0.431 0.675
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.287
## mnthssncbsl 0.116 0.246
## baseline_ag -0.904 -0.256 -0.051
## educ -0.765 -0.161 -0.027 0.431
## y.plt:mnths 0.028 0.696 0.261 0.002 -0.011
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 133.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.11416 -0.08299 -0.00647 0.05428 2.79101
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.829114 0.91056
## monthssincebaseline 0.002996 0.05474 -0.04
## Residual 0.028998 0.17029
## Number of obs: 168, groups: unique_id, 34
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.12340 1.56289 30.09185 -0.719 0.4778
## y.plot -0.58759 0.12567 30.18181 -4.676 5.74e-05 ***
## monthssincebaseline 0.01802 0.01379 13.83295 1.307 0.2125
## baseline_age -0.00408 0.02143 30.00162 -0.190 0.8503
## educ 0.13236 0.06855 29.74841 1.931 0.0631 .
## y.plot:monthssincebaseline -0.01361 0.01421 14.21126 -0.958 0.3542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.101
## mnthssncbsl -0.008 -0.011
## baseline_ag -0.693 0.012 0.001
## educ -0.493 -0.109 0.003 -0.277
## y.plt:mnths 0.004 -0.046 0.039 0.000 -0.006
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 130.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10735 -0.08072 -0.00504 0.06190 2.81223
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.688394 0.82970
## monthssincebaseline 0.003179 0.05638 0.15
## Residual 0.028804 0.16972
## Number of obs: 170, groups: unique_id, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.069449 1.388378 30.313858 0.050 0.9604
## y.plot -0.754524 0.112288 30.650571 -6.720 1.71e-07 ***
## monthssincebaseline 0.020884 0.014144 13.926695 1.477 0.1620
## baseline_age -0.018960 0.019758 29.351903 -0.960 0.3451
## educ 0.117290 0.060540 28.939433 1.937 0.0625 .
## y.plot:monthssincebaseline -0.002823 0.012740 14.570022 -0.222 0.8277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.067
## mnthssncbsl 0.032 0.036
## baseline_ag -0.704 0.158 -0.013
## educ -0.456 -0.241 -0.014 -0.303
## y.plt:mnths -0.005 0.059 0.092 0.003 0.008
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 137.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.11383 -0.08561 -0.00505 0.05974 2.81167
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.891757 0.94433
## monthssincebaseline 0.002991 0.05469 -0.17
## Residual 0.028767 0.16961
## Number of obs: 170, groups: unique_id, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.38762 1.57915 30.84369 -0.245 0.8077
## y.plot -0.67431 0.12610 31.02452 -5.347 7.92e-06 ***
## monthssincebaseline 0.01509 0.01375 13.74393 1.098 0.2912
## baseline_age -0.01302 0.02214 30.29780 -0.588 0.5607
## educ 0.12456 0.06885 29.95399 1.809 0.0805 .
## y.plot:monthssincebaseline -0.01399 0.01212 14.52249 -1.154 0.2669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.139
## mnthssncbsl -0.033 -0.044
## baseline_ag -0.700 0.098 0.009
## educ -0.470 -0.264 0.014 -0.293
## y.plt:mnths 0.015 -0.113 0.095 -0.015 -0.006Sample description for model 3
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3 4 5 6
## 170 142 101 5 5 2
## [1] "Gentic breakdown"
##
## NONE
## 227
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 0 1
## 32 77 54LME model 3: mutation - full sample
## [1] "y.plot = baseline_gonogo"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 65.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1507 -0.0307 -0.0062 0.0148 3.7674
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.575352 0.75852
## monthssincebaseline 0.001546 0.03932 0.34
## Residual 0.016090 0.12685
## Number of obs: 302, groups: unique_id, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.1076558 0.7051567 50.0664822 -1.571 0.123
## y.plot -0.5180017 0.0800176 54.2106384 -6.474 2.9e-08
## monthssincebaseline 0.0105122 0.0068259 29.9227286 1.540 0.134
## baseline_age 0.0180465 0.0079310 52.0263023 2.275 0.027
## educ 0.0067169 0.0322154 48.8533807 0.208 0.836
## y.plot:monthssincebaseline -0.0009868 0.0062595 31.3134875 -0.158 0.876
##
## (Intercept)
## y.plot ***
## monthssincebaseline
## baseline_age *
## educ
## y.plot:monthssincebaseline
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.121
## mnthssncbsl 0.033 0.033
## baseline_ag -0.615 0.270 -0.006
## educ -0.758 -0.040 0.009 -0.027
## y.plt:mnths -0.024 0.194 -0.018 0.014 0.024
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -655.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7915 -0.0102 -0.0005 0.0059 5.0101
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2144721 0.46311
## monthssincebaseline 0.0012368 0.03517 0.63
## Residual 0.0003577 0.01891
## Number of obs: 248, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.539727 0.415854 25.713286 -1.298 0.2059
## y.plot -0.252431 0.078794 44.805329 -3.204 0.0025 **
## monthssincebaseline 0.009957 0.005916 40.902295 1.683 0.1000 .
## baseline_age 0.008857 0.005749 28.577642 1.541 0.1344
## educ -0.017605 0.017232 21.767294 -1.022 0.3182
## y.plot:monthssincebaseline -0.024099 0.005342 41.116043 -4.511 5.3e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.342
## mnthssncbsl 0.021 0.061
## baseline_ag -0.701 0.627 0.068
## educ -0.673 -0.149 0.025 -0.030
## y.plt:mnths 0.024 0.406 -0.001 -0.007 -0.021
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 78.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3219 -0.0293 -0.0065 0.0160 3.9314
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.670053 0.81857
## monthssincebaseline 0.001428 0.03779 0.42
## Residual 0.014802 0.12167
## Number of obs: 339, groups: unique_id, 70
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.951482 0.689767 50.779892 -1.379 0.1738
## y.plot -0.653923 0.083041 66.885142 -7.875 4.09e-11 ***
## monthssincebaseline 0.010982 0.006015 33.085664 1.826 0.0769 .
## baseline_age 0.012768 0.007953 53.510195 1.605 0.1143
## educ 0.018420 0.032221 46.003237 0.572 0.5703
## y.plot:monthssincebaseline -0.004132 0.005125 36.463366 -0.806 0.4254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.142
## mnthssncbsl 0.030 0.060
## baseline_ag -0.589 0.434 0.004
## educ -0.754 -0.139 0.015 -0.068
## y.plt:mnths -0.023 0.241 0.016 0.017 0.026
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: -591
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6424 -0.0131 -0.0008 0.0085 4.8090
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.1657487 0.40712
## monthssincebaseline 0.0018192 0.04265 0.69
## Residual 0.0003875 0.01968
## Number of obs: 221, groups: unique_id, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.373228 0.356899 25.574905 -1.046 0.3055
## y.plot -0.165865 0.069392 39.424402 -2.390 0.0217 *
## monthssincebaseline 0.009231 0.007576 38.120052 1.218 0.2305
## baseline_age 0.007068 0.004459 27.605532 1.585 0.1244
## educ -0.022938 0.014709 24.474129 -1.559 0.1317
## y.plot:monthssincebaseline -0.010118 0.007183 35.397011 -1.409 0.1677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.180
## mnthssncbsl 0.075 0.022
## baseline_ag -0.702 0.359 0.037
## educ -0.719 -0.085 0.019 0.044
## y.plt:mnths 0.002 0.610 -0.025 0.020 -0.019
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 75.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3005 -0.0262 -0.0019 0.0103 3.8609
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.66350 0.81456
## monthssincebaseline 0.00127 0.03564 0.15
## Residual 0.01518 0.12319
## Number of obs: 328, groups: unique_id, 66
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.651188 0.777476 61.223078 -0.838 0.4055
## y.plot -0.773291 0.113523 62.461913 -6.812 4.41e-09 ***
## monthssincebaseline 0.010443 0.006006 32.503649 1.739 0.0915 .
## baseline_age -0.002945 0.010101 61.898757 -0.292 0.7716
## educ 0.057890 0.035479 60.535375 1.632 0.1079
## y.plot:monthssincebaseline -0.010244 0.005826 33.097846 -1.758 0.0879 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.308
## mnthssncbsl 0.011 0.011
## baseline_ag -0.623 0.644 0.003
## educ -0.670 -0.203 -0.001 -0.148
## y.plt:mnths -0.001 0.048 -0.106 -0.012 0.015
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 60.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3352 -0.0296 -0.0053 0.0180 3.9221
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.505135 0.71073
## monthssincebaseline 0.001293 0.03596 0.24
## Residual 0.014805 0.12168
## Number of obs: 339, groups: unique_id, 70
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.373664 0.634305 60.393825 -0.589 0.5580
## y.plot -0.840147 0.083808 65.758141 -10.025 7.12e-15 ***
## monthssincebaseline 0.010208 0.005901 33.400039 1.730 0.0929 .
## baseline_age -0.005122 0.008008 62.125551 -0.640 0.5247
## educ 0.044855 0.029799 57.784046 1.505 0.1377
## y.plot:monthssincebaseline -0.007820 0.005602 35.359141 -1.396 0.1714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.225
## mnthssncbsl 0.011 0.029
## baseline_ag -0.592 0.596 0.010
## educ -0.704 -0.221 0.004 -0.138
## y.plt:mnths -0.003 0.083 -0.066 -0.018 0.023
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_box.computed~y.plot*monthssincebaseline+baseline_age+educ+(1+monthssincebaseline | unique_id)"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: fml.lme
## Data: df.lme.loop
##
## REML criterion at convergence: 67.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4528 -0.0257 -0.0023 0.0099 4.0209
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.698562 0.8358
## monthssincebaseline 0.001156 0.0340 0.14
## Residual 0.014064 0.1186
## Number of obs: 356, groups: unique_id, 73
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.355738 0.727290 67.753549 -0.489 0.6263
## y.plot -0.776376 0.103109 69.010666 -7.530 1.45e-10 ***
## monthssincebaseline 0.009437 0.005486 35.230180 1.720 0.0942 .
## baseline_age -0.002240 0.009211 68.262156 -0.243 0.8086
## educ 0.035257 0.034498 66.629692 1.022 0.3105
## y.plot:monthssincebaseline -0.010434 0.005123 36.562652 -2.037 0.0490 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot -0.256
## mnthssncbsl 0.003 0.017
## baseline_ag -0.589 0.615 0.010
## educ -0.702 -0.205 0.002 -0.146
## y.plt:mnths 0.000 0.054 -0.069 -0.005 0.007
Power analysis
Power anlaysis with Full Sample
Presents change over 1 year. Difference scores were calculated as annualized change ([~1 year score - baseline]/time). First presents a plot with all tests. Then presents a restricted plot showing only sample sizes <1000
## Warning: Using one column matrices in `filter()` was deprecated in dplyr 1.1.0.
## ℹ Please use one dimensional logical vectors instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.
| Task | n.with.test | Z.Decline | CI.low | CI.high | sample_size.25 | sample_size.40 |
|---|---|---|---|---|---|---|
| change_moca | 44 | 0.0104035 | -0.6478557 | 0.6686627 | 1005529 | 392785 |
| change_ftlcdrm_sob | 46 | 0.1713188 | -0.3363173 | 0.6789549 | 2206 | 862 |
| change_strp | 37 | 0.0780440 | -0.0809017 | 0.2369897 | 1042 | 407 |
| change_flk | 45 | -0.1117156 | -0.2102493 | -0.0131818 | 196 | 77 |
| change_nback | 35 | 0.2691843 | -0.1892757 | 0.7276443 | 729 | 285 |
| change_humi | 44 | 0.0788197 | -0.0475274 | 0.2051669 | 646 | 253 |
| change_gonogo | 37 | -27.1431066 | -33.4097271 | -20.8764860 | 14 | 6 |
| change_trailsb | 38 | 3.0008595 | -10.9294388 | 16.9311578 | 5413 | 2115 |
| change_animals | 42 | 0.2456758 | -1.4317130 | 1.9230645 | 11709 | 4574 |
| change_uds3ef | 43 | 0.0257309 | -0.1291771 | 0.1806390 | 9104 | 3556 |
| change_trailsb_ratio | 38 | -0.0039344 | -0.0466109 | 0.0387421 | 29552 | 11544 |
| change_test3meanZ | 45 | -0.0422802 | -0.1904704 | 0.1059100 | 3086 | 1206 |
| change_test5meanZ | 46 | 0.0256067 | -0.1537859 | 0.2049993 | 12327 | 4816 |
Power Familial
Presents change over 1 year. Difference scores were calculated as annualized change ([~1 year score - baseline]/time). First presents a plot with all tests. Then presents a restricted plot showing only sample sizes <1000
## Warning: Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.
| Task | n.with.test | Z.Decline | CI.low | CI.high | sample_size.25 | sample_size.40 |
|---|---|---|---|---|---|---|
| change_moca | 20 | 0.3263958 | -0.2228450 | 0.8756366 | 712 | 278 |
| change_ftlcdrm_sob | 21 | 0.1289899 | -0.4621162 | 0.7200961 | 5275 | 2061 |
| change_strp | 20 | 0.0651410 | -0.0706241 | 0.2009061 | 1091 | 427 |
| change_flk | 21 | -0.1211937 | -0.2148427 | -0.0275448 | 150 | 59 |
| change_nback | 19 | 0.4156407 | -0.0022629 | 0.8335442 | 254 | 100 |
| change_humi | 21 | 0.0700139 | -0.0509873 | 0.1910151 | 751 | 294 |
| change_gonogo | 17 | -27.1124517 | -33.8396853 | -20.3852182 | 16 | 7 |
| change_trailsb | 17 | 1.3883653 | -9.5696159 | 12.3463465 | 15647 | 6112 |
| change_animals | 19 | 0.1046917 | -1.6717601 | 1.8811435 | 72317 | 28249 |
| change_uds3ef | 20 | 0.0185432 | -0.1236633 | 0.1607497 | 14772 | 5771 |
| change_trailsb_ratio | 17 | -0.0075054 | -0.0483390 | 0.0333281 | 7435 | 2904 |
| change_test3meanZ | 21 | -0.0459534 | -0.1516137 | 0.0597068 | 1328 | 519 |
| change_test5meanZ | 21 | 0.0631541 | -0.0634504 | 0.1897587 | 1010 | 395 |
Power Sporadic
Presents change over 1 year. Difference scores were calculated as annualized change ([~1 year score - baseline]/time). First presents a plot with all tests. Then presents a restricted plot showing only sample sizes <1000
## Warning: Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.
| Task | n.with.test | Z.Decline | CI.low | CI.high | sample_size.25 | sample_size.40 |
|---|---|---|---|---|---|---|
| change_moca | 22 | -0.2067335 | -0.8050542 | 0.3915872 | 2104 | 822 |
| change_ftlcdrm_sob | 23 | 0.2165820 | -0.2353659 | 0.6685299 | 1094 | 428 |
| change_strp | 16 | 0.0875933 | -0.1027420 | 0.2779286 | 1186 | 464 |
| change_flk | 22 | -0.1000289 | -0.2080684 | 0.0080107 | 293 | 115 |
| change_nback | 15 | 0.0977832 | -0.3735157 | 0.5690821 | 5835 | 2280 |
| change_humi | 21 | 0.0860915 | -0.0524747 | 0.2246578 | 651 | 255 |
| change_gonogo | 18 | -26.9446090 | -33.2694596 | -20.6197583 | 14 | 6 |
| change_trailsb | 19 | 4.1781463 | -12.7440309 | 21.1003236 | 4121 | 1610 |
| change_animals | 21 | 0.2878007 | -1.3760610 | 1.9516623 | 8395 | 3280 |
| change_uds3ef | 21 | 0.0258267 | -0.1487428 | 0.2003961 | 11476 | 4483 |
| change_trailsb_ratio | 19 | 0.0017167 | -0.0447079 | 0.0481413 | 183676 | 71749 |
| change_test3meanZ | 22 | -0.0482329 | -0.2331190 | 0.1366533 | 3691 | 1442 |
| change_test5meanZ | 23 | -0.0130740 | -0.2319168 | 0.2057689 | 70374 | 27490 |
Power baseline CDR 0.5 and 1
Presents change over 1 year. Difference scores were calculated as annualized change ([~1 year score - baseline]/time). First presents a plot with all tests. Then presents a restricted plot showing only sample sizes <1000
## Warning: Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Use of `df.model$Task` is discouraged.
## ℹ Use `Task` instead.
## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.## Warning: Use of `df.model.rest$Task` is discouraged.
## ℹ Use `Task` instead.
| Task | n.with.test | Z.Decline | CI.low | CI.high | sample_size.25 | sample_size.40 |
|---|---|---|---|---|---|---|
| change_moca | 11 | -0.1676607 | -1.0987822 | 0.7634607 | 7747 | 3026 |
| change_ftlcdrm_sob | 12 | 0.3499182 | -0.1888556 | 0.8886921 | 596 | 233 |
| change_strp | 8 | 0.1796356 | 0.0144110 | 0.3448603 | 213 | 83 |
| change_flk | 12 | -0.0786802 | -0.1741220 | 0.0167617 | 370 | 145 |
| change_nback | 8 | 0.0965442 | -0.5117842 | 0.7048726 | 9972 | 3896 |
| change_humi | 12 | 0.0567625 | -0.0844454 | 0.1979705 | 1555 | 608 |
| change_gonogo | 11 | -26.7714457 | -32.7198005 | -20.8230910 | 13 | 5 |
| change_trailsb | 10 | 9.2536561 | -12.6574901 | 31.1648023 | 1409 | 551 |
| change_animals | 11 | 0.1985917 | -1.6048098 | 2.0019932 | 20712 | 8091 |
| change_uds3ef | 11 | -0.0031339 | -0.2118314 | 0.2055636 | 1113853 | 435099 |
| change_trailsb_ratio | 10 | -0.0100370 | -0.0522874 | 0.0322135 | 4451 | 1739 |
| change_test3meanZ | 12 | -0.0581246 | -0.2767255 | 0.1604762 | 3553 | 1388 |
| change_test5meanZ | 12 | -0.0195084 | -0.2790557 | 0.2400389 | 44458 | 17367 |
Other analyses
Association of cognitive task and change in CDR+NACC FTLD SOB
| Task | n.with.task | beta.store | CI.low | CI.high |
|---|---|---|---|---|
| change_moca | 119 | -0.1948138 | -0.3621116 | -0.0153567 |
| change_strp | 97 | -0.1157481 | -0.3080812 | 0.0856751 |
| change_flk | 120 | 0.0962503 | -0.0844479 | 0.2708195 |
| change_nback | 88 | -0.1863733 | -0.3809456 | 0.0240060 |
| change_humi | 117 | -0.1111952 | -0.2869362 | 0.0717869 |
| change_gonogo | 85 | 0.1055816 | -0.1100182 | 0.3116920 |
| change_animals | 110 | -0.1002516 | -0.2821959 | 0.0886540 |
| change_uds3ef | 80 | -0.3473058 | -0.5268215 | -0.1381294 |
| change_trailsb_ratio | 75 | -0.2461222 | -0.4480555 | -0.0202940 |
| change_test3meanZ | 120 | -0.1051794 | -0.2791579 | 0.0754850 |
| change_test5meanZ | 125 | -0.1621786 | -0.3284324 | 0.0138226 |
Post-hoc association of cognitive task and change in CDR+NACC FTLD SOB
limited to sample with animals, uds3ef, smartphone composites, and moca.
| Task | n.with.task | beta.store | CI.low | CI.high |
|---|---|---|---|---|
| change_moca | 66 | -0.4043314 | -0.5887484 | -0.1799043 |
| change_strp | 52 | 0.0487425 | -0.2271797 | 0.3174205 |
| change_flk | 66 | -0.0688232 | -0.3057628 | 0.1761436 |
| change_nback | 51 | -0.0027108 | -0.2780868 | 0.2730768 |
| change_humi | 64 | -0.1770920 | -0.4052600 | 0.0718447 |
| change_gonogo | 59 | 0.0260349 | -0.2315914 | 0.2802487 |
| change_animals | 66 | 0.0448907 | -0.1993076 | 0.2838395 |
| change_uds3ef | 66 | -0.1472479 | -0.3758846 | 0.0982877 |
| change_trailsb_ratio | 66 | -0.0696584 | -0.3065233 | 0.1753304 |
| change_test3meanZ | 66 | -0.2077374 | -0.4282386 | 0.0361110 |
| change_test5meanZ | 66 | -0.2812354 | -0.4899202 | -0.0420660 |
Association of smartphone composite and change in CDR
## [1] "Raw correlation"##
## Pearson's product-moment correlation
##
## data: df.smartphone.other$baseline_test5meanZ and df.smartphone.other$change_ftlcdr_sob
## t = -2.77, df = 135, p-value = 0.006395
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38466050 -0.06678828
## sample estimates:
## cor
## -0.2319061## [1] "Standardized correlation"##
## Pearson's product-moment correlation
##
## data: df.smartphone.other$change_z and df.smartphone.other$composite5_z
## t = -2.6152, df = 135, p-value = 0.009931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.37354757 -0.05385728
## sample estimates:
## cor
## -0.2195893