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_motor_sob~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: -495.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3458 -0.0584 -0.0137 0.0267 5.9586
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.059563 0.24406
## monthssincebaseline 0.003596 0.05997 -0.80
## Residual 0.002799 0.05290
## Number of obs: 269, groups: unique_id, 54
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.455772 0.033579 46.588618 -13.573 <2e-16 ***
## y.plot -0.061617 0.030386 48.331730 -2.028 0.0481 *
## monthssincebaseline -0.014665 0.009751 36.868620 -1.504 0.1411
## baseline_age 0.001566 0.028666 33.409306 0.055 0.9567
## educ 0.013107 0.028180 35.698911 0.465 0.6447
## y.plot:monthssincebaseline 0.021738 0.008742 38.687492 2.487 0.0173 *
## ---
## 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 0.036
## mnthssncbsl -0.626 -0.013
## baseline_ag 0.024 0.199 -0.021
## educ -0.003 0.049 0.098 -0.247
## y.plt:mnths -0.021 -0.613 0.031 -0.077 0.057
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_motor_sob~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: -535.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5355 -0.0344 -0.0087 0.0105 6.5946
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.059677 0.24429
## monthssincebaseline 0.001284 0.03583 -0.48
## Residual 0.002360 0.04858
## Number of obs: 270, groups: unique_id, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.475450 0.033182 48.540074 -14.329 <2e-16 ***
## y.plot -0.083432 0.044014 46.203196 -1.896 0.0643 .
## monthssincebaseline -0.003347 0.007017 37.098711 -0.477 0.6362
## baseline_age -0.041154 0.041125 39.257752 -1.001 0.3231
## educ 0.035488 0.033234 36.489578 1.068 0.2926
## y.plot:monthssincebaseline 0.005547 0.007420 39.857106 0.748 0.4591
## ---
## 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 0.003
## mnthssncbsl -0.278 -0.037
## baseline_ag 0.067 0.594 -0.060
## educ -0.095 0.130 0.065 -0.123
## y.plt:mnths -0.017 -0.196 -0.143 0.003 0.013
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_motor_sob~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: -547.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4376 -0.0633 -0.0126 0.0426 6.1226
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.055469 0.23552
## monthssincebaseline 0.002425 0.04924 -0.66
## Residual 0.002651 0.05149
## Number of obs: 290, groups: unique_id, 60
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.437611 0.031112 53.481468 -14.065 < 2e-16 ***
## y.plot -0.118901 0.038521 54.343918 -3.087 0.00318 **
## monthssincebaseline -0.014676 0.008333 42.839743 -1.761 0.08535 .
## baseline_age -0.050068 0.038142 41.912512 -1.313 0.19643
## educ 0.039940 0.028925 42.856651 1.381 0.17450
## y.plot:monthssincebaseline 0.027108 0.008484 46.201251 3.195 0.00252 **
## ---
## 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 -0.447 0.010
## baseline_ag -0.071 0.600 -0.042
## educ -0.085 0.003 0.098 -0.213
## y.plt:mnths 0.026 -0.317 -0.121 0.018 0.003
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_motor_sob~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: -504.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4485 -0.0562 -0.0108 0.0314 6.4149
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.051958 0.22794
## monthssincebaseline 0.001419 0.03767 -0.56
## Residual 0.002489 0.04989
## Number of obs: 253, groups: unique_id, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.4657899 0.0326873 43.0082800 -14.250 < 2e-16
## y.plot -0.1117846 0.0332887 44.3256883 -3.358 0.00162
## monthssincebaseline -0.0026380 0.0074968 31.4621550 -0.352 0.72727
## baseline_age -0.0414756 0.0335695 34.5065822 -1.236 0.22498
## educ 0.0517082 0.0319853 32.2529822 1.617 0.11570
## y.plot:monthssincebaseline -0.0009477 0.0083941 43.6950830 -0.113 0.91062
##
## (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 -0.074
## mnthssncbsl -0.343 -0.023
## baseline_ag 0.002 0.351 -0.034
## educ -0.007 -0.164 0.080 -0.330
## y.plt:mnths -0.009 -0.202 -0.209 0.049 -0.038
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_motor_sob~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: -532.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4250 -0.0478 -0.0087 0.0235 6.0670
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.053695 0.23172
## monthssincebaseline 0.003342 0.05781 -0.75
## Residual 0.002690 0.05186
## Number of obs: 284, groups: unique_id, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.454281 0.030825 50.082113 -14.738 < 2e-16 ***
## y.plot -0.098412 0.032761 52.599156 -3.004 0.00407 **
## monthssincebaseline -0.012163 0.009291 38.736970 -1.309 0.19820
## baseline_age -0.035578 0.030658 32.543853 -1.161 0.25429
## educ 0.041416 0.027851 35.668747 1.487 0.14579
## y.plot:monthssincebaseline 0.015764 0.009143 43.189867 1.724 0.09184 .
## ---
## 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 0.006
## mnthssncbsl -0.562 -0.053
## baseline_ag -0.004 0.440 -0.063
## educ -0.040 -0.197 0.103 -0.354
## y.plt:mnths -0.027 -0.518 -0.046 -0.085 0.090
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_motor_sob~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: -544.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4370 -0.0589 -0.0148 0.0280 6.1144
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.054657 0.23379
## monthssincebaseline 0.002785 0.05277 -0.67
## Residual 0.002656 0.05154
## Number of obs: 290, groups: unique_id, 60
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.446629 0.030683 52.898930 -14.556 < 2e-16 ***
## y.plot -0.116770 0.035502 53.278255 -3.289 0.00179 **
## monthssincebaseline -0.011956 0.008755 41.905644 -1.366 0.17934
## baseline_age -0.054454 0.034996 35.223957 -1.556 0.12865
## educ 0.056228 0.028842 40.523443 1.949 0.05818 .
## y.plot:monthssincebaseline 0.020080 0.008805 46.646946 2.281 0.02719 *
## ---
## 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 -0.027
## mnthssncbsl -0.467 -0.044
## baseline_ag -0.014 0.539 -0.067
## educ -0.077 -0.168 0.106 -0.322
## y.plt:mnths -0.014 -0.401 -0.087 -0.057 0.063
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_motor_sob~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: -559.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4724 -0.0527 -0.0119 0.0222 6.1679
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.053420 0.23113
## monthssincebaseline 0.002985 0.05463 -0.71
## Residual 0.002608 0.05106
## Number of obs: 296, groups: unique_id, 62
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.446455 0.029923 54.933748 -14.920 < 2e-16 ***
## y.plot -0.121493 0.035830 57.440114 -3.391 0.00127 **
## monthssincebaseline -0.011206 0.008887 44.810361 -1.261 0.21384
## baseline_age -0.054314 0.033929 41.265263 -1.601 0.11705
## educ 0.047712 0.027472 41.361304 1.737 0.08987 .
## y.plot:monthssincebaseline 0.016293 0.009256 51.309152 1.760 0.08434 .
## ---
## 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 -0.059
## mnthssncbsl -0.499 -0.044
## baseline_ag -0.006 0.547 -0.101
## educ -0.089 -0.133 0.120 -0.301
## y.plt:mnths 0.005 -0.376 -0.128 0.013 0.035Sample 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
## 60 22 12
## [1] "Gentic breakdown"
##
## C9 MAPT
## 27 10
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 1 2
## 13 3 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.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0694314 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00200828 (tol = 0.002, component 1)
## boundary (singular) fit: see help('isSingular')
## [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: -47.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1625 -0.0644 -0.0069 0.0489 3.6455
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2722147 0.52174
## monthssincebaseline 0.0007231 0.02689 0.32
## Residual 0.0028577 0.05346
## Number of obs: 68, groups: unique_id, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.856288 0.663425 11.799764 -2.798 0.01633 *
## y.plot 0.109826 0.118568 11.097699 0.926 0.37401
## monthssincebaseline 0.012016 0.009675 6.086990 1.242 0.25998
## baseline_age 0.030392 0.012503 11.873277 2.431 0.03188 *
## educ 0.021796 0.006282 9.041001 3.469 0.00701 **
## y.plot:monthssincebaseline 0.002573 0.010439 6.251182 0.246 0.81320
## ---
## 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 -0.312
## mnthssncbsl 0.035 0.016
## baseline_ag -0.959 0.359 -0.006
## educ -0.053 -0.115 0.058 -0.155
## y.plt:mnths 0.016 0.127 0.091 -0.015 0.013
## [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: -53.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8698 -0.0360 -0.0032 0.0989 3.3623
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.461e-01 0.382253
## monthssincebaseline 1.004e-05 0.003168 0.47
## Residual 3.518e-03 0.059311
## Number of obs: 52, groups: unique_id, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.4784463 1.0253754 7.3432153 -2.417 0.0447 *
## y.plot -0.1360304 0.1645331 7.5076875 -0.827 0.4339
## monthssincebaseline -0.0006591 0.0021448 7.5716935 -0.307 0.7669
## baseline_age 0.0156757 0.0105578 7.3449217 1.485 0.1792
## educ 0.1050905 0.0494203 7.6063929 2.126 0.0679 .
## y.plot:monthssincebaseline 0.0022546 0.0030057 11.4258145 0.750 0.4684
## ---
## 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 -0.240
## mnthssncbsl 0.078 -0.065
## baseline_ag -0.574 0.139 0.038
## educ -0.845 0.160 -0.092 0.064
## y.plt:mnths 0.071 0.153 -0.634 -0.148 -0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0694314 (tol = 0.002, component 1)
##
## [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: -71.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2866 -0.0685 0.0014 0.0469 3.8667
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.246943 0.49693
## monthssincebaseline 0.000169 0.01300 0.20
## Residual 0.002546 0.05046
## Number of obs: 77, groups: unique_id, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.722608 0.854442 13.992577 -2.016 0.06341 .
## y.plot -0.053042 0.206789 14.059055 -0.257 0.80128
## monthssincebaseline 0.005595 0.004419 7.223826 1.266 0.24478
## baseline_age 0.027014 0.013922 13.974492 1.940 0.07279 .
## educ 0.023265 0.010388 13.830954 2.240 0.04209 *
## y.plot:monthssincebaseline -0.013964 0.003279 6.942927 -4.258 0.00382 **
## ---
## 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 -0.728
## mnthssncbsl 0.040 -0.012
## baseline_ag -0.963 0.613 -0.026
## educ -0.630 0.802 -0.005 0.438
## y.plt:mnths 0.036 0.034 0.159 -0.023 -0.063
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00200828 (tol = 0.002, component 1)
##
## [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: -51.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8329 -0.1091 -0.0189 0.0846 3.3773
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.228e-01 0.350399
## monthssincebaseline 2.584e-05 0.005083 -1.00
## Residual 3.546e-03 0.059547
## Number of obs: 49, groups: unique_id, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.964599 0.534447 6.378716 -1.805 0.1182
## y.plot -0.355772 0.119401 7.249678 -2.980 0.0197 *
## monthssincebaseline 0.001890 0.002259 6.943181 0.837 0.4305
## baseline_age -0.009185 0.004059 3.744655 -2.263 0.0910 .
## educ 0.086830 0.035316 7.326733 2.459 0.0421 *
## y.plot:monthssincebaseline -0.003589 0.002256 7.923605 -1.591 0.1506
## ---
## 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 -0.019
## mnthssncbsl -0.537 -0.071
## baseline_ag 0.118 0.163 -0.196
## educ -0.918 -0.022 0.420 -0.456
## y.plt:mnths 0.152 -0.763 -0.286 0.040 -0.166
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
##
## [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: -63.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2615 -0.0773 -0.0098 0.0371 3.8070
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2452005 0.49518
## monthssincebaseline 0.0004693 0.02166 0.44
## Residual 0.0026178 0.05116
## Number of obs: 74, groups: unique_id, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.059679 0.706102 10.331596 -1.501 0.1633
## y.plot -0.196187 0.133419 12.985299 -1.470 0.1653
## monthssincebaseline 0.008115 0.007515 6.883063 1.080 0.3166
## baseline_age 0.015855 0.013003 10.445411 1.219 0.2495
## educ 0.019334 0.005879 9.219041 3.289 0.0091 **
## y.plot:monthssincebaseline -0.013383 0.008363 7.173937 -1.600 0.1525
## ---
## 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 -0.586
## mnthssncbsl 0.105 -0.034
## baseline_ag -0.969 0.572 -0.059
## educ -0.202 0.207 0.006 0.026
## y.plt:mnths 0.018 0.149 0.273 0.010 -0.154
## [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: -70.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2926 -0.0626 -0.0108 0.0425 3.8526
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2354120 0.48519
## monthssincebaseline 0.0002491 0.01578 0.46
## Residual 0.0025529 0.05053
## Number of obs: 77, groups: unique_id, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.970038 0.790646 11.334285 -1.227 0.2447
## y.plot -0.233796 0.159074 13.810506 -1.470 0.1640
## monthssincebaseline 0.007215 0.005098 7.501805 1.415 0.1971
## baseline_age 0.015627 0.013793 11.393805 1.133 0.2805
## educ 0.015889 0.007071 11.451416 2.247 0.0452 *
## y.plot:monthssincebaseline -0.014169 0.004415 7.728243 -3.209 0.0130 *
## ---
## 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 -0.713
## mnthssncbsl 0.106 -0.039
## baseline_ag -0.973 0.660 -0.065
## educ -0.487 0.578 -0.005 0.325
## y.plt:mnths 0.038 0.142 0.139 -0.007 -0.167
## [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: -69.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2879 -0.0476 -0.0042 0.0347 3.8554
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.227633 0.47711
## monthssincebaseline 0.000300 0.01732 0.46
## Residual 0.002554 0.05054
## Number of obs: 77, groups: unique_id, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.958660 0.719049 12.497298 -1.333 0.2062
## y.plot -0.256772 0.148308 14.082230 -1.731 0.1052
## monthssincebaseline 0.009128 0.005509 7.574427 1.657 0.1383
## baseline_age 0.015763 0.012608 12.498419 1.250 0.2341
## educ 0.015844 0.006516 10.779157 2.432 0.0337 *
## y.plot:monthssincebaseline -0.013731 0.005117 7.866894 -2.684 0.0282 *
## ---
## 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 -0.650
## mnthssncbsl 0.086 -0.027
## baseline_ag -0.968 0.587 -0.046
## educ -0.413 0.503 0.036 0.232
## y.plt:mnths 0.055 0.158 0.030 -0.028 -0.164LME 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"
## [1] "ftldcdr_motor_sob~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: -226.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5658 -0.0374 -0.0050 0.0274 5.8106
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.149399 0.38652
## monthssincebaseline 0.009108 0.09544 0.57
## Residual 0.006251 0.07906
## Number of obs: 217, groups: unique_id, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.32807 0.06176 37.33425 -5.312 5.23e-06 ***
## y.plot -0.14676 0.07809 38.05675 -1.880 0.0678 .
## monthssincebaseline 0.02692 0.01944 30.28371 1.385 0.1762
## baseline_age 0.09852 0.05617 35.27097 1.754 0.0881 .
## educ -0.06004 0.04529 31.43146 -1.326 0.1945
## y.plot:monthssincebaseline -0.00848 0.02062 26.85583 -0.411 0.6842
## ---
## 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 -0.079
## mnthssncbsl 0.518 -0.036
## baseline_ag 0.046 0.318 0.014
## educ -0.004 -0.076 0.015 0.052
## y.plt:mnths -0.044 0.515 -0.048 0.036 -0.007
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_motor_sob~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: -797.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3508 -0.0333 0.0004 0.0309 5.7280
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.1224904 0.34999
## monthssincebaseline 0.0002896 0.01702 0.14
## Residual 0.0002570 0.01603
## Number of obs: 223, groups: unique_id, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.388323 0.054960 40.100534 -7.066 1.5e-08 ***
## y.plot -0.057666 0.078474 40.288120 -0.735 0.467
## monthssincebaseline 0.005254 0.003619 29.663191 1.452 0.157
## baseline_age 0.082699 0.071341 40.016606 1.159 0.253
## educ -0.054245 0.045674 39.704141 -1.188 0.242
## y.plot:monthssincebaseline -0.001288 0.003486 30.667681 -0.370 0.714
## ---
## 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 0.171
## mnthssncbsl 0.122 0.011
## baseline_ag 0.273 0.714 0.019
## educ -0.036 -0.103 0.006 -0.027
## y.plt:mnths -0.002 0.086 -0.001 0.002 0.001
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_motor_sob~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: -282.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7841 -0.0738 0.0038 0.0518 6.0735
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.129633 0.36005
## monthssincebaseline 0.008924 0.09447 0.78
## Residual 0.005692 0.07544
## Number of obs: 240, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.34668 0.05452 41.23504 -6.359 1.3e-07 ***
## y.plot -0.29770 0.08174 51.74463 -3.642 0.000626 ***
## monthssincebaseline 0.01543 0.01798 37.18291 0.858 0.396249
## baseline_age -0.03069 0.05170 41.62848 -0.594 0.555973
## educ -0.01894 0.03518 30.62777 -0.538 0.594329
## y.plot:monthssincebaseline 0.01184 0.02288 42.18854 0.517 0.607523
## ---
## 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 -0.091
## mnthssncbsl 0.683 -0.053
## baseline_ag 0.101 0.529 0.110
## educ -0.006 -0.189 -0.001 -0.090
## y.plt:mnths -0.132 0.540 -0.237 -0.030 0.054
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_motor_sob~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: -705.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1435 -0.0362 0.0010 0.0365 5.4469
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.1335158 0.36540
## monthssincebaseline 0.0002945 0.01716 0.08
## Residual 0.0002839 0.01685
## Number of obs: 199, groups: unique_id, 37
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.370265 0.060520 33.014854 -6.118 6.81e-07 ***
## y.plot -0.115930 0.068874 33.034804 -1.683 0.102
## monthssincebaseline 0.006183 0.003893 25.433886 1.588 0.125
## baseline_age 0.088382 0.066092 33.000037 1.337 0.190
## educ -0.057602 0.050469 32.974870 -1.141 0.262
## y.plot:monthssincebaseline -0.004953 0.003699 27.787697 -1.339 0.191
## ---
## 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 0.038
## mnthssncbsl 0.076 -0.001
## baseline_ag 0.109 0.424 0.004
## educ -0.030 -0.065 0.001 0.043
## y.plt:mnths -0.003 0.067 -0.065 0.002 -0.001
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_motor_sob~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: -259.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6779 -0.0362 -0.0035 0.0140 5.9883
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.12214 0.34949
## monthssincebaseline 0.00763 0.08735 0.49
## Residual 0.00589 0.07674
## Number of obs: 231, groups: unique_id, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.31244 0.05509 40.04068 -5.672 1.36e-06 ***
## y.plot -0.22326 0.07157 45.31946 -3.120 0.00315 **
## monthssincebaseline 0.02919 0.01783 31.64744 1.637 0.11161
## baseline_age 0.01606 0.06434 39.56063 0.250 0.80412
## educ -0.02646 0.04482 36.95800 -0.590 0.55863
## y.plot:monthssincebaseline -0.02674 0.01777 36.37102 -1.505 0.14104
## ---
## 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 -0.194
## mnthssncbsl 0.453 -0.061
## baseline_ag -0.037 0.628 0.015
## educ 0.041 -0.296 0.009 -0.129
## y.plt:mnths -0.089 0.337 -0.185 0.005 0.008
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_motor_sob~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: -281
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7487 -0.0349 -0.0078 0.0263 6.0747
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.107704 0.32818
## monthssincebaseline 0.007916 0.08897 0.59
## Residual 0.005713 0.07558
## Number of obs: 240, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.31652 0.05041 42.54800 -6.279 1.5e-07 ***
## y.plot -0.30326 0.07424 50.65226 -4.085 0.000157 ***
## monthssincebaseline 0.02621 0.01766 33.84413 1.484 0.147046
## baseline_age -0.04027 0.05987 41.45043 -0.673 0.504931
## educ -0.01242 0.03933 36.66855 -0.316 0.753988
## y.plot:monthssincebaseline -0.01806 0.01910 39.79778 -0.946 0.349946
## ---
## 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 -0.165
## mnthssncbsl 0.537 -0.067
## baseline_ag 0.016 0.671 0.036
## educ 0.030 -0.297 0.012 -0.165
## y.plt:mnths -0.118 0.378 -0.215 0.002 0.024
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_motor_sob~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: -308.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8699 -0.0318 -0.0094 0.0199 6.3030
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.126167 0.35520
## monthssincebaseline 0.007438 0.08625 0.56
## Residual 0.005318 0.07292
## Number of obs: 257, groups: unique_id, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.35227 0.05216 46.95838 -6.754 1.94e-08 ***
## y.plot -0.17399 0.07165 52.82019 -2.428 0.0186 *
## monthssincebaseline 0.02376 0.01638 36.92836 1.451 0.1552
## baseline_age 0.01931 0.06011 46.24075 0.321 0.7495
## educ -0.04362 0.04020 41.91298 -1.085 0.2841
## y.plot:monthssincebaseline -0.01368 0.01630 39.82355 -0.839 0.4063
## ---
## 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 -0.113
## mnthssncbsl 0.508 -0.034
## baseline_ag 0.047 0.660 0.049
## educ 0.022 -0.152 0.017 -0.057
## y.plt:mnths -0.087 0.389 -0.147 0.022 0.004
Sample description for model 2 (Sporadic)
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3
## 121 48 27
## [1] "Gentic breakdown"
##
## NONE
## 77
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 1
## 21 34LME 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_motor_sob~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: 84.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.66738 -0.17845 -0.01928 0.07065 3.06938
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.4478 1.2033
## monthssincebaseline 0.5526 0.7433 0.62
## Residual 0.0225 0.1500
## Number of obs: 131, groups: unique_id, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.197991 0.256641 23.053700 4.668 0.000106 ***
## y.plot -0.526604 0.198041 25.157334 -2.659 0.013434 *
## monthssincebaseline 0.340961 0.196116 13.070879 1.739 0.105594
## baseline_age -0.002775 0.170673 23.295617 -0.016 0.987166
## educ 0.127931 0.184134 23.020395 0.695 0.494154
## y.plot:monthssincebaseline 0.044719 0.169116 13.161065 0.264 0.795546
## ---
## 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 0.168
## mnthssncbsl 0.663 0.053
## baseline_ag -0.082 -0.016 -0.005
## educ 0.054 0.037 -0.009 -0.156
## y.plt:mnths 0.047 0.710 0.051 0.030 0.013
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_motor_sob~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: -130.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25033 -0.02995 -0.00067 0.02890 2.89681
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.8937961 0.94541
## monthssincebaseline 0.3849604 0.62045 0.61
## Residual 0.0009838 0.03137
## Number of obs: 73, groups: unique_id, 13
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.5478 0.2944 7.1244 1.861 0.1043
## y.plot -0.8879 0.2719 8.8163 -3.266 0.0100 *
## monthssincebaseline 0.2035 0.2063 9.3748 0.986 0.3487
## baseline_age 0.2808 0.1969 8.9609 1.426 0.1877
## educ 0.1030 0.2425 8.8847 0.425 0.6811
## y.plot:monthssincebaseline -0.5876 0.1848 8.9933 -3.180 0.0112 *
## ---
## 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 0.232
## mnthssncbsl 0.618 0.144
## baseline_ag 0.103 0.176 -0.012
## educ 0.165 -0.400 -0.023 0.147
## y.plt:mnths 0.141 0.593 0.202 0.018 -0.012
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_motor_sob~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: 98.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.70424 -0.09427 -0.00753 0.04311 3.13716
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.49854 1.2241
## monthssincebaseline 0.53827 0.7337 0.65
## Residual 0.02158 0.1469
## Number of obs: 142, groups: unique_id, 33
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.475969 0.240011 18.713812 6.150 7e-06 ***
## y.plot -0.814513 0.192027 19.961670 -4.242 0.000401 ***
## monthssincebaseline 0.369714 0.185195 14.074439 1.996 0.065617 .
## baseline_age -0.093692 0.154640 26.370101 -0.606 0.549781
## educ 0.244531 0.167127 26.049286 1.463 0.155391
## y.plot:monthssincebaseline 0.001298 0.148129 14.117059 0.009 0.993129
## ---
## 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 0.154
## mnthssncbsl 0.696 0.056
## baseline_ag -0.114 0.149 -0.016
## educ 0.004 -0.167 -0.016 -0.260
## y.plt:mnths 0.054 0.694 0.045 0.021 0.000
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_motor_sob~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: -128.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.24355 -0.02684 0.00310 0.02301 2.87983
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.3930192 1.18026
## monthssincebaseline 0.8381586 0.91551 0.90
## Residual 0.0009931 0.03151
## Number of obs: 71, groups: unique_id, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.51663 0.36611 10.64792 1.411 0.187
## y.plot -0.27369 0.34580 10.23548 -0.791 0.447
## monthssincebaseline 0.35663 0.29050 10.51255 1.228 0.246
## baseline_age 0.13860 0.17599 7.81340 0.788 0.454
## educ -0.19714 0.16346 8.43017 -1.206 0.261
## y.plot:monthssincebaseline -0.06945 0.26993 9.88808 -0.257 0.802
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.201
## mnthssncbsl 0.874 0.182
## baseline_ag 0.085 -0.162 -0.040
## educ 0.189 -0.107 -0.012 0.408
## y.plt:mnths 0.177 0.882 0.169 0.002 -0.013
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_motor_sob~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: 96.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.69134 -0.14588 -0.00498 0.05243 3.11567
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.12828 1.0622
## monthssincebaseline 0.49013 0.7001 0.42
## Residual 0.02178 0.1476
## Number of obs: 140, groups: unique_id, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.31199 0.21666 22.56571 6.056 3.85e-06 ***
## y.plot -0.81351 0.18728 26.62203 -4.344 0.000182 ***
## monthssincebaseline 0.28911 0.18017 13.72332 1.605 0.131316
## baseline_age 0.04665 0.15485 28.01287 0.301 0.765434
## educ 0.32273 0.17683 27.82056 1.825 0.078753 .
## y.plot:monthssincebaseline -0.19589 0.18322 14.06151 -1.069 0.303007
## ---
## 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 0.134
## mnthssncbsl 0.555 -0.017
## baseline_ag -0.149 -0.009 0.004
## educ -0.017 -0.106 0.005 -0.226
## y.plt:mnths -0.013 0.682 -0.008 0.003 -0.011
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_motor_sob~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: 91.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.68820 -0.09009 -0.00607 0.05254 3.13471
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.09391 1.0459
## monthssincebaseline 0.51933 0.7206 0.59
## Residual 0.02159 0.1469
## Number of obs: 142, groups: unique_id, 33
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.37097 0.21265 12.61656 6.447 2.51e-05 ***
## y.plot -0.90712 0.17685 16.74547 -5.129 8.77e-05 ***
## monthssincebaseline 0.31958 0.18480 13.92029 1.729 0.1059
## baseline_age -0.05873 0.13729 28.09508 -0.428 0.6721
## educ 0.30395 0.15131 27.89389 2.009 0.0543 .
## y.plot:monthssincebaseline -0.05218 0.16841 14.15050 -0.310 0.7612
## ---
## 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 0.678 0.020
## baseline_ag -0.118 0.087 -0.008
## educ -0.006 -0.180 -0.009 -0.255
## y.plt:mnths 0.018 0.730 0.012 0.001 0.003
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_motor_sob~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: 98.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.68238 -0.13004 -0.00464 0.05062 3.14269
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.01904 1.0095
## monthssincebaseline 0.49995 0.7071 0.31
## Residual 0.02156 0.1468
## Number of obs: 142, groups: unique_id, 33
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.33471 0.20606 18.13800 6.477 4.15e-06 ***
## y.plot -0.91607 0.16853 22.31491 -5.436 1.76e-05 ***
## monthssincebaseline 0.24574 0.18048 13.66441 1.362 0.1953
## baseline_age -0.01576 0.15548 27.92701 -0.101 0.9200
## educ 0.32084 0.17365 27.54705 1.848 0.0754 .
## y.plot:monthssincebaseline -0.18980 0.15927 14.13615 -1.192 0.2530
## ---
## 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 0.161
## mnthssncbsl 0.501 -0.004
## baseline_ag -0.139 0.041 0.012
## educ -0.002 -0.240 0.014 -0.238
## y.plt:mnths 0.003 0.592 0.046 -0.017 -0.007Sample description for model 3
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3
## 226 103 60
## [1] "Gentic breakdown"
##
## NONE
## 154
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 0 1
## 23 58 34LME model 3: mutation - full sample
## [1] "y.plot = baseline_gonogo"
## [1] "ftldcdr_motor_sob~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
## -2.3986 -0.0300 -0.0049 0.0103 4.4073
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.04015 1.0199
## monthssincebaseline 0.24331 0.4933 0.66
## Residual 0.01095 0.1046
## Number of obs: 275, groups: unique_id, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.264821 0.142234 51.486798 1.862 0.0683 .
## y.plot -0.608340 0.117261 59.852912 -5.188 2.66e-06 ***
## monthssincebaseline 0.153494 0.083788 33.122423 1.832 0.0760 .
## baseline_age 0.231958 0.107217 51.101941 2.163 0.0352 *
## educ 0.003638 0.094150 48.611887 0.039 0.9693
## y.plot:monthssincebaseline -0.025269 0.078765 33.894615 -0.321 0.7503
## ---
## 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 0.100
## mnthssncbsl 0.667 0.012
## baseline_ag 0.049 0.196 0.004
## educ -0.015 -0.019 0.014 -0.008
## y.plt:mnths 0.011 0.687 -0.025 0.016 0.025
## [1] "y.plot = baseline_strp"
## [1] "ftldcdr_motor_sob~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: -587.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9189 -0.0066 -0.0009 0.0033 5.0517
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.5497224 0.7414
## monthssincebaseline 0.1716530 0.4143 0.74
## Residual 0.0003239 0.0180
## Number of obs: 234, groups: unique_id, 48
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.16951 0.11707 42.67815 -1.448 0.154922
## y.plot -0.55712 0.12163 55.65701 -4.580 2.66e-05 ***
## monthssincebaseline 0.10351 0.07031 42.36994 1.472 0.148393
## baseline_age 0.07713 0.09159 38.33308 0.842 0.404962
## educ -0.05902 0.06311 33.23580 -0.935 0.356438
## y.plot:monthssincebaseline -0.27358 0.06597 42.62510 -4.147 0.000157 ***
## ---
## 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 0.202
## mnthssncbsl 0.720 0.035
## baseline_ag 0.321 0.523 0.047
## educ 0.009 -0.145 0.016 -0.041
## y.plt:mnths 0.011 0.628 -0.002 -0.006 -0.015
## [1] "y.plot = baseline_flk"
## [1] "ftldcdr_motor_sob~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: 18.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5141 -0.0269 -0.0047 0.0166 4.6163
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.174830 1.08390
## monthssincebaseline 0.231576 0.48122 0.69
## Residual 0.009989 0.09995
## Number of obs: 309, groups: unique_id, 69
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.40579 0.13737 50.84732 2.954 0.00474 **
## y.plot -0.84052 0.11979 63.76576 -7.017 1.77e-09 ***
## monthssincebaseline 0.17034 0.07607 35.58424 2.239 0.03148 *
## baseline_age 0.17777 0.10494 51.78911 1.694 0.09628 .
## educ 0.05880 0.09296 45.21568 0.633 0.53023
## y.plot:monthssincebaseline -0.06871 0.06857 37.71170 -1.002 0.32271
## ---
## 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 0.113
## mnthssncbsl 0.678 0.005
## baseline_ag 0.081 0.325 0.007
## educ -0.037 -0.115 0.020 -0.061
## y.plt:mnths 0.005 0.653 -0.054 0.014 0.018
## [1] "y.plot = baseline_nback"
## [1] "ftldcdr_motor_sob~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: -530.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7570 -0.0078 -0.0011 0.0043 4.8400
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.6219814 0.78866
## monthssincebaseline 0.2658625 0.51562 0.86
## Residual 0.0003527 0.01878
## Number of obs: 208, groups: unique_id, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.18010 0.13043 39.86886 -1.381 0.1750
## y.plot -0.30456 0.13441 40.75283 -2.266 0.0288 *
## monthssincebaseline 0.10622 0.09138 38.26754 1.162 0.2523
## baseline_age 0.08554 0.07189 32.51993 1.190 0.2427
## educ -0.08417 0.05321 30.03943 -1.582 0.1242
## y.plot:monthssincebaseline -0.10635 0.08937 35.96474 -1.190 0.2419
## ---
## 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 0.056
## mnthssncbsl 0.852 0.009
## baseline_ag 0.159 0.251 0.032
## educ 0.030 -0.069 0.017 0.043
## y.plt:mnths 0.004 0.833 -0.014 0.013 -0.017
## [1] "y.plot = baseline_humi"
## [1] "ftldcdr_motor_sob~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: 19.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4565 -0.0219 -0.0024 0.0119 4.5401
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.97698 0.9884
## monthssincebaseline 0.20059 0.4479 0.47
## Residual 0.01028 0.1014
## Number of obs: 298, groups: unique_id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.45156 0.12915 58.23604 3.496 0.000909 ***
## y.plot -0.98285 0.13685 74.52211 -7.182 4.35e-10 ***
## monthssincebaseline 0.16240 0.07549 34.72560 2.151 0.038502 *
## baseline_age -0.02290 0.13406 60.83424 -0.171 0.864952
## educ 0.16145 0.10446 60.09098 1.546 0.127476
## y.plot:monthssincebaseline -0.15393 0.07347 35.06847 -2.095 0.043451 *
## ---
## 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 0.014
## mnthssncbsl 0.534 -0.063
## baseline_ag 0.024 0.539 0.005
## educ -0.058 -0.182 0.000 -0.135
## y.plt:mnths -0.072 0.468 -0.164 -0.010 0.012
## [1] "y.plot = baseline_test3meanZ"
## [1] "ftldcdr_motor_sob~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: -0.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4827 -0.0231 -0.0048 0.0156 4.6117
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.792443 0.89019
## monthssincebaseline 0.204795 0.45254 0.57
## Residual 0.009989 0.09994
## Number of obs: 309, groups: unique_id, 69
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.40145 0.11511 48.90535 3.487 0.00104 **
## y.plot -1.06855 0.11161 76.10078 -9.574 1.06e-14 ***
## monthssincebaseline 0.15822 0.07447 35.83424 2.125 0.04059 *
## baseline_age -0.05045 0.10432 61.93277 -0.484 0.63039
## educ 0.13609 0.08586 59.22842 1.585 0.11828
## y.plot:monthssincebaseline -0.12446 0.07324 36.95099 -1.699 0.09766 .
## ---
## 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 0.069
## mnthssncbsl 0.619 -0.057
## baseline_ag 0.098 0.457 0.012
## educ -0.052 -0.185 0.006 -0.129
## y.plt:mnths -0.063 0.582 -0.138 -0.014 0.016
## [1] "y.plot = baseline_test5meanZ"
## [1] "ftldcdr_motor_sob~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: 4.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5505 -0.0203 -0.0038 0.0088 4.7509
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.996478 0.99824
## monthssincebaseline 0.185354 0.43053 0.47
## Residual 0.009424 0.09708
## Number of obs: 326, groups: unique_id, 72
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.38776 0.12406 60.47151 3.126 0.00272 **
## y.plot -1.00670 0.12705 78.68008 -7.924 1.26e-11 ***
## monthssincebaseline 0.14668 0.06916 37.86308 2.121 0.04054 *
## baseline_age -0.02689 0.12244 67.32419 -0.220 0.82684
## educ 0.10169 0.10053 66.06126 1.011 0.31548
## y.plot:monthssincebaseline -0.14990 0.06616 38.60806 -2.266 0.02915 *
## ---
## 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 0.083
## mnthssncbsl 0.520 -0.036
## baseline_ag 0.102 0.526 0.012
## educ -0.034 -0.190 0.004 -0.138
## y.plt:mnths -0.046 0.465 -0.116 -0.005 0.005
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 | 46 | 0.0016445 | -0.1171879 | 0.1204770 | 1311418 | 512273 |
| change_ftlcdrm_sob | 49 | 0.0680562 | -0.1215492 | 0.2576616 | 1950 | 762 |
| change_strp | 39 | 0.0561758 | -0.0957903 | 0.2081419 | 1838 | 718 |
| change_flk | 47 | -0.1063423 | -0.1979202 | -0.0147644 | 187 | 73 |
| change_nback | 37 | 0.2202996 | -0.1761753 | 0.6167745 | 814 | 318 |
| change_humi | 46 | 0.0789750 | -0.0688138 | 0.2267638 | 880 | 344 |
| change_gonogo | 39 | -0.7086424 | -0.8579513 | -0.5593335 | 12 | 5 |
| change_trailsb | 38 | 0.0488388 | -0.1771472 | 0.2748247 | 5378 | 2101 |
| change_animals | 42 | 0.0307359 | -0.1813445 | 0.2428163 | 11959 | 4672 |
| change_uds3ef | 44 | 0.0199383 | -0.1039561 | 0.1438327 | 9699 | 3789 |
| change_trailsb | 38 | 0.0488388 | -0.1771472 | 0.2748247 | 5378 | 2101 |
| change_test3meanZ | 47 | -0.0108771 | -0.1225869 | 0.1008326 | 26492 | 10349 |
| change_test5meanZ | 48 | 0.0446316 | -0.0994255 | 0.1886887 | 2617 | 1023 |
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 | 22 | 0.0546608 | -0.0426198 | 0.1519413 | 796 | 311 |
| change_ftlcdrm_sob | 23 | 0.0712102 | -0.1655438 | 0.3079642 | 2777 | 1085 |
| change_strp | 22 | 0.0365825 | -0.0965223 | 0.1696872 | 3326 | 1299 |
| change_flk | 23 | -0.1119959 | -0.1972305 | -0.0267613 | 146 | 57 |
| change_nback | 21 | 0.3188890 | -0.0555448 | 0.6933227 | 347 | 136 |
| change_humi | 23 | 0.0574810 | -0.0800773 | 0.1950393 | 1439 | 562 |
| change_gonogo | 19 | -0.6980856 | -0.8680622 | -0.5281090 | 15 | 6 |
| change_trailsb | 17 | 0.0222778 | -0.1531571 | 0.1977127 | 15576 | 6085 |
| change_animals | 19 | 0.0124166 | -0.2118619 | 0.2366951 | 81946 | 32011 |
| change_uds3ef | 21 | 0.0137885 | -0.0981062 | 0.1256832 | 16541 | 6461 |
| change_trailsb_ratio | 17 | 395.5248489 | -1223.6570119 | 2014.7067097 | 4210 | 1645 |
| change_test3meanZ | 23 | -0.0179142 | -0.1160679 | 0.0802396 | 7541 | 2946 |
| change_test5meanZ | 23 | 0.0674000 | -0.0350702 | 0.1698702 | 581 | 227 |
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 | -0.0382327 | -0.1489209 | 0.0724555 | 2106 | 823 |
| change_ftlcdrm_sob | 24 | 0.0680872 | -0.0775101 | 0.2136845 | 1149 | 449 |
| change_strp | 16 | 0.0760328 | -0.1024619 | 0.2545275 | 1385 | 541 |
| change_flk | 22 | -0.0977089 | -0.2004608 | 0.0050431 | 278 | 109 |
| change_nback | 15 | 0.0935475 | -0.3188679 | 0.5059628 | 4882 | 1907 |
| change_humi | 21 | 0.0992470 | -0.0644335 | 0.2629276 | 684 | 267 |
| change_gonogo | 18 | -0.7137384 | -0.8505067 | -0.5769701 | 10 | 4 |
| change_trailsb | 19 | 0.0682152 | -0.2075629 | 0.3439933 | 4105 | 1604 |
| change_animals | 21 | 0.0363308 | -0.1741772 | 0.2468388 | 8433 | 3294 |
| change_uds3ef | 21 | 0.0204583 | -0.1209361 | 0.1618527 | 11998 | 4687 |
| change_trailsb_ratio | 19 | -5.2072241 | -22.7633894 | 12.3489412 | 2855 | 1116 |
| change_test3meanZ | 22 | -0.0133122 | -0.1400988 | 0.1134744 | 22783 | 8900 |
| change_test5meanZ | 23 | 0.0163610 | -0.1627228 | 0.1954448 | 30092 | 11755 |
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
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 | -0.2066665 | -0.3727945 | -0.0277015 |
| change_strp | 96 | -0.1160003 | -0.3092932 | 0.0864973 |
| change_flk | 121 | 0.0912820 | -0.0886591 | 0.2654534 |
| change_nback | 89 | -0.1780339 | -0.3724803 | 0.0313865 |
| change_humi | 118 | -0.1282161 | -0.3019768 | 0.0537899 |
| change_gonogo | 86 | 0.1114490 | -0.1028551 | 0.3158657 |
| change_animals | 109 | -0.0935155 | -0.2767491 | 0.0962798 |
| change_uds3ef | 80 | -0.3532072 | -0.5316640 | -0.1447215 |
| change_trailsb_ratio | 74 | -0.3316226 | -0.5206676 | -0.1115788 |
| change_test3meanZ | 121 | -0.0357850 | -0.2129215 | 0.1436289 |
| change_test5meanZ | 125 | -0.1542666 | -0.3211734 | 0.0219352 |
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 | 65 | -0.4301507 | -0.6100477 | -0.2080822 |
| change_strp | 52 | 0.1193019 | -0.1587675 | 0.3798357 |
| change_flk | 65 | -0.0722872 | -0.3107083 | 0.1746918 |
| change_nback | 51 | -0.0665476 | -0.3359698 | 0.2129413 |
| change_humi | 63 | -0.1495455 | -0.3831153 | 0.1019988 |
| change_gonogo | 58 | 0.0420896 | -0.2185825 | 0.2971549 |
| change_animals | 65 | 0.0721875 | -0.1747890 | 0.3106177 |
| change_uds3ef | 65 | -0.1680826 | -0.3957576 | 0.0790575 |
| change_trailsb_ratio | 65 | -0.3477403 | -0.5453835 | -0.1134645 |
| change_test3meanZ | 65 | -0.3071846 | -0.5126732 | -0.0684109 |
| change_test5meanZ | 65 | -0.3565664 | -0.5524234 | -0.1233989 |
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 = -3.1569, df = 134, p-value = 0.00197
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.41314251 -0.09916674
## sample estimates:
## cor
## -0.2631076## [1] "Standardized correlation"##
## Pearson's product-moment correlation
##
## data: df.smartphone.other$change_z and df.smartphone.other$composite5_z
## t = -3.0281, df = 134, p-value = 0.002953
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.40418783 -0.08851135
## sample estimates:
## cor
## -0.2530736