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: -381
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.2586 -0.0390 -0.0171 0.0059 3.3202
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.0500871 0.22380
## monthssincebaseline 0.0001084 0.01041 0.16
## Residual 0.0041197 0.06419
## Number of obs: 282, groups: unique_id, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.9894053 0.2361784 48.2836794 -4.189 0.000118
## y.plot -0.0579086 0.0295491 50.9145283 -1.960 0.055511
## monthssincebaseline 0.0018262 0.0018133 35.2622538 1.007 0.320733
## baseline_age 0.0006052 0.0027117 48.6612175 0.223 0.824322
## educ 0.0286649 0.0137799 49.0984055 2.080 0.042748
## y.plot:monthssincebaseline 0.0008665 0.0017522 34.6590985 0.494 0.624081
##
## (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 -0.190
## mnthssncbsl 0.024 0.001
## baseline_ag -0.372 0.237 0.006
## educ -0.846 0.068 -0.018 -0.163
## y.plt:mnths -0.002 0.070 -0.035 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: -397.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.4796 -0.0434 -0.0124 0.0121 3.4059
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.276e-02 0.206795
## monthssincebaseline 9.501e-05 0.009747 0.31
## Residual 3.987e-03 0.063142
## Number of obs: 283, groups: unique_id, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.769376 0.228222 47.257904 -3.371 0.0015 **
## y.plot -0.079589 0.036784 53.363230 -2.164 0.0350 *
## monthssincebaseline 0.002274 0.001672 36.414281 1.360 0.1822
## baseline_age -0.003903 0.002900 49.773726 -1.346 0.1844
## educ 0.025973 0.012492 48.489549 2.079 0.0429 *
## y.plot:monthssincebaseline -0.003212 0.001704 36.956254 -1.885 0.0673 .
## ---
## 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 -0.459
## mnthssncbsl 0.026 0.021
## baseline_ag -0.468 0.596 0.040
## educ -0.813 0.132 -0.027 -0.120
## y.plt:mnths 0.018 0.150 -0.092 -0.008 -0.014
## [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: -430.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.6086 -0.0439 -0.0090 0.0110 3.4397
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.350e-02 0.208578
## monthssincebaseline 9.737e-05 0.009868 0.13
## Residual 3.876e-03 0.062260
## Number of obs: 303, groups: unique_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.679764 0.225281 56.428125 -3.017 0.003820 **
## y.plot -0.136641 0.034727 58.798401 -3.935 0.000223 ***
## monthssincebaseline 0.001681 0.001643 40.205723 1.023 0.312332
## baseline_age -0.006234 0.002972 57.078591 -2.097 0.040405 *
## educ 0.029727 0.012234 56.548267 2.430 0.018305 *
## y.plot:monthssincebaseline -0.001225 0.001663 40.741157 -0.737 0.465523
## ---
## 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 -0.427
## mnthssncbsl 0.012 0.008
## baseline_ag -0.480 0.611 0.013
## educ -0.784 0.048 -0.014 -0.155
## y.plt:mnths 0.010 0.032 -0.076 -0.011 -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: -370.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.1657 -0.0528 -0.0150 0.0242 3.3029
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 3.448e-02 0.185681
## monthssincebaseline 9.016e-05 0.009495 0.40
## Residual 4.238e-03 0.065103
## Number of obs: 261, groups: unique_id, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.896430 0.184604 39.218329 -4.856 1.95e-05 ***
## y.plot -0.091655 0.027130 47.028660 -3.378 0.00147 **
## monthssincebaseline 0.003070 0.001702 30.933179 1.804 0.08098 .
## baseline_age -0.003562 0.002381 43.352439 -1.496 0.14196
## educ 0.032652 0.011695 42.059041 2.792 0.00785 **
## y.plot:monthssincebaseline -0.005804 0.001736 33.691912 -3.343 0.00204 **
## ---
## 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 -0.075
## mnthssncbsl 0.058 0.011
## baseline_ag -0.258 0.418 0.031
## educ -0.818 -0.169 -0.037 -0.327
## y.plt:mnths 0.013 0.193 -0.140 -0.034 0.006
## [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: -416.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.5027 -0.0441 -0.0134 0.0100 3.4065
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.044925 0.21196
## monthssincebaseline 0.000101 0.01005 0.19
## Residual 0.003949 0.06284
## Number of obs: 297, groups: unique_id, 59
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.9672801 0.2104248 52.3069982 -4.597 2.75e-05
## y.plot -0.1031237 0.0319081 54.0650785 -3.232 0.00210
## monthssincebaseline 0.0017220 0.0016904 38.7454542 1.019 0.31466
## baseline_age -0.0036767 0.0027690 52.5259243 -1.328 0.18998
## educ 0.0393759 0.0130208 52.9084645 3.024 0.00384
## y.plot:monthssincebaseline -0.0007615 0.0016646 40.3053282 -0.457 0.64980
##
## (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 -0.095
## mnthssncbsl 0.024 0.011
## baseline_ag -0.299 0.508 0.019
## educ -0.806 -0.215 -0.021 -0.310
## y.plt:mnths 0.018 0.097 -0.101 0.014 -0.026
## [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: -428.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.5966 -0.0455 -0.0103 0.0127 3.4386
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.462e-02 0.211237
## monthssincebaseline 9.788e-05 0.009893 0.17
## Residual 3.884e-03 0.062318
## Number of obs: 303, groups: unique_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.881618 0.211190 55.214017 -4.175 0.000107 ***
## y.plot -0.125385 0.033856 56.547179 -3.704 0.000485 ***
## monthssincebaseline 0.001670 0.001649 40.217325 1.013 0.317254
## baseline_age -0.005522 0.002928 55.356780 -1.886 0.064527 .
## educ 0.039725 0.012507 55.701642 3.176 0.002433 **
## y.plot:monthssincebaseline -0.001071 0.001647 41.603584 -0.650 0.518946
## ---
## 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 -0.226
## mnthssncbsl 0.019 0.011
## baseline_ag -0.373 0.583 0.018
## educ -0.776 -0.159 -0.020 -0.281
## y.plt:mnths 0.012 0.073 -0.107 0.007 -0.017
## [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: -442.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.6987 -0.0421 -0.0150 0.0152 3.4611
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.539e-02 0.213054
## monthssincebaseline 9.145e-05 0.009563 0.04
## Residual 3.812e-03 0.061737
## Number of obs: 309, groups: unique_id, 63
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.863057 0.215263 58.095053 -4.009 0.000176 ***
## y.plot -0.110714 0.033714 58.537091 -3.284 0.001731 **
## monthssincebaseline 0.001883 0.001604 39.935863 1.174 0.247348
## baseline_age -0.004289 0.002914 58.147612 -1.472 0.146425
## educ 0.034675 0.012461 58.121098 2.783 0.007261 **
## y.plot:monthssincebaseline -0.002553 0.001619 41.270094 -1.577 0.122405
## ---
## 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 -0.270
## mnthssncbsl 0.001 0.004
## baseline_ag -0.396 0.600 0.004
## educ -0.787 -0.113 -0.005 -0.241
## y.plt:mnths 0.004 -0.008 -0.116 -0.003 -0.003Sample 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
## 57 22 12
## [1] "Gentic breakdown"
##
## C9 MAPT
## 25 12
## [1] "Baseline CDR-NACC+FTLD-motor global score"
##
## 0.5 1 2
## 14 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.
## [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: 31.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0308 -0.0427 0.0030 0.0616 1.5717
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2634902 0.51331
## monthssincebaseline 0.0009246 0.03041 -0.50
## Residual 0.0146029 0.12084
## Number of obs: 69, groups: unique_id, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.149873 0.598307 12.158091 -3.593 0.003619 **
## y.plot 0.106928 0.119174 12.457471 0.897 0.386598
## monthssincebaseline 0.012361 0.010217 6.651204 1.210 0.267560
## baseline_age 0.031699 0.011355 12.153092 2.792 0.016128 *
## educ 0.032605 0.005691 8.036632 5.729 0.000432 ***
## y.plot:monthssincebaseline 0.005873 0.011802 6.148022 0.498 0.636047
## ---
## 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 -0.304
## mnthssncbsl -0.084 -0.015
## baseline_ag -0.957 0.344 0.016
## educ -0.034 -0.103 -0.074 -0.170
## y.plt:mnths -0.034 -0.295 0.011 0.032 -0.006
## [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: 18.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3273 -0.1101 -0.0059 0.1081 1.5028
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.135903 0.36865
## monthssincebaseline 0.000418 0.02045 -0.82
## Residual 0.018502 0.13602
## Number of obs: 53, groups: unique_id, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.238580 0.734013 6.990133 -3.050 0.0186 *
## y.plot -0.062312 0.147572 9.350818 -0.422 0.6824
## monthssincebaseline 0.010963 0.007303 8.379862 1.501 0.1700
## baseline_age 0.017221 0.008064 8.127343 2.136 0.0647 .
## educ 0.085337 0.038364 8.512909 2.224 0.0548 .
## y.plot:monthssincebaseline -0.015268 0.009427 8.076944 -1.620 0.1436
## ---
## 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 -0.314 0.170
## baseline_ag -0.467 0.136 0.098
## educ -0.820 0.047 0.174 -0.100
## y.plt:mnths -0.038 -0.673 -0.188 0.242 -0.084
## [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: 20.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3484 -0.0776 0.0000 0.0603 1.7549
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2336763 0.48340
## monthssincebaseline 0.0004899 0.02213 -0.48
## Residual 0.0130509 0.11424
## Number of obs: 78, groups: unique_id, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.932063 0.713679 14.847193 -2.707 0.01634 *
## y.plot 0.074725 0.179486 15.732820 0.416 0.68279
## monthssincebaseline 0.008366 0.007019 9.354772 1.192 0.26268
## baseline_age 0.027474 0.011876 14.637605 2.313 0.03569 *
## educ 0.032303 0.008728 13.022332 3.701 0.00266 **
## y.plot:monthssincebaseline -0.013414 0.005711 8.347454 -2.349 0.04552 *
## ---
## 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 -0.665
## mnthssncbsl -0.156 0.068
## baseline_ag -0.958 0.546 0.095
## educ -0.573 0.743 0.058 0.364
## y.plt:mnths -0.075 -0.158 0.172 0.046 0.135
## [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: 22.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0078 -0.0501 0.0169 0.1571 1.3720
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.1593038 0.39913
## monthssincebaseline 0.0003747 0.01936 -0.90
## Residual 0.0210890 0.14522
## Number of obs: 45, groups: unique_id, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.665638 0.829090 3.215560 -2.009 0.1320
## y.plot -0.235695 0.162348 7.932454 -1.452 0.1849
## monthssincebaseline 0.011816 0.007513 5.605894 1.573 0.1703
## baseline_age -0.003645 0.011863 5.521325 -0.307 0.7699
## educ 0.111539 0.042883 6.122948 2.601 0.0399 *
## y.plot:monthssincebaseline -0.014213 0.007243 5.145846 -1.962 0.1054
## ---
## 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 -0.289
## mnthssncbsl -0.377 -0.079
## baseline_ag -0.526 0.577 0.063
## educ -0.706 -0.110 0.246 -0.211
## y.plt:mnths -0.162 -0.550 0.122 0.188 0.006
## [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: 24.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2910 -0.0541 -0.0020 0.0510 1.6679
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2263934 0.47581
## monthssincebaseline 0.0008425 0.02903 -0.24
## Residual 0.0133810 0.11568
## Number of obs: 75, groups: unique_id, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.393657 0.714154 13.124140 -1.951 0.072675 .
## y.plot -0.128879 0.134340 14.011093 -0.959 0.353648
## monthssincebaseline 0.011833 0.009928 7.795363 1.192 0.268324
## baseline_age 0.018836 0.013132 13.046681 1.434 0.175004
## educ 0.028243 0.006036 12.497520 4.679 0.000478 ***
## y.plot:monthssincebaseline -0.006474 0.012117 8.102851 -0.534 0.607501
## ---
## 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 -0.633
## mnthssncbsl -0.070 0.019
## baseline_ag -0.971 0.614 0.040
## educ -0.222 0.215 -0.003 0.044
## y.plt:mnths -0.035 -0.102 0.213 0.020 0.085
## [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: 22
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3596 -0.0469 -0.0017 0.0678 1.7335
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2185188 0.46746
## monthssincebaseline 0.0005803 0.02409 -0.29
## Residual 0.0130639 0.11430
## Number of obs: 78, groups: unique_id, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.398006 0.785570 14.098653 -1.780 0.09670 .
## y.plot -0.102896 0.159841 15.056965 -0.644 0.52943
## monthssincebaseline 0.009837 0.007792 8.882347 1.262 0.23894
## baseline_age 0.019662 0.013690 13.977498 1.436 0.17295
## educ 0.026426 0.007132 13.996020 3.705 0.00235 **
## y.plot:monthssincebaseline -0.012205 0.007155 8.727346 -1.706 0.12332
## ---
## 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 -0.735
## mnthssncbsl -0.091 0.043
## baseline_ag -0.973 0.680 0.059
## educ -0.498 0.583 0.020 0.334
## y.plt:mnths -0.043 -0.090 0.147 0.024 0.116
## [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: 19.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3553 -0.0645 0.0062 0.0764 1.7164
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.223086 0.47232
## monthssincebaseline 0.000451 0.02124 -0.42
## Residual 0.013098 0.11444
## Number of obs: 78, groups: unique_id, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.380179 0.724184 14.472094 -1.906 0.076731 .
## y.plot -0.092876 0.149710 15.466474 -0.620 0.544046
## monthssincebaseline 0.009119 0.006789 8.680655 1.343 0.213268
## baseline_age 0.018561 0.012820 14.283559 1.448 0.169255
## educ 0.028354 0.006430 11.626700 4.410 0.000917 ***
## y.plot:monthssincebaseline -0.015801 0.006606 8.590440 -2.392 0.041689 *
## ---
## 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 -0.677
## mnthssncbsl -0.131 0.059
## baseline_ag -0.971 0.627 0.085
## educ -0.415 0.480 0.010 0.243
## y.plt:mnths -0.090 -0.135 0.128 0.067 0.155LME 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"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00255417 (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: -262.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4149 -0.0686 -0.0064 0.0418 5.5919
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.701e-02 0.258859
## monthssincebaseline 5.101e-05 0.007142 0.49
## Residual 4.632e-03 0.068062
## Number of obs: 205, groups: unique_id, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.5590434 0.2785901 31.8464904 -2.007 0.0533 .
## y.plot -0.0919632 0.0538670 36.7710004 -1.707 0.0962 .
## monthssincebaseline 0.0019029 0.0014697 30.1024393 1.295 0.2053
## baseline_age 0.0071785 0.0033161 34.9142156 2.165 0.0373 *
## educ -0.0170555 0.0118312 29.6084493 -1.442 0.1599
## y.plot:monthssincebaseline -0.0005716 0.0015495 25.9370769 -0.369 0.7152
## ---
## 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 -0.195
## mnthssncbsl 0.033 -0.015
## baseline_ag -0.685 0.371 0.009
## educ -0.755 -0.066 0.016 0.064
## y.plt:mnths -0.022 0.382 0.018 0.040 -0.009
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00255417 (tol = 0.002, component 1)## [1] "y.plot = baseline_strp"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00434854 (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: -758.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2069 -0.0385 0.0002 0.0352 5.5418
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.494e-02 0.254824
## monthssincebaseline 2.086e-06 0.001444 0.04
## Residual 2.458e-04 0.015677
## Number of obs: 211, groups: unique_id, 43
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.6122628 0.2970105 39.0954157 -2.061 0.046 *
## y.plot -0.0261604 0.0580324 39.1133869 -0.451 0.655
## monthssincebaseline 0.0004510 0.0003147 28.5583435 1.433 0.163
## baseline_age 0.0067892 0.0041697 39.1136396 1.628 0.112
## educ -0.0140882 0.0119994 39.0800448 -1.174 0.247
## y.plot:monthssincebaseline -0.0001065 0.0002970 29.3245499 -0.359 0.722
## ---
## 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 -0.468
## mnthssncbsl -0.003 0.004
## baseline_ag -0.720 0.724 0.006
## educ -0.672 -0.094 0.002 -0.013
## y.plt:mnths -0.002 0.014 -0.009 0.002 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00434854 (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: -334.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6643 -0.0850 -0.0019 0.0785 5.9466
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 5.682e-02 0.238376
## monthssincebaseline 4.607e-05 0.006787 0.64
## Residual 4.086e-03 0.063922
## Number of obs: 234, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.406417 0.235844 37.216602 -1.723 0.093145 .
## y.plot -0.149602 0.042181 46.968504 -3.547 0.000897 ***
## monthssincebaseline 0.001131 0.001271 37.278600 0.890 0.379393
## baseline_age 0.001092 0.003269 43.675207 0.334 0.739836
## educ -0.007347 0.010137 33.204857 -0.725 0.473667
## y.plot:monthssincebaseline 0.000770 0.001181 43.185399 0.652 0.517945
## ---
## 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 -0.282
## mnthssncbsl -0.007 0.066
## baseline_ag -0.673 0.615 0.095
## educ -0.674 -0.223 0.009 -0.073
## y.plt:mnths -0.022 0.323 -0.042 -0.019 0.052## [1] "y.plot = baseline_nback"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00335639 (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: -661.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9923 -0.0378 0.0009 0.0524 5.2509
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 7.187e-02 0.268092
## monthssincebaseline 2.123e-06 0.001457 -0.01
## Residual 2.734e-04 0.016534
## Number of obs: 187, groups: unique_id, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.6122379 0.3158756 32.0431607 -1.938 0.0614 .
## y.plot -0.0670684 0.0506972 32.0436031 -1.323 0.1952
## monthssincebaseline 0.0005399 0.0003396 24.4449832 1.590 0.1247
## baseline_age 0.0071864 0.0038499 32.0465710 1.867 0.0711 .
## educ -0.0147369 0.0133046 32.0489769 -1.108 0.2763
## y.plot:monthssincebaseline -0.0004086 0.0003153 26.5239046 -1.296 0.2062
## ---
## 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 -0.238
## mnthssncbsl 0.000 0.000
## baseline_ag -0.690 0.429 -0.002
## educ -0.748 -0.060 -0.001 0.057
## y.plt:mnths 0.000 -0.017 -0.072 0.000 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00335639 (tol = 0.002, component 1)
## [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: -310.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5783 -0.0481 -0.0021 0.0244 5.8540
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.197e-02 0.248937
## monthssincebaseline 4.123e-05 0.006421 0.35
## Residual 4.233e-03 0.065058
## Number of obs: 225, groups: unique_id, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.464592 0.286949 38.835519 -1.619 0.114
## y.plot -0.135514 0.052841 41.834941 -2.565 0.014 *
## monthssincebaseline 0.001841 0.001294 32.298687 1.423 0.164
## baseline_age 0.003230 0.004080 40.814096 0.792 0.433
## educ -0.008959 0.012412 37.189255 -0.722 0.475
## y.plot:monthssincebaseline -0.001738 0.001247 37.018792 -1.394 0.172
## ---
## 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 -0.328
## mnthssncbsl 0.013 -0.015
## baseline_ag -0.683 0.694 0.010
## educ -0.626 -0.306 0.011 -0.127
## y.plt:mnths -0.011 0.140 -0.070 0.000 0.010
## [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: -333.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6400 -0.0455 -0.0079 0.0385 5.9477
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 5.279e-02 0.229764
## monthssincebaseline 4.244e-05 0.006515 0.43
## Residual 4.097e-03 0.064012
## Number of obs: 234, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.550e-01 2.576e-01 4.073e+01 -1.378 0.175681
## y.plot -1.764e-01 4.985e-02 4.521e+01 -3.539 0.000943
## monthssincebaseline 1.626e-03 1.269e-03 3.444e+01 1.281 0.208726
## baseline_age -2.141e-05 3.861e-03 4.359e+01 -0.006 0.995601
## educ -5.601e-03 1.111e-02 3.798e+01 -0.504 0.617068
## y.plot:monthssincebaseline -9.821e-04 1.204e-03 4.048e+01 -0.816 0.419524
##
## (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 -0.374
## mnthssncbsl 0.001 -0.001
## baseline_ag -0.687 0.739 0.030
## educ -0.596 -0.315 0.017 -0.160
## y.plt:mnths -0.023 0.170 -0.065 0.000 0.025
## [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: -368
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7692 -0.0452 -0.0017 0.0292 6.1741
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.308e-02 0.251154
## monthssincebaseline 3.957e-05 0.006291 0.41
## Residual 3.807e-03 0.061700
## Number of obs: 251, groups: unique_id, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.4434478 0.2727029 45.5388735 -1.626 0.1108
## y.plot -0.0963059 0.0511489 48.4406059 -1.883 0.0657 .
## monthssincebaseline 0.0015835 0.0011885 37.5877549 1.332 0.1908
## baseline_age 0.0038546 0.0037525 47.4008391 1.027 0.3095
## educ -0.0135563 0.0112846 42.8689374 -1.201 0.2362
## y.plot:monthssincebaseline -0.0008985 0.0011246 40.2136998 -0.799 0.4290
## ---
## 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 -0.429
## mnthssncbsl -0.012 0.010
## baseline_ag -0.710 0.709 0.048
## educ -0.661 -0.153 0.017 -0.043
## y.plt:mnths -0.025 0.210 -0.062 0.023 0.004
Sample description for model 2 (Sporadic)
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3
## 115 46 27
## [1] "Gentic breakdown"
##
## NONE
## 76
## [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.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00394044 (tol = 0.002, component 1)
## [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: 66.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84290 -0.15434 -0.01260 0.06264 2.97988
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.804989 0.89721
## monthssincebaseline 0.003633 0.06027 0.20
## Residual 0.016371 0.12795
## Number of obs: 134, groups: unique_id, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.071928 1.673579 24.459658 -0.043 0.966068
## y.plot -0.524148 0.131974 24.313509 -3.972 0.000555 ***
## monthssincebaseline 0.026539 0.015845 13.359597 1.675 0.117190
## baseline_age -0.004940 0.022837 24.302143 -0.216 0.830546
## educ 0.066663 0.069936 24.019584 0.953 0.349990
## y.plot:monthssincebaseline 0.005363 0.013879 13.355965 0.386 0.705265
## ---
## 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 0.047
## mnthssncbsl 0.034 0.032
## baseline_ag -0.727 -0.056 -0.012
## educ -0.496 0.039 -0.014 -0.227
## y.plt:mnths -0.030 0.109 0.056 0.028 0.011
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00394044 (tol = 0.002, component 1)
##
## [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: -126.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26409 -0.03335 0.00061 0.02450 2.91524
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.4110582 0.64114
## monthssincebaseline 0.0024987 0.04999 0.31
## Residual 0.0008692 0.02948
## Number of obs: 73, groups: unique_id, 13
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.72204 2.22144 8.77069 -1.225 0.2523
## y.plot -0.39806 0.18457 9.02947 -2.157 0.0593 .
## monthssincebaseline 0.01808 0.01661 9.42962 1.088 0.3034
## baseline_age 0.03545 0.02459 8.30664 1.442 0.1860
## educ 0.03843 0.08363 7.86091 0.460 0.6583
## y.plot:monthssincebaseline -0.04806 0.01477 8.72542 -3.253 0.0104 *
## ---
## 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 0.068 0.103
## baseline_ag -0.794 0.196 -0.024
## educ -0.722 -0.475 -0.045 0.161
## y.plt:mnths -0.002 0.224 0.208 0.035 -0.023
## [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: 78.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.92429 -0.07658 -0.00912 0.03387 3.10767
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.866070 0.93063
## monthssincebaseline 0.003406 0.05836 0.32
## Residual 0.015083 0.12281
## Number of obs: 151, groups: unique_id, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.697720 1.523939 29.489770 0.458 0.650
## y.plot -0.742744 0.133473 30.997470 -5.565 4.25e-06 ***
## monthssincebaseline 0.028271 0.014153 15.201805 1.997 0.064 .
## baseline_age -0.022805 0.021880 26.953746 -1.042 0.307
## educ 0.099655 0.065745 26.530736 1.516 0.141
## y.plot:monthssincebaseline 0.001958 0.011607 15.331433 0.169 0.868
## ---
## 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 -0.026
## mnthssncbsl 0.059 0.062
## baseline_ag -0.714 0.218 -0.020
## educ -0.443 -0.204 -0.024 -0.303
## y.plt:mnths -0.025 0.205 0.080 0.031 0.005
## [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: -124.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.24313 -0.03148 0.00452 0.02120 2.87825
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.3304432 0.57484
## monthssincebaseline 0.0054189 0.07361 0.74
## Residual 0.0008891 0.02982
## Number of obs: 71, groups: unique_id, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.524685 1.826157 6.435219 0.287 0.783
## y.plot -0.113305 0.170569 9.555068 -0.664 0.522
## monthssincebaseline 0.032375 0.023143 10.661949 1.399 0.190
## baseline_age 0.011218 0.020422 5.713156 0.549 0.604
## educ -0.070184 0.053603 6.628790 -1.309 0.234
## y.plot:monthssincebaseline -0.008877 0.021607 10.046439 -0.411 0.690
##
## Correlation of Fixed Effects:
## (Intr) y.plot mnthss bsln_g educ
## y.plot 0.280
## mnthssncbsl 0.113 0.191
## baseline_ag -0.904 -0.257 -0.051
## educ -0.764 -0.161 -0.025 0.429
## y.plt:mnths 0.022 0.692 0.183 0.002 -0.012
## [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: 75.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.88130 -0.11609 -0.00431 0.03889 3.08788
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.854373 0.92432
## monthssincebaseline 0.003056 0.05528 -0.07
## Residual 0.015217 0.12336
## Number of obs: 149, groups: unique_id, 34
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.158247 1.576711 30.059387 -0.735 0.468
## y.plot -0.604461 0.130335 30.116160 -4.638 6.42e-05 ***
## monthssincebaseline 0.021135 0.013754 14.949026 1.537 0.145
## baseline_age -0.004159 0.021638 30.004166 -0.192 0.849
## educ 0.135409 0.069262 29.852174 1.955 0.060 .
## y.plot:monthssincebaseline -0.014941 0.014507 15.362029 -1.030 0.319
## ---
## 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 0.098
## mnthssncbsl -0.015 -0.014
## baseline_ag -0.692 0.011 0.004
## educ -0.493 -0.108 0.005 -0.279
## y.plt:mnths 0.004 -0.059 -0.004 0.002 -0.010
## [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: 72.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.89796 -0.07188 -0.01013 0.05073 3.10587
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.703708 0.8389
## monthssincebaseline 0.003283 0.0573 0.18
## Residual 0.015084 0.1228
## Number of obs: 151, groups: unique_id, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.029963 1.393147 30.124400 0.022 0.9830
## y.plot -0.792243 0.117495 30.782349 -6.743 1.57e-07 ***
## monthssincebaseline 0.024480 0.014127 15.086490 1.733 0.1035
## baseline_age -0.018556 0.019830 28.875311 -0.936 0.3571
## educ 0.120109 0.060830 28.579408 1.975 0.0581 .
## y.plot:monthssincebaseline -0.001484 0.013230 15.503360 -0.112 0.9121
## ---
## 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 0.062
## mnthssncbsl 0.036 0.039
## baseline_ag -0.704 0.157 -0.013
## educ -0.455 -0.239 -0.017 -0.304
## y.plt:mnths -0.008 0.077 0.038 0.006 0.009
## [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: 78.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8733 -0.1095 -0.0040 0.0411 3.1112
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.90334 0.95044
## monthssincebaseline 0.00311 0.05577 -0.16
## Residual 0.01507 0.12275
## Number of obs: 151, groups: unique_id, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.40909 1.58212 30.84278 -0.259 0.798
## y.plot -0.70691 0.13089 31.05037 -5.401 6.77e-06 ***
## monthssincebaseline 0.01884 0.01383 14.91262 1.363 0.193
## baseline_age -0.01285 0.02221 30.40534 -0.578 0.567
## educ 0.12658 0.06913 30.13965 1.831 0.077 .
## y.plot:monthssincebaseline -0.01301 0.01261 15.36292 -1.032 0.318
## ---
## 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 -0.031 -0.035
## baseline_ag -0.700 0.098 0.009
## educ -0.468 -0.264 0.012 -0.294
## y.plt:mnths 0.014 -0.106 0.048 -0.013 -0.006Sample description for model 3
## [1] "N participants at each visit (NOT chapter)"
##
## 1 2 3
## 220 101 60
## [1] "Gentic breakdown"
##
## NONE
## 153
## [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_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: -18.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5672 -0.0323 -0.0045 0.0133 4.1382
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.572031 0.75633
## monthssincebaseline 0.001646 0.04057 0.34
## Residual 0.008509 0.09224
## Number of obs: 266, groups: unique_id, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.1140447 0.6992086 49.7545560 -1.593 0.1174
## y.plot -0.5361751 0.0816994 53.9472791 -6.563 2.12e-08
## monthssincebaseline 0.0120826 0.0069758 32.5483646 1.732 0.0927
## baseline_age 0.0172499 0.0078619 51.5988443 2.194 0.0328
## educ 0.0085750 0.0319700 48.7010606 0.268 0.7897
## y.plot:monthssincebaseline -0.0007371 0.0065320 33.1463841 -0.113 0.9108
##
## (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 -0.119
## mnthssncbsl 0.034 0.038
## baseline_ag -0.614 0.270 -0.006
## educ -0.759 -0.040 0.010 -0.028
## y.plt:mnths -0.024 0.204 0.002 0.015 0.025
## [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: -557.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7926 -0.0106 -0.0004 0.0055 4.8896
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.2140089 0.46261
## monthssincebaseline 0.0012066 0.03474 0.60
## Residual 0.0003093 0.01759
## Number of obs: 222, groups: unique_id, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.515632 0.422991 26.438427 -1.219 0.2336
## y.plot -0.262362 0.080709 44.551314 -3.251 0.0022 **
## monthssincebaseline 0.009516 0.005849 41.607209 1.627 0.1113
## baseline_age 0.008325 0.005839 29.333041 1.426 0.1644
## educ -0.017628 0.017599 22.583422 -1.002 0.3271
## y.plot:monthssincebaseline -0.023904 0.005384 41.828587 -4.440 6.46e-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 0.020 0.062
## baseline_ag -0.699 0.633 0.064
## educ -0.676 -0.151 0.023 -0.030
## y.plt:mnths 0.025 0.386 0.007 -0.008 -0.020
## [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: -25.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7303 -0.0295 -0.0045 0.0144 4.3870
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.668061 0.81735
## monthssincebaseline 0.001573 0.03967 0.46
## Residual 0.007578 0.08705
## Number of obs: 306, groups: unique_id, 70
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.907186 0.676767 47.122225 -1.340 0.1865
## y.plot -0.691448 0.085948 67.342467 -8.045 1.93e-11 ***
## monthssincebaseline 0.012987 0.006192 35.298664 2.097 0.0432 *
## baseline_age 0.011414 0.007797 49.494519 1.464 0.1495
## educ 0.021229 0.031549 42.260282 0.673 0.5047
## y.plot:monthssincebaseline -0.003954 0.005439 37.997032 -0.727 0.4717
## ---
## 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 -0.144
## mnthssncbsl 0.036 0.058
## baseline_ag -0.591 0.429 0.004
## educ -0.752 -0.136 0.016 -0.067
## y.plt:mnths -0.027 0.278 -0.004 0.020 0.028
## [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: -497.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6328 -0.0130 -0.0012 0.0090 4.6789
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.1663573 0.40787
## monthssincebaseline 0.0017930 0.04234 0.69
## Residual 0.0003375 0.01837
## Number of obs: 196, groups: unique_id, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.366103 0.360176 25.760400 -1.016 0.3189
## y.plot -0.168779 0.070989 39.358154 -2.378 0.0224 *
## monthssincebaseline 0.009094 0.007509 38.322935 1.211 0.2333
## baseline_age 0.007003 0.004497 27.777105 1.557 0.1307
## educ -0.023391 0.014860 24.714631 -1.574 0.1282
## y.plot:monthssincebaseline -0.010112 0.007269 35.642938 -1.391 0.1728
## ---
## 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 -0.178
## mnthssncbsl 0.075 0.030
## baseline_ag -0.701 0.361 0.035
## educ -0.720 -0.085 0.019 0.045
## y.plt:mnths 0.004 0.603 -0.010 0.019 -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: -22.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6242 -0.0219 -0.0014 0.0113 4.3139
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.673871 0.82090
## monthssincebaseline 0.001333 0.03651 0.13
## Residual 0.007805 0.08834
## Number of obs: 295, groups: unique_id, 66
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.672053 0.782355 61.495355 -0.859 0.3937
## y.plot -0.780553 0.115558 62.316713 -6.755 5.59e-09 ***
## monthssincebaseline 0.011821 0.006096 35.067870 1.939 0.0606 .
## baseline_age -0.003083 0.010155 61.920000 -0.304 0.7624
## educ 0.059204 0.035726 61.076112 1.657 0.1026
## y.plot:monthssincebaseline -0.011552 0.005977 35.442945 -1.933 0.0613 .
## ---
## 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 -0.307
## mnthssncbsl 0.010 0.010
## baseline_ag -0.623 0.644 0.003
## educ -0.671 -0.203 -0.001 -0.148
## y.plt:mnths -0.001 0.042 -0.105 -0.011 0.014
## [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: -42.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6783 -0.0236 -0.0044 0.0142 4.3833
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.509426 0.71374
## monthssincebaseline 0.001379 0.03714 0.24
## Residual 0.007576 0.08704
## Number of obs: 306, groups: unique_id, 70
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.360990 0.634870 60.433672 -0.569 0.5717
## y.plot -0.869994 0.086447 65.866904 -10.064 5.97e-15 ***
## monthssincebaseline 0.011792 0.006036 36.136175 1.954 0.0585 .
## baseline_age -0.005693 0.008005 61.928246 -0.711 0.4797
## educ 0.046529 0.029825 58.031719 1.560 0.1242
## y.plot:monthssincebaseline -0.008519 0.005862 37.489102 -1.453 0.1545
## ---
## 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 -0.227
## mnthssncbsl 0.012 0.027
## baseline_ag -0.593 0.596 0.011
## educ -0.704 -0.221 0.004 -0.139
## y.plt:mnths -0.003 0.087 -0.078 -0.018 0.024
## [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: -43.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7402 -0.0189 -0.0031 0.0088 4.5173
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 0.700218 0.83679
## monthssincebaseline 0.001237 0.03517 0.14
## Residual 0.007141 0.08450
## Number of obs: 323, groups: unique_id, 73
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.347004 0.726513 67.801585 -0.478 0.6344
## y.plot -0.800589 0.105641 69.061554 -7.578 1.17e-10 ***
## monthssincebaseline 0.010897 0.005617 38.271479 1.940 0.0598 .
## baseline_age -0.002750 0.009193 68.235290 -0.299 0.7657
## educ 0.036665 0.034451 66.740974 1.064 0.2910
## y.plot:monthssincebaseline -0.011044 0.005350 39.015759 -2.065 0.0457 *
## ---
## 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 -0.258
## mnthssncbsl 0.004 0.016
## baseline_ag -0.590 0.615 0.011
## educ -0.701 -0.205 0.002 -0.146
## y.plt:mnths -0.001 0.058 -0.077 -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 | -0.0382077 | -0.1886104 | 0.1121950 | 3892 | 1521 |
| change_test5meanZ | 46 | 0.0315872 | -0.1504258 | 0.2136002 | 8340 | 3258 |
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 | -0.0412737 | -0.1480054 | 0.0654580 | 1680 | 657 |
| change_test5meanZ | 21 | 0.0705397 | -0.0582554 | 0.1993349 | 838 | 328 |
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 | -0.0446723 | -0.2326941 | 0.1433495 | 4450 | 1738 |
| change_test5meanZ | 23 | -0.0082146 | -0.2298488 | 0.2134195 | 182834 | 71420 |
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 | -0.0569793 | -0.2780355 | 0.1640768 | 3781 | 1477 |
| change_test5meanZ | 12 | -0.0169723 | -0.2798492 | 0.2459045 | 60253 | 23537 |
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 | 118 | -0.2099112 | -0.3763890 | -0.0303016 |
| change_strp | 96 | -0.0809956 | -0.2769838 | 0.1214627 |
| change_flk | 120 | 0.0908792 | -0.0898253 | 0.2657910 |
| change_nback | 88 | -0.1735234 | -0.3695355 | 0.0372735 |
| change_humi | 117 | -0.0984351 | -0.2750531 | 0.0846098 |
| change_gonogo | 85 | 0.1179789 | -0.0975992 | 0.3229819 |
| change_animals | 109 | -0.0938963 | -0.2771038 | 0.0958992 |
| change_uds3ef | 79 | -0.3537916 | -0.5331920 | -0.1439413 |
| change_trailsb_ratio | 74 | -0.2427848 | -0.4465128 | -0.0151252 |
| change_test3meanZ | 120 | -0.1094040 | -0.2830939 | 0.0712343 |
| change_test5meanZ | 124 | -0.1664203 | -0.3329667 | 0.0101955 |
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 | -0.4277844 | -0.6082237 | -0.2053062 |
| change_strp | 52 | 0.1193574 | -0.1587126 | 0.3798839 |
| change_flk | 65 | -0.0714677 | -0.3099639 | 0.1754904 |
| change_nback | 51 | -0.0650439 | -0.3346293 | 0.2143826 |
| change_humi | 63 | -0.1472020 | -0.3810689 | 0.1043696 |
| change_gonogo | 58 | 0.0450987 | -0.2157098 | 0.2999010 |
| change_animals | 65 | 0.0710922 | -0.1758561 | 0.3096227 |
| change_uds3ef | 65 | -0.1644864 | -0.3926338 | 0.0827318 |
| change_trailsb_ratio | 65 | -0.0976619 | -0.3336145 | 0.1498055 |
| change_test3meanZ | 65 | -0.2805350 | -0.4908491 | -0.0393267 |
| change_test5meanZ | 65 | -0.3371548 | -0.5369034 | -0.1016114 |
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 = -2.7997, df = 134, p-value = 0.005871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38805902 -0.06949983
## sample estimates:
## cor
## -0.2350825## [1] "Standardized correlation"##
## Pearson's product-moment correlation
##
## data: df.smartphone.other$change_z and df.smartphone.other$composite5_z
## t = -2.5607, df = 134, p-value = 0.01155
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.37083874 -0.04945399
## sample estimates:
## cor
## -0.2159888