TTC_Analyses_2.0
Emily Moraes
4/6/2021
- Summaries
- TTC Start and PTQ
- TTC Start and GAD_i (recorded at each exam)
- TTC Start and DEP_i (PHQ recorded at each exam)
- TTC Start and GAD_i by Prediction 2
- TTC Start and DEP_i by Prediction 2
- TTC Start and Prediction 1
- TTC Start and Prediction 1 (controlling for confidence)
- TTC Start and Prediction 2
- TTC Start and Prediction 2 (controlling for confidence)
- TTC Start and Prediction change
Summaries
Times
TTC Stats by Time Type
##
## Descriptive statistics by group
## group: ttc_Start
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3014 52.7 757.36 4 7.51 5.93 0 30114 30114 34.57 1273.98
## se
## X1 13.8
## ------------------------------------------------------------
## group: ttc_End
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3014 56.51 768.83 5 8.84 5.93 0 30115 30115 33.32 1201.7 14
## ------------------------------------------------------------
## group: TimetoComplete
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3014 3.81 108.71 1 0.61 1.48 0 5884 5884 52.69 2838.52 1.98
TTC_Start by Exam
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 820 24.3 91.07 4 7.54 5.93 0 1248 1248 9.11 101.91 3.18
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 898 71.89 1058.3 3 6.9 4.45 0 30114 30114 26.23 726.57
## se
## X1 35.32
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 853 32 141.44 5 8.3 7.41 0 1918 1918 9.13 92.64 4.84
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 443 106.26 1255.87 3 7.65 4.45 0 25833 25833 19.54 395
## se
## X1 59.67Symptom Measures
PTQ Scores
## df$PTQscore
## n missing distinct Info Mean Gmd .05 .10
## 2978 36 58 0.999 20.41 14.88 1 3
## .25 .50 .75 .90 .95
## 10 20 29 38 43
##
## lowest : 0 1 2 3 4, highest: 53 54 55 56 60
PTQ by Cohort
##
## Descriptive statistics by group
## group: 3000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 620 20.11 12.07 19 19.7 13.34 0 60 60 0.26 -0.71 0.48
## ------------------------------------------------------------
## group: 4000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 924 20.78 12.99 21 20.22 14.83 0 60 60 0.31 -0.52 0.43
## ------------------------------------------------------------
## group: 5000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1434 20.29 13.49 19 19.67 14.83 0 60 60 0.34 -0.58 0.36
PTQ by Exam
##
## Descriptive statistics by group
## group: 3000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 620 20.11 12.07 19 19.7 13.34 0 60 60 0.26 -0.71 0.48
## ------------------------------------------------------------
## group: 4000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 924 20.78 12.99 21 20.22 14.83 0 60 60 0.31 -0.52 0.43
## ------------------------------------------------------------
## group: 5000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1434 20.29 13.49 19 19.67 14.83 0 60 60 0.34 -0.58 0.36
GAD_i by Cohort
##
## Descriptive statistics by group
## group: 3000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 475 4.61 4.75 4 3.87 4.45 0 21 21 1.25 1.31 0.22
## ------------------------------------------------------------
## group: 4000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 458 5.57 5.26 4 4.82 4.45 0 21 21 1.08 0.5 0.25
## ------------------------------------------------------------
## group: 5000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 985 6.29 5.39 5 5.66 5.93 0 21 21 0.89 0.08 0.17
GAD_i by Exam
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 580 6.02 5 5 5.45 4.45 0 21 21 0.93 0.39 0.21
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 528 5.8 5.31 4 5.12 4.45 0 21 21 0.99 0.23 0.23
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 508 5.61 5.35 5 4.84 5.93 0 21 21 1.07 0.48 0.24
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 302 5.05 5.39 4 4.18 5.93 0 21 21 1.18 0.77 0.31
DEP_i by Cohort
##
## Descriptive statistics by group
## group: 3000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 475 4.92 5.04 4 4.17 5.93 0 24 24 1.11 0.79 0.23
## ------------------------------------------------------------
## group: 4000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 456 5.46 5.47 4 4.64 5.93 0 27 27 1.19 1.12 0.26
## ------------------------------------------------------------
## group: 5000
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 975 6 5.27 5 5.38 5.93 0 25 25 0.88 0.08 0.17
DEP_i by Exam
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 580 5.84 5.12 5 5.24 5.93 0 27 27 0.94 0.59 0.21
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 524 5.84 5.5 4 5.08 4.45 0 25 25 1.06 0.5 0.24
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 500 5.49 5.2 4 4.78 4.45 0 24 24 1.04 0.5 0.23
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 302 4.91 5.26 3 4.12 4.45 0 21 21 1.02 0.25 0.3
Predictions
Prediction 1
## df$Prediction_1
## n missing distinct Info Mean Gmd .05 .10
## 2538 476 69 0.988 72.51 17.69 40 50
## .25 .50 .75 .90 .95
## 65 75 85 90 92
##
## lowest : 0 2 3 5 6, highest: 95 96 97 98 100
Prediction 2
## df$Prediction_2
## n missing distinct Info Mean Gmd .05 .10
## 2773 241 80 0.989 69.29 18.61 34 50
## .25 .50 .75 .90 .95
## 60 70 80 90 90
##
## lowest : 0 1 2 3 5, highest: 95 96 97 98 100
Are Predictions 1 and 2 different?
##
## Paired t-test
##
## data: df$Prediction_1 and df$Prediction_2
## t = 17.642, df = 2536, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.441838 3.052526
## sample estimates:
## mean of the differences
## 2.747182All observations
TTC Start
## df$ttc_Start
## n missing distinct Info Mean Gmd .05 .10
## 3014 0 226 0.982 52.7 97.33 0 0
## .25 .50 .75 .90 .95
## 0 4 12 51 114
##
## lowest : 0 1 2 3 4, highest: 1998 4852 9298 25833 30114
##
## Value 0 500 1000 1500 2000 5000 9500 26000 30000
## Frequency 2948 40 10 10 2 1 1 1 1
## Proportion 0.978 0.013 0.003 0.003 0.001 0.000 0.000 0.000 0.000
##
## For the frequency table, variable is rounded to the nearest 500Cut off 1 (< 1,440 mins)
TTC Start
## ttcmod$ttc_Start
## n missing distinct Info Mean Gmd .05 .10
## 3002 0 214 0.982 25.16 42.65 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 4.0 12.0 48.0 106.9
##
## lowest : 0 1 2 3 4, highest: 1248 1256 1284 1354 1387TTC Start and PTQ
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ PTQscore + (1 | ID)
## Data: df
##
## REML criterion at convergence: 46485.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.221 -0.092 -0.049 -0.005 50.485
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0 0.0
## Residual 353379 594.5
## Number of obs: 2978, groups: ID, 919
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.234 20.225 2976.000 -0.259 0.79580
## PTQscore 2.298 0.835 2976.000 2.752 0.00595 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PTQscore -0.843
## convergence code: 0
## boundary (singular) fit: see ?isSingularAll obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ PTQ_C + (1 | ID)
## Data: df
##
## REML criterion at convergence: 10760.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7269 -0.6128 -0.1572 0.4601 6.0826
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7294 0.854
## Residual 1.6510 1.285
## Number of obs: 2978, groups: ID, 919
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.732e+00 3.711e-02 8.790e+02 46.665 <2e-16 ***
## PTQ_C 5.144e-03 2.821e-03 9.062e+02 1.823 0.0686 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PTQ_C -0.004Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ PTQ_C + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 35073.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0107 -0.2050 -0.1620 -0.0985 13.5779
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1080 32.87
## Residual 6991 83.61
## Number of obs: 2969, groups: ID, 918
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 23.6786 1.8959 899.2271 12.490 <2e-16 ***
## PTQ_C 0.2575 0.1449 934.2009 1.777 0.0759 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PTQ_C 0.001Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ PTQ_C + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 10548.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8641 -0.6176 -0.1580 0.4774 3.8414
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.729 0.8538
## Residual 1.533 1.2381
## Number of obs: 2969, groups: ID, 918
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.712e+00 3.659e-02 8.847e+02 46.776 <2e-16 ***
## PTQ_C 4.201e-03 2.784e-03 9.112e+02 1.509 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PTQ_C -0.003Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ PTQ_C + (1 | ID)
## Data: ttcmodz
##
## REML criterion at convergence: 36056.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4152 -0.1841 -0.1484 -0.0958 13.8797
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1646 40.57
## Residual 9461 97.27
## Number of obs: 2973, groups: ID, 918
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.7162 2.2510 903.2912 11.424 <2e-16 ***
## PTQ_C 0.2204 0.1720 937.3858 1.281 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PTQ_C 0.001TTC Start and GAD_i (recorded at each exam)
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + exam_num + cohort + (1 | ID)
## Data: df_g
##
## REML criterion at convergence: 26955.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4481 -0.0684 -0.0027 0.0418 14.3765
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 903640 950.60
## Residual 9974 99.87
## Number of obs: 1918, groups: ID, 728
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 219.9535 79.0922 724.4299 2.781 0.00556 **
## GAD_i -0.4416 0.9587 1219.1223 -0.461 0.64517
## exam_num2 0.2502 6.6852 1182.1331 0.037 0.97015
## exam_num3 8.7295 6.8010 1182.3764 1.284 0.19955
## exam_num4 10.1107 7.9778 1181.0175 1.267 0.20527
## cohort4000 -193.6228 101.4845 717.2259 -1.908 0.05680 .
## cohort5000 -200.2065 93.5285 715.9339 -2.141 0.03264 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.064
## exam_num2 -0.044 0.068
## exam_num3 -0.047 0.129 0.522
## exam_num4 -0.049 0.129 0.422 0.437
## cohort4000 -0.775 -0.007 0.008 0.009 0.019
## cohort5000 -0.839 -0.019 -0.006 -0.008 0.000 0.655All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ GAD_i + exam_num + cohort + (1 | ID)
## Data: df_g
##
## REML criterion at convergence: 6792.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1662 -0.6147 -0.2444 0.4695 4.6938
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5376 0.7332
## Residual 1.5803 1.2571
## Number of obs: 1918, groups: ID, 728
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.359e+00 1.042e-01 9.419e+02 22.645 < 2e-16 ***
## GAD_i 1.085e-02 7.039e-03 1.388e+03 1.541 0.12342
## exam_num2 -1.512e-01 7.869e-02 1.432e+03 -1.922 0.05486 .
## exam_num3 6.869e-02 7.993e-02 1.438e+03 0.859 0.39030
## exam_num4 6.273e-02 9.565e-02 1.378e+03 0.656 0.51202
## cohort4000 -3.787e-01 1.182e-01 6.948e+02 -3.205 0.00141 **
## cohort5000 -1.226e+00 1.032e-01 5.686e+02 -11.880 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.349
## exam_num2 -0.375 0.035
## exam_num3 -0.381 0.062 0.494
## exam_num4 -0.397 0.078 0.415 0.421
## cohort4000 -0.639 -0.044 0.066 0.077 0.186
## cohort5000 -0.620 -0.118 -0.041 -0.051 0.012 0.602Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + exam_num + cohort + (1 | ID)
## Data: ttcmod_g
##
## REML criterion at convergence: 23033.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9383 -0.2105 -0.1359 -0.0500 12.1830
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1801 42.44
## Residual 8420 91.76
## Number of obs: 1916, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 35.4916 7.0194 801.2941 5.056 5.3e-07 ***
## GAD_i 1.0175 0.4814 1110.6958 2.114 0.03477 *
## exam_num2 -6.3421 5.6937 1327.6859 -1.114 0.26553
## exam_num3 1.3914 5.7854 1338.3075 0.240 0.80998
## exam_num4 6.8152 6.9404 1264.9399 0.982 0.32631
## cohort4000 -15.2818 7.8413 544.6101 -1.949 0.05182 .
## cohort5000 -21.9029 6.7796 420.1642 -3.231 0.00133 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.354
## exam_num2 -0.403 0.032
## exam_num3 -0.407 0.055 0.490
## exam_num4 -0.423 0.073 0.415 0.420
## cohort4000 -0.625 -0.045 0.069 0.081 0.200
## cohort5000 -0.597 -0.121 -0.042 -0.052 0.012 0.593Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ GAD_i + exam_num + cohort + (1 | ID)
## Data: ttcmod_g
##
## REML criterion at convergence: 6732.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2076 -0.6179 -0.2436 0.4795 4.0473
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5322 0.7295
## Residual 1.5314 1.2375
## Number of obs: 1916, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.336e+00 1.031e-01 9.498e+02 22.656 < 2e-16 ***
## GAD_i 1.171e-02 6.953e-03 1.404e+03 1.685 0.09228 .
## exam_num2 -1.503e-01 7.750e-02 1.440e+03 -1.939 0.05264 .
## exam_num3 5.539e-02 7.877e-02 1.447e+03 0.703 0.48207
## exam_num4 4.612e-02 9.426e-02 1.385e+03 0.489 0.62475
## cohort4000 -3.578e-01 1.170e-01 7.063e+02 -3.059 0.00231 **
## cohort5000 -1.208e+00 1.023e-01 5.804e+02 -11.811 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.349
## exam_num2 -0.374 0.035
## exam_num3 -0.379 0.062 0.493
## exam_num4 -0.393 0.078 0.415 0.421
## cohort4000 -0.641 -0.044 0.065 0.077 0.184
## cohort5000 -0.622 -0.117 -0.041 -0.051 0.010 0.603Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + (1 | ID)
## Data: ttcmodz_g
##
## REML criterion at convergence: 23075.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0277 -0.1975 -0.1489 -0.0928 12.0529
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1876 43.32
## Residual 8418 91.75
## Number of obs: 1916, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 21.3794 3.8677 703.2155 5.528 4.57e-08 ***
## GAD_i 0.7808 0.4788 1121.4465 1.631 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## GAD_i -0.716## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + cohort + exam_num + (1 | ID)
## Data: ttcmodz_g
##
## REML criterion at convergence: 23033.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9383 -0.2105 -0.1359 -0.0500 12.1830
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1801 42.44
## Residual 8420 91.76
## Number of obs: 1916, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 35.4916 7.0194 801.2941 5.056 5.3e-07 ***
## GAD_i 1.0175 0.4814 1110.6958 2.114 0.03477 *
## cohort4000 -15.2818 7.8413 544.6101 -1.949 0.05182 .
## cohort5000 -21.9029 6.7796 420.1642 -3.231 0.00133 **
## exam_num2 -6.3421 5.6937 1327.6859 -1.114 0.26553
## exam_num3 1.3914 5.7854 1338.3075 0.240 0.80998
## exam_num4 6.8152 6.9404 1264.9399 0.982 0.32631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i ch4000 ch5000 exm_n2 exm_n3
## GAD_i -0.354
## cohort4000 -0.625 -0.045
## cohort5000 -0.597 -0.121 0.593
## exam_num2 -0.403 0.032 0.069 -0.042
## exam_num3 -0.407 0.055 0.081 -0.052 0.490
## exam_num4 -0.423 0.073 0.200 0.012 0.415 0.420AICs
AIC(mod_all)## [1] 26973.94AIC(mod_all_log)## [1] 6810.401AIC(mod_cut1)## [1] 23051.14AIC(mod_cut1_log)## [1] 6750.401AIC(mod_cut2_simple)## [1] 23083.09AIC(mod_cut2)## [1] 23051.14TTC Start and DEP_i (PHQ recorded at each exam)
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ DEP_i + cohort + exam_num + (1 | ID)
## Data: df_d
##
## REML criterion at convergence: 26791.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4585 -0.0733 -0.0028 0.0476 14.4304
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 905648 951.66
## Residual 9872 99.36
## Number of obs: 1906, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 214.3995 79.1860 723.8489 2.708 0.00694 **
## DEP_i 0.4695 0.9650 1209.5262 0.487 0.62664
## cohort4000 -192.4443 101.5931 716.3247 -1.894 0.05859 .
## cohort5000 -201.6099 93.6582 714.7891 -2.153 0.03168 *
## exam_num2 1.1936 6.6618 1171.1119 0.179 0.85784
## exam_num3 11.0084 6.7625 1170.8516 1.628 0.10382
## exam_num4 11.7559 7.9231 1169.9046 1.484 0.13815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.066
## cohort4000 -0.775 -0.004
## cohort5000 -0.839 -0.012 0.655
## exam_num2 -0.039 -0.003 0.009 -0.005
## exam_num3 -0.044 0.075 0.010 -0.006 0.514
## exam_num4 -0.048 0.110 0.019 0.001 0.413 0.431All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ DEP_i + cohort + exam_num + (1 | ID)
## Data: df_d
##
## REML criterion at convergence: 6751.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2270 -0.6154 -0.2444 0.4847 4.6755
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5362 0.7323
## Residual 1.5823 1.2579
## Number of obs: 1906, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.318e+00 1.046e-01 9.383e+02 22.168 < 2e-16 ***
## DEP_i 1.756e-02 7.006e-03 1.351e+03 2.506 0.01232 *
## cohort4000 -3.712e-01 1.182e-01 6.883e+02 -3.141 0.00175 **
## cohort5000 -1.223e+00 1.028e-01 5.599e+02 -11.895 < 2e-16 ***
## exam_num2 -1.559e-01 7.887e-02 1.420e+03 -1.977 0.04828 *
## exam_num3 8.258e-02 8.027e-02 1.420e+03 1.029 0.30375
## exam_num4 6.909e-02 9.569e-02 1.362e+03 0.722 0.47038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.358
## cohort4000 -0.645 -0.021
## cohort5000 -0.634 -0.076 0.600
## exam_num2 -0.364 0.006 0.067 -0.036
## exam_num3 -0.375 0.044 0.081 -0.045 0.489
## exam_num4 -0.395 0.072 0.189 0.016 0.412 0.419Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ DEP_i + cohort + exam_num + (1 | ID)
## Data: ttcmod_d
##
## REML criterion at convergence: 22896.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0221 -0.2120 -0.1334 -0.0445 12.1205
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1894 43.52
## Residual 8397 91.63
## Number of obs: 1904, groups: ID, 726
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.2143 7.0956 778.9413 4.822 1.71e-06 ***
## DEP_i 1.1946 0.4813 1073.6708 2.482 0.01321 *
## cohort4000 -14.5960 7.9089 525.8569 -1.846 0.06552 .
## cohort5000 -21.4672 6.8212 404.2270 -3.147 0.00177 **
## exam_num2 -6.4496 5.7004 1298.9035 -1.131 0.25809
## exam_num3 1.9068 5.8045 1303.5361 0.329 0.74259
## exam_num4 7.1189 6.9350 1230.4285 1.027 0.30485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.362
## cohort4000 -0.633 -0.020
## cohort5000 -0.614 -0.077 0.592
## exam_num2 -0.389 0.006 0.070 -0.037
## exam_num3 -0.399 0.041 0.084 -0.046 0.486
## exam_num4 -0.417 0.069 0.201 0.015 0.412 0.418Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ DEP_i + cohort + exam_num + (1 | ID)
## Data: ttcmod_d
##
## REML criterion at convergence: 6692.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2651 -0.6136 -0.2457 0.4895 4.0269
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5308 0.7286
## Residual 1.5335 1.2384
## Number of obs: 1904, groups: ID, 726
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.29857 0.10347 946.29403 22.215 < 2e-16 ***
## DEP_i 0.01774 0.00692 1367.15302 2.563 0.01047 *
## cohort4000 -0.34981 0.11696 699.34143 -2.991 0.00288 **
## cohort5000 -1.20377 0.10191 571.27032 -11.813 < 2e-16 ***
## exam_num2 -0.15529 0.07768 1428.13831 -1.999 0.04578 *
## exam_num3 0.06861 0.07910 1429.25964 0.867 0.38589
## exam_num4 0.05174 0.09430 1368.62315 0.549 0.58334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.358
## cohort4000 -0.646 -0.021
## cohort5000 -0.636 -0.075 0.601
## exam_num2 -0.363 0.006 0.067 -0.036
## exam_num3 -0.373 0.045 0.080 -0.045 0.489
## exam_num4 -0.391 0.072 0.186 0.014 0.412 0.418Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ DEP_i + cohort + exam_num + (1 | ID)
## Data: ttcmodz_d
##
## REML criterion at convergence: 22896.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0221 -0.2120 -0.1334 -0.0445 12.1205
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1894 43.52
## Residual 8397 91.63
## Number of obs: 1904, groups: ID, 726
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.2143 7.0956 778.9413 4.822 1.71e-06 ***
## DEP_i 1.1946 0.4813 1073.6708 2.482 0.01321 *
## cohort4000 -14.5960 7.9089 525.8569 -1.846 0.06552 .
## cohort5000 -21.4672 6.8212 404.2270 -3.147 0.00177 **
## exam_num2 -6.4496 5.7004 1298.9035 -1.131 0.25809
## exam_num3 1.9068 5.8045 1303.5361 0.329 0.74259
## exam_num4 7.1189 6.9350 1230.4285 1.027 0.30485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.362
## cohort4000 -0.633 -0.020
## cohort5000 -0.614 -0.077 0.592
## exam_num2 -0.389 0.006 0.070 -0.037
## exam_num3 -0.399 0.041 0.084 -0.046 0.486
## exam_num4 -0.417 0.069 0.201 0.015 0.412 0.418AICs
AIC(mod_all)## [1] 26809.48AIC(mod_all_log)## [1] 6769.784AIC(mod_cut1)## [1] 22914.92AIC(mod_cut1_log)## [1] 6710.157AIC(mod_cut2)## [1] 22914.92TTC Start and GAD_i by Prediction 2
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + Prediction_2 + exam_num + cohort + (1 | ID)
## Data: df_g
##
## REML criterion at convergence: 21431.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1382 -0.1877 -0.0962 -0.0077 11.0047
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4420 66.48
## Residual 6876 82.92
## Number of obs: 1783, groups: ID, 714
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.0818 14.1851 1327.0738 4.377 1.3e-05 ***
## GAD_i 0.8907 0.5482 1266.1969 1.625 0.1045
## Prediction_2 -0.3696 0.1599 1687.8382 -2.311 0.0210 *
## exam_num2 -2.9780 5.6159 1012.4710 -0.530 0.5960
## exam_num3 3.8157 5.7954 982.2799 0.658 0.5104
## exam_num4 9.1437 6.8333 925.0681 1.338 0.1812
## cohort4000 -15.7824 9.4187 400.0436 -1.676 0.0946 .
## cohort5000 -23.9761 8.4646 335.7663 -2.833 0.0049 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i Prdc_2 exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.313
## Predictin_2 -0.817 0.144
## exam_num2 -0.077 0.040 -0.153
## exam_num3 -0.248 0.106 0.047 0.515
## exam_num4 -0.075 0.086 -0.168 0.465 0.447
## cohort4000 -0.385 -0.053 0.006 0.063 0.091 0.160
## cohort5000 -0.457 -0.114 0.122 -0.088 -0.078 -0.046 0.603All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ GAD_i + Prediction_2 + exam_num + cohort + (1 |
## ID)
## Data: df_g
##
## REML criterion at convergence: 6237
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9755 -0.6093 -0.2311 0.4734 4.1402
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5678 0.7535
## Residual 1.4648 1.2103
## Number of obs: 1783, groups: ID, 714
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.696e+00 1.909e-01 1.456e+03 14.125 < 2e-16 ***
## GAD_i 7.368e-03 7.341e-03 1.334e+03 1.004 0.31572
## Prediction_2 -4.581e-03 2.181e-03 1.631e+03 -2.100 0.03588 *
## exam_num2 -1.448e-01 8.071e-02 1.354e+03 -1.795 0.07296 .
## exam_num3 9.522e-03 8.339e-02 1.335e+03 0.114 0.90910
## exam_num4 1.868e-02 9.861e-02 1.281e+03 0.189 0.84977
## cohort4000 -3.869e-01 1.199e-01 6.732e+02 -3.226 0.00132 **
## cohort5000 -1.226e+00 1.066e-01 5.585e+02 -11.500 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i Prdc_2 exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.322
## Predictin_2 -0.831 0.154
## exam_num2 -0.100 0.038 -0.140
## exam_num3 -0.264 0.099 0.050 0.511
## exam_num4 -0.092 0.080 -0.161 0.462 0.445
## cohort4000 -0.359 -0.055 0.004 0.068 0.100 0.182
## cohort5000 -0.430 -0.119 0.128 -0.093 -0.082 -0.047 0.589Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + Prediction_2 + exam_num + cohort + (1 | ID)
## Data: ttcmod_g
##
## REML criterion at convergence: 21431.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1382 -0.1877 -0.0962 -0.0077 11.0047
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4420 66.48
## Residual 6876 82.92
## Number of obs: 1783, groups: ID, 714
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.0818 14.1851 1327.0738 4.377 1.3e-05 ***
## GAD_i 0.8907 0.5482 1266.1969 1.625 0.1045
## Prediction_2 -0.3696 0.1599 1687.8382 -2.311 0.0210 *
## exam_num2 -2.9780 5.6159 1012.4710 -0.530 0.5960
## exam_num3 3.8157 5.7954 982.2799 0.658 0.5104
## exam_num4 9.1437 6.8333 925.0681 1.338 0.1812
## cohort4000 -15.7824 9.4187 400.0436 -1.676 0.0946 .
## cohort5000 -23.9761 8.4646 335.7663 -2.833 0.0049 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i Prdc_2 exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.313
## Predictin_2 -0.817 0.144
## exam_num2 -0.077 0.040 -0.153
## exam_num3 -0.248 0.106 0.047 0.515
## exam_num4 -0.075 0.086 -0.168 0.465 0.447
## cohort4000 -0.385 -0.053 0.006 0.063 0.091 0.160
## cohort5000 -0.457 -0.114 0.122 -0.088 -0.078 -0.046 0.603Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ GAD_i + Prediction_2 + exam_num + cohort + (1 |
## ID)
## Data: ttcmod_g
##
## REML criterion at convergence: 6237
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9755 -0.6093 -0.2311 0.4734 4.1402
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5678 0.7535
## Residual 1.4648 1.2103
## Number of obs: 1783, groups: ID, 714
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.696e+00 1.909e-01 1.456e+03 14.125 < 2e-16 ***
## GAD_i 7.368e-03 7.341e-03 1.334e+03 1.004 0.31572
## Prediction_2 -4.581e-03 2.181e-03 1.631e+03 -2.100 0.03588 *
## exam_num2 -1.448e-01 8.071e-02 1.354e+03 -1.795 0.07296 .
## exam_num3 9.522e-03 8.339e-02 1.335e+03 0.114 0.90910
## exam_num4 1.868e-02 9.861e-02 1.281e+03 0.189 0.84977
## cohort4000 -3.869e-01 1.199e-01 6.732e+02 -3.226 0.00132 **
## cohort5000 -1.226e+00 1.066e-01 5.585e+02 -11.500 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i Prdc_2 exm_n2 exm_n3 exm_n4 ch4000
## GAD_i -0.322
## Predictin_2 -0.831 0.154
## exam_num2 -0.100 0.038 -0.140
## exam_num3 -0.264 0.099 0.050 0.511
## exam_num4 -0.092 0.080 -0.161 0.462 0.445
## cohort4000 -0.359 -0.055 0.004 0.068 0.100 0.182
## cohort5000 -0.430 -0.119 0.128 -0.093 -0.082 -0.047 0.589Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + (1 | ID)
## Data: ttcmodz_g
##
## REML criterion at convergence: 23075.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0277 -0.1975 -0.1489 -0.0928 12.0529
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1876 43.32
## Residual 8418 91.75
## Number of obs: 1916, groups: ID, 727
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 21.3794 3.8677 703.2155 5.528 4.57e-08 ***
## GAD_i 0.7808 0.4788 1121.4465 1.631 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## GAD_i -0.716## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ GAD_i + Prediction_2 + cohort + exam_num + (1 | ID)
## Data: ttcmodz_g
##
## REML criterion at convergence: 21431.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1382 -0.1877 -0.0962 -0.0077 11.0047
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4420 66.48
## Residual 6876 82.92
## Number of obs: 1783, groups: ID, 714
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.0818 14.1851 1327.0738 4.377 1.3e-05 ***
## GAD_i 0.8907 0.5482 1266.1969 1.625 0.1045
## Prediction_2 -0.3696 0.1599 1687.8382 -2.311 0.0210 *
## cohort4000 -15.7824 9.4187 400.0436 -1.676 0.0946 .
## cohort5000 -23.9761 8.4646 335.7663 -2.833 0.0049 **
## exam_num2 -2.9780 5.6159 1012.4710 -0.530 0.5960
## exam_num3 3.8157 5.7954 982.2799 0.658 0.5104
## exam_num4 9.1437 6.8333 925.0681 1.338 0.1812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GAD_i Prdc_2 ch4000 ch5000 exm_n2 exm_n3
## GAD_i -0.313
## Predictin_2 -0.817 0.144
## cohort4000 -0.385 -0.053 0.006
## cohort5000 -0.457 -0.114 0.122 0.603
## exam_num2 -0.077 0.040 -0.153 0.063 -0.088
## exam_num3 -0.248 0.106 0.047 0.091 -0.078 0.515
## exam_num4 -0.075 0.086 -0.168 0.160 -0.046 0.465 0.447AICs
AIC(mod_all)## [1] 21451.66AIC(mod_all_log)## [1] 6257.043AIC(mod_cut1)## [1] 21451.66AIC(mod_cut1_log)## [1] 6257.043AIC(mod_cut2_simple)## [1] 23083.09AIC(mod_cut2)## [1] 21451.66TTC Start and DEP_i by Prediction 2
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ DEP_i + Prediction_2 + cohort + exam_num + (1 | ID)
## Data: df_d
##
## REML criterion at convergence: 21309.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0690 -0.1882 -0.0955 -0.0067 10.9747
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4409 66.40
## Residual 6929 83.24
## Number of obs: 1772, groups: ID, 713
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.6845 14.2830 1322.0019 4.179 3.12e-05 ***
## DEP_i 1.0943 0.5357 1260.3773 2.043 0.04127 *
## Prediction_2 -0.3552 0.1607 1676.6986 -2.210 0.02721 *
## cohort4000 -15.3307 9.4192 400.2588 -1.628 0.10440
## cohort5000 -23.2706 8.4348 334.1498 -2.759 0.00612 **
## exam_num2 -3.4052 5.6438 1009.3275 -0.603 0.54640
## exam_num3 3.8019 5.8212 964.1478 0.653 0.51385
## exam_num4 9.0692 6.8482 912.8676 1.324 0.18573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i Prdc_2 ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.323
## Predictin_2 -0.818 0.152
## cohort4000 -0.392 -0.022 0.010
## cohort5000 -0.475 -0.056 0.132 0.601
## exam_num2 -0.065 -0.002 -0.157 0.065 -0.083
## exam_num3 -0.240 0.069 0.046 0.096 -0.067 0.508
## exam_num4 -0.069 0.064 -0.171 0.164 -0.040 0.461 0.443All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ DEP_i + Prediction_2 + cohort + exam_num + (1 |
## ID)
## Data: df_d
##
## REML criterion at convergence: 6201.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9523 -0.6102 -0.2319 0.4757 4.1224
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5633 0.7506
## Residual 1.4693 1.2122
## Number of obs: 1772, groups: ID, 713
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.624e+00 1.918e-01 1.447e+03 13.685 < 2e-16 ***
## DEP_i 1.423e-02 7.158e-03 1.326e+03 1.988 0.04704 *
## Prediction_2 -4.140e-03 2.187e-03 1.621e+03 -1.893 0.05848 .
## cohort4000 -3.827e-01 1.197e-01 6.698e+02 -3.196 0.00146 **
## cohort5000 -1.222e+00 1.060e-01 5.537e+02 -11.526 < 2e-16 ***
## exam_num2 -1.508e-01 8.091e-02 1.347e+03 -1.864 0.06253 .
## exam_num3 2.179e-02 8.362e-02 1.316e+03 0.261 0.79442
## exam_num4 2.160e-02 9.863e-02 1.267e+03 0.219 0.82671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i Prdc_2 ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.332
## Predictin_2 -0.832 0.161
## cohort4000 -0.367 -0.023 0.009
## cohort5000 -0.450 -0.059 0.139 0.587
## exam_num2 -0.089 0.002 -0.144 0.070 -0.088
## exam_num3 -0.257 0.067 0.049 0.106 -0.072 0.505
## exam_num4 -0.086 0.062 -0.164 0.186 -0.042 0.459 0.441Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ DEP_i + Prediction_2 + cohort + exam_num + (1 | ID)
## Data: ttcmod_d
##
## REML criterion at convergence: 21309.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0690 -0.1882 -0.0955 -0.0067 10.9747
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4409 66.40
## Residual 6929 83.24
## Number of obs: 1772, groups: ID, 713
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.6845 14.2830 1322.0019 4.179 3.12e-05 ***
## DEP_i 1.0943 0.5357 1260.3773 2.043 0.04127 *
## Prediction_2 -0.3552 0.1607 1676.6986 -2.210 0.02721 *
## cohort4000 -15.3307 9.4192 400.2588 -1.628 0.10440
## cohort5000 -23.2706 8.4348 334.1498 -2.759 0.00612 **
## exam_num2 -3.4052 5.6438 1009.3275 -0.603 0.54640
## exam_num3 3.8019 5.8212 964.1478 0.653 0.51385
## exam_num4 9.0692 6.8482 912.8676 1.324 0.18573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i Prdc_2 ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.323
## Predictin_2 -0.818 0.152
## cohort4000 -0.392 -0.022 0.010
## cohort5000 -0.475 -0.056 0.132 0.601
## exam_num2 -0.065 -0.002 -0.157 0.065 -0.083
## exam_num3 -0.240 0.069 0.046 0.096 -0.067 0.508
## exam_num4 -0.069 0.064 -0.171 0.164 -0.040 0.461 0.443Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ DEP_i + Prediction_2 + cohort + exam_num + (1 |
## ID)
## Data: ttcmod_d
##
## REML criterion at convergence: 6201.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9523 -0.6102 -0.2319 0.4757 4.1224
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.5633 0.7506
## Residual 1.4693 1.2122
## Number of obs: 1772, groups: ID, 713
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.624e+00 1.918e-01 1.447e+03 13.685 < 2e-16 ***
## DEP_i 1.423e-02 7.158e-03 1.326e+03 1.988 0.04704 *
## Prediction_2 -4.140e-03 2.187e-03 1.621e+03 -1.893 0.05848 .
## cohort4000 -3.827e-01 1.197e-01 6.698e+02 -3.196 0.00146 **
## cohort5000 -1.222e+00 1.060e-01 5.537e+02 -11.526 < 2e-16 ***
## exam_num2 -1.508e-01 8.091e-02 1.347e+03 -1.864 0.06253 .
## exam_num3 2.179e-02 8.362e-02 1.316e+03 0.261 0.79442
## exam_num4 2.160e-02 9.863e-02 1.267e+03 0.219 0.82671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i Prdc_2 ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.332
## Predictin_2 -0.832 0.161
## cohort4000 -0.367 -0.023 0.009
## cohort5000 -0.450 -0.059 0.139 0.587
## exam_num2 -0.089 0.002 -0.144 0.070 -0.088
## exam_num3 -0.257 0.067 0.049 0.106 -0.072 0.505
## exam_num4 -0.086 0.062 -0.164 0.186 -0.042 0.459 0.441Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ DEP_i + Prediction_2 + cohort + exam_num + (1 | ID)
## Data: ttcmodz_d
##
## REML criterion at convergence: 21309.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0690 -0.1882 -0.0955 -0.0067 10.9747
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4409 66.40
## Residual 6929 83.24
## Number of obs: 1772, groups: ID, 713
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.6845 14.2830 1322.0019 4.179 3.12e-05 ***
## DEP_i 1.0943 0.5357 1260.3773 2.043 0.04127 *
## Prediction_2 -0.3552 0.1607 1676.6986 -2.210 0.02721 *
## cohort4000 -15.3307 9.4192 400.2588 -1.628 0.10440
## cohort5000 -23.2706 8.4348 334.1498 -2.759 0.00612 **
## exam_num2 -3.4052 5.6438 1009.3275 -0.603 0.54640
## exam_num3 3.8019 5.8212 964.1478 0.653 0.51385
## exam_num4 9.0692 6.8482 912.8676 1.324 0.18573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DEP_i Prdc_2 ch4000 ch5000 exm_n2 exm_n3
## DEP_i -0.323
## Predictin_2 -0.818 0.152
## cohort4000 -0.392 -0.022 0.010
## cohort5000 -0.475 -0.056 0.132 0.601
## exam_num2 -0.065 -0.002 -0.157 0.065 -0.083
## exam_num3 -0.240 0.069 0.046 0.096 -0.067 0.508
## exam_num4 -0.069 0.064 -0.171 0.164 -0.040 0.461 0.443AICs
AIC(mod_all)## [1] 21329.57AIC(mod_all_log)## [1] 6221.205AIC(mod_cut1)## [1] 21329.57AIC(mod_cut1_log)## [1] 6221.205AIC(mod_cut2)## [1] 21329.57TTC Start and Prediction 1
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_1 + (1 | ID)
## Data: df
##
## REML criterion at convergence: 39714.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.109 -0.061 -0.053 -0.035 49.597
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 299.3 17.3
## Residual 366999.4 605.8
## Number of obs: 2538, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.1393 53.4076 2155.8490 0.096 0.923
## Prediction_1 0.4258 0.7176 2142.9259 0.593 0.553
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_1 -0.974All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_1 + (1 | ID)
## Data: df
##
## REML criterion at convergence: 9033.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8354 -0.6038 -0.1375 0.4591 6.3639
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7085 0.8417
## Residual 1.5344 1.2387
## Number of obs: 2538, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.754e+00 1.368e-01 2.382e+03 12.820 <2e-16 ***
## Prediction_1 -1.033e-03 1.831e-03 2.443e+03 -0.564 0.573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_1 -0.961Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_1 + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 29830.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2894 -0.1953 -0.1414 -0.0790 13.3815
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1392 37.31
## Residual 6435 80.22
## Number of obs: 2534, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.4579 8.1201 2224.0079 5.106 3.58e-07 ***
## Prediction_1 -0.2624 0.1090 2259.1388 -2.406 0.0162 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_1 -0.967Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_1 + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 8929.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9271 -0.6128 -0.1415 0.4716 3.9560
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7144 0.8452
## Residual 1.4650 1.2104
## Number of obs: 2534, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.750e+00 1.347e-01 2.391e+03 12.989 <2e-16 ***
## Prediction_1 -1.113e-03 1.802e-03 2.452e+03 -0.617 0.537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_1 -0.960Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_1 + (1 | ID)
## Data: ttcmodz
##
## REML criterion at convergence: 30629.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0456 -0.1907 -0.1416 -0.0848 14.2807
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1429 37.81
## Residual 9026 95.00
## Number of obs: 2537, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.9423 9.3374 2160.0583 5.027 5.38e-07 ***
## Prediction_1 -0.3137 0.1255 2184.3578 -2.500 0.0125 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_1 -0.969TTC Start and Prediction 1 (controlling for confidence)
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_1 + Prediction_1_confidence + (1 | ID)
## Data: df
##
## REML criterion at convergence: 39640.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.126 -0.064 -0.049 -0.029 49.524
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 493.4 22.21
## Residual 367611.4 606.31
## Number of obs: 2533, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -16.4236 62.6258 2055.1585 -0.262 0.793
## Prediction_1 0.4425 0.7221 2129.4239 0.613 0.540
## Prediction_1_confidence 0.3319 0.4961 2036.1465 0.669 0.504
##
## Correlation of Fixed Effects:
## (Intr) Prdc_1
## Predictin_1 -0.853
## Prdctn_1_cn -0.514 0.034All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_1 + Prediction_1_confidence + (1 | ID)
## Data: df
##
## REML criterion at convergence: 9020.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8349 -0.6160 -0.1367 0.4627 6.4087
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7142 0.8451
## Residual 1.5267 1.2356
## Number of obs: 2533, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.873e+00 1.616e-01 2.364e+03 11.590 <2e-16 ***
## Prediction_1 -1.231e-03 1.840e-03 2.437e+03 -0.669 0.503
## Prediction_1_confidence -1.767e-03 1.274e-03 2.432e+03 -1.387 0.166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_1
## Predictin_1 -0.844
## Prdctn_1_cn -0.527 0.054Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_1 + Prediction_1_confidence + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 29778.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2717 -0.1948 -0.1403 -0.0799 13.3761
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1391 37.30
## Residual 6451 80.32
## Number of obs: 2529, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.0301 9.5802 2156.6099 4.074 4.79e-05 ***
## Prediction_1 -0.2635 0.1097 2245.8162 -2.401 0.0164 *
## Prediction_1_confidence 0.0407 0.0759 2198.8953 0.536 0.5918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_1
## Predictin_1 -0.848
## Prdctn_1_cn -0.523 0.046Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_1 + Prediction_1_confidence + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 8914.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9418 -0.6239 -0.1428 0.4763 3.9572
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7212 0.8492
## Residual 1.4557 1.2065
## Number of obs: 2529, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.901e+00 1.591e-01 2.376e+03 11.952 <2e-16 ***
## Prediction_1 -1.348e-03 1.811e-03 2.447e+03 -0.745 0.4566
## Prediction_1_confidence -2.246e-03 1.254e-03 2.442e+03 -1.790 0.0736 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_1
## Predictin_1 -0.843
## Prdctn_1_cn -0.527 0.054Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_1 + Prediction_1_confidence + (1 | ID)
## Data: ttcmodz
##
## REML criterion at convergence: 30575.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0327 -0.1914 -0.1398 -0.0838 14.2376
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1430 37.82
## Residual 9042 95.09
## Number of obs: 2532, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.84194 11.01009 2076.60215 3.710 0.000213 ***
## Prediction_1 -0.31189 0.12625 2170.57257 -2.470 0.013575 *
## Prediction_1_confidence 0.09729 0.08719 2106.13564 1.116 0.264633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_1
## Predictin_1 -0.849
## Prdctn_1_cn -0.522 0.045TTC Start and Prediction 2
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_2 + (1 | ID)
## Data: df
##
## REML criterion at convergence: 9920.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8233 -0.6072 -0.1479 0.4600 6.2899
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7455 0.8634
## Residual 1.5624 1.2499
## Number of obs: 2773, groups: ID, 912
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.851e+00 1.251e-01 2.550e+03 14.797 <2e-16 ***
## Prediction_2 -2.117e-03 1.738e-03 2.655e+03 -1.218 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_2 -0.954Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_2 + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 32726
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6281 -0.1916 -0.1334 -0.0733 12.8535
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1733 41.63
## Residual 6677 81.72
## Number of obs: 2766, groups: ID, 911
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.6107 7.5891 2275.4459 5.615 2.21e-08 ***
## Prediction_2 -0.2766 0.1059 2369.8943 -2.611 0.00908 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_2 -0.961Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_2 + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 9756.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9288 -0.6156 -0.1491 0.4743 3.9445
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7374 0.8587
## Residual 1.4717 1.2131
## Number of obs: 2766, groups: ID, 911
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.836e+00 1.223e-01 2.560e+03 15.010 <2e-16 ***
## Prediction_2 -2.151e-03 1.698e-03 2.665e+03 -1.267 0.205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_2 -0.953Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_2 + (1 | ID)
## Data: ttcmodz
##
## REML criterion at convergence: 33685.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0455 -0.1766 -0.1276 -0.0765 13.6272
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2274 47.69
## Residual 9372 96.81
## Number of obs: 2770, groups: ID, 911
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.0262 8.9174 2307.4650 4.937 8.5e-07 ***
## Prediction_2 -0.2666 0.1245 2389.8709 -2.141 0.0324 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Predictin_2 -0.961TTC Start and Prediction 2 (controlling for confidence)
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_2 + Prediction_2_confidence + (1 | ID)
## Data: df
##
## REML criterion at convergence: 33600.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.106 -0.061 -0.043 -0.026 44.979
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0 0.0
## Residual 446722 668.4
## Number of obs: 2121, groups: ID, 729
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.061e+01 6.918e+01 2.118e+03 1.021 0.308
## Prediction_2 5.305e-03 8.259e-01 2.118e+03 0.006 0.995
## Prediction_2_confidence -5.962e-01 5.888e-01 2.118e+03 -1.013 0.311
##
## Correlation of Fixed Effects:
## (Intr) Prdc_2
## Predictin_2 -0.848
## Prdctn_2_cn -0.543 0.068
## convergence code: 0
## boundary (singular) fit: see ?isSingularAll obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_2 + Prediction_2_confidence + (1 | ID)
## Data: df
##
## REML criterion at convergence: 7567.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5982 -0.6379 -0.1526 0.4473 6.3130
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.6027 0.7763
## Residual 1.5996 1.2648
## Number of obs: 2121, groups: ID, 729
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.918e+00 1.628e-01 1.847e+03 11.778 <2e-16 ***
## Prediction_2 -3.368e-03 1.915e-03 1.973e+03 -1.759 0.0788 .
## Prediction_2_confidence -2.768e-03 1.417e-03 1.738e+03 -1.954 0.0509 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_2
## Predictin_2 -0.832
## Prdctn_2_cn -0.554 0.071Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_2 + Prediction_2_confidence + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 24846.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0227 -0.1797 -0.1151 -0.0548 13.4893
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1654 40.66
## Residual 5967 77.25
## Number of obs: 2118, groups: ID, 729
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 35.53532 9.57452 1663.65955 3.711 0.000213 ***
## Prediction_2 -0.30626 0.11295 1819.95217 -2.712 0.006761 **
## Prediction_2_confidence 0.09972 0.08307 1499.41761 1.200 0.230151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_2
## Predictin_2 -0.835
## Prdctn_2_cn -0.553 0.070Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ Prediction_2 + Prediction_2_confidence + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 7469.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7248 -0.6364 -0.1544 0.4621 3.9695
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.6223 0.7889
## Residual 1.5108 1.2292
## Number of obs: 2118, groups: ID, 729
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.889e+00 1.603e-01 1.869e+03 11.783 <2e-16 ***
## Prediction_2 -3.215e-03 1.882e-03 1.993e+03 -1.708 0.0877 .
## Prediction_2_confidence -2.602e-03 1.396e-03 1.769e+03 -1.863 0.0626 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_2
## Predictin_2 -0.831
## Prdctn_2_cn -0.555 0.071Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ Prediction_2 + Prediction_2_confidence + (1 | ID)
## Data: ttcmodz
##
## REML criterion at convergence: 25159.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7769 -0.1850 -0.1214 -0.0591 15.7424
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1408 37.52
## Residual 7213 84.93
## Number of obs: 2119, groups: ID, 729
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 35.20375 10.12268 1609.84737 3.478 0.000519 ***
## Prediction_2 -0.33299 0.11978 1747.99741 -2.780 0.005494 **
## Prediction_2_confidence 0.14589 0.08747 1433.61040 1.668 0.095557 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prdc_2
## Predictin_2 -0.839
## Prdctn_2_cn -0.551 0.070TTC Start and Prediction change
All observations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ I(Prediction_1 - Prediction_2) + (1 | ID)
## Data: df
##
## REML criterion at convergence: 39697.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.276 -0.058 -0.049 -0.033 49.585
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 342.3 18.5
## Residual 367008.7 605.8
## Number of obs: 2537, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.828 12.762 1242.785 2.494 0.0128 *
## I(Prediction_1 - Prediction_2) 1.526 1.535 2508.266 0.995 0.3201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(Pr_1-P_2) -0.330All obs (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ I(Prediction_1 - Prediction_2) + (1 | ID)
## Data: df
##
## REML criterion at convergence: 9028.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8407 -0.6038 -0.1352 0.4589 6.3446
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7052 0.8398
## Residual 1.5357 1.2392
## Number of obs: 2537, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.676e+00 3.928e-02 9.396e+02 42.666 <2e-16
## I(Prediction_1 - Prediction_2) 1.533e-03 3.591e-03 2.430e+03 0.427 0.669
##
## (Intercept) ***
## I(Prediction_1 - Prediction_2)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(Pr_1-P_2) -0.257Cut off 1 (< 1,440 mins)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ I(Prediction_1 - Prediction_2) + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 29823.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2356 -0.1880 -0.1497 -0.0923 13.2679
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1402 37.45
## Residual 6446 80.29
## Number of obs: 2533, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 22.0554 2.1497 955.5948 10.260 <2e-16
## I(Prediction_1 - Prediction_2) 0.1809 0.2218 2518.4852 0.815 0.415
##
## (Intercept) ***
## I(Prediction_1 - Prediction_2)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(Pr_1-P_2) -0.288Cut off 1 (log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start_log ~ I(Prediction_1 - Prediction_2) + (1 | ID)
## Data: ttcmod
##
## REML criterion at convergence: 8924.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9336 -0.6132 -0.1357 0.4736 3.9556
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7111 0.8433
## Residual 1.4663 1.2109
## Number of obs: 2533, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.666e+00 3.897e-02 9.388e+02 42.758 <2e-16
## I(Prediction_1 - Prediction_2) 1.574e-03 3.525e-03 2.418e+03 0.447 0.655
##
## (Intercept) ***
## I(Prediction_1 - Prediction_2)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(Pr_1-P_2) -0.255Cut off 2 (z <= 2.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ttc_Start ~ I(Prediction_1 - Prediction_2) + (1 | ID)
## Data: ttcmodz
##
## REML criterion at convergence: 30623.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9795 -0.1818 -0.1553 -0.1017 14.3060
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1434 37.87
## Residual 9050 95.13
## Number of obs: 2536, groups: ID, 895
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.416e+01 2.414e+00 9.333e+02 10.009 <2e-16
## I(Prediction_1 - Prediction_2) 5.851e-02 2.582e-01 2.532e+03 0.227 0.821
##
## (Intercept) ***
## I(Prediction_1 - Prediction_2)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(Pr_1-P_2) -0.297