Open ld-archer opened 1 year ago
MINIMAL
Call:
polr(formula = form.adlstat.min, data = trans.adlstat.complete,
na.action = na.omit, Hess = TRUE)
Coefficients:
Value Std. Error t value
male -0.07345 0.02237 -3.284
scale(l2age65l) 0.15987 0.01683 9.500
scale(l2age6574) 0.17675 0.01636 10.804
scale(l2age75p) 0.24362 0.01111 21.936
Intercepts:
Value Std. Error t value
1|2 1.6048 0.0150 106.8095
2|3 2.4600 0.0183 134.7670
3|4 3.1453 0.0227 138.7306
Residual Deviance: 72608.87
AIC: 72622.87
CURRENT FULL
Call:
polr(formula = form.adlstat, data = trans.adlstat.complete, na.action = na.omit,
Hess = TRUE)
Coefficients:
Value Std. Error t value
male 0.15078 0.02377 6.3438
white -0.05396 0.07719 -0.6991
hsless 0.34579 0.02468 14.0121
college -0.46398 0.03885 -11.9417
scale(l2age65l) 0.13222 0.01724 7.6706
scale(l2age6574) 0.10541 0.01709 6.1668
scale(l2age75p) 0.15808 0.01174 13.4624
l2stroke 0.83925 0.04676 17.9471
l2demene 0.72275 0.13062 5.5333
l2alzhe 0.23433 0.30621 0.7652
l2osteoe 0.78229 0.03907 20.0247
l2catracte 0.24655 0.02880 8.5604
l2lnly 0.61535 0.01858 33.1175
l2sociso 0.10714 0.01083 9.8933
Intercepts:
Value Std. Error t value
1|2 3.0008 0.0871 34.4692
2|3 3.8987 0.0880 44.3010
3|4 4.6104 0.0892 51.6730
Residual Deviance: 69652.86
AIC: 69686.86
Call:
polr(formula = form.adlstat.test, data = trans.adlstat.complete,
na.action = na.omit, Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 0.19947 0.028203 7.072
white 0.35560 0.093918 3.786
hsless1 0.10398 0.028453 3.654
college1 -0.25685 0.043310 -5.930
scale(l2age65l) 0.17605 0.019377 9.086
scale(l2age6574) 0.10177 0.019171 5.309
scale(l2age75p) 0.12935 0.013509 9.575
l2stroke 0.66853 0.052934 12.629
l2demene 0.31991 0.147535 2.168
l2osteoe 0.63960 0.043882 14.575
l2catracte 0.22628 0.032269 7.012
l2lnly 0.22525 0.023103 9.750
l2sociso 0.06313 0.013488 4.680
l2cesd 0.25411 0.006592 38.545
l2married -0.07088 0.036872 -1.922
l2widowed -0.25370 0.044145 -5.747
l2alcfreq 0.08803 0.005912 14.889
l2smokev 0.28192 0.027398 10.290
l2tr20 -0.02492 0.004380 -5.689
l2verbf -0.01471 0.002258 -6.515
l2orient -0.15020 0.023956 -6.270
Intercepts:
Value Std. Error t value
1|2 2.4739 0.1520 16.2769
2|3 3.4209 0.1526 22.4139
3|4 4.1523 0.1534 27.0661
Residual Deviance: 56957.72
AIC: 57005.72
MINIMAL
Call:
polr(formula = form.iadlstat.min, data = trans.adlstat.complete,
na.action = na.omit, Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 -0.4381 0.02438 -17.972
scale(l2age65l) 0.1178 0.01720 6.847
scale(l2age6574) 0.1683 0.01749 9.620
scale(l2age75p) 0.3635 0.01194 30.456
Intercepts:
Value Std. Error t value
1|2 1.3786 0.0152 90.4114
2|3 2.2979 0.0189 121.8917
Residual Deviance: 57829.33
AIC: 57841.33
FULL
Call:
polr(formula = form.iadlstat, data = trans.adlstat.complete,
na.action = na.omit, Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 -0.24434 0.02578 -9.4795
white -0.22421 0.08286 -2.7060
hsless1 0.39176 0.02631 14.8892
college1 -0.42308 0.04140 -10.2206
scale(l2age65l) 0.08633 0.01767 4.8855
scale(l2age6574) 0.08793 0.01836 4.7884
scale(l2age75p) 0.29006 0.01266 22.9164
l2stroke 0.88063 0.05296 16.6284
l2demene 1.81877 0.15267 11.9129
l2alzhe 0.32237 0.44921 0.7176
l2osteoe 0.77297 0.04214 18.3414
l2catracte 0.26561 0.03109 8.5446
l2lnly 0.63754 0.01995 31.9551
l2sociso 0.11748 0.01164 10.0896
Intercepts:
Value Std. Error t value
1|2 2.6706 0.0931 28.6704
2|3 3.6460 0.0942 38.7074
Residual Deviance: 54941.98
AIC: 54973.98
Not much better than the minimal here. First try the variables for adlstat.
TEST
Call:
polr(formula = form.iadlstat.test, data = trans.adlstat.complete,
na.action = na.omit, Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 -0.18779 0.028246 -6.649
white 0.19249 0.087463 2.201
hsless1 0.11710 0.028059 4.173
college1 -0.19387 0.042852 -4.524
scale(l2age65l) 0.10475 0.018259 5.737
scale(l2age6574) 0.06249 0.019115 3.269
scale(l2age75p) 0.25638 0.013397 19.137
l2stroke 0.71522 0.054308 13.170
l2demene 1.51484 0.149865 10.108
l2osteoe 0.64856 0.043141 15.033
l2catracte 0.25180 0.031829 7.911
l2lnly 0.22718 0.022802 9.963
l2sociso 0.06503 0.013405 4.851
l2cesd 0.24986 0.006530 38.264
l2married -0.07061 0.036595 -1.930
l2widowed -0.09897 0.043007 -2.301
l2alcfreq 0.09799 0.005850 16.749
l2smokev 0.24002 0.026811 8.952
l2tr20 -0.03788 0.004325 -8.758
l2verbf -0.01937 0.002249 -8.614
l2orient -0.23852 0.023613 -10.101
Intercepts:
Value Std. Error t value
1|2 1.5627 0.1473 10.6054
2|3 2.5924 0.1478 17.5368
Residual Deviance: 52463.73
AIC: 52509.73
Happy enough.
Final step is to add in the cognitive score variables to the model and fit a model for each. Will use the same predictors for all to save time.
MINIMAL
Call:
lm(formula = form.tr20.min, data = trans.notDead, na.action = na.omit)
Residuals:
Min 1Q Median 3Q Max
-12.8418 -2.0782 0.1195 2.2348 11.9578
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.63778 0.01728 615.72 <2e-16 ***
male1 -0.71416 0.02620 -27.26 <2e-16 ***
scale(l2age65l) -0.20722 0.01632 -12.70 <2e-16 ***
scale(l2age6574) -0.86352 0.01955 -44.17 <2e-16 ***
scale(l2age75p) -0.87997 0.01768 -49.76 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.358 on 67225 degrees of freedom
(17207 observations deleted due to missingness)
Multiple R-squared: 0.1926, Adjusted R-squared: 0.1925
F-statistic: 4009 on 4 and 67225 DF, p-value: < 2.2e-16
FIRST TEST
Call:
lm(formula = form.tr20, data = trans.notDead, na.action = na.omit)
Residuals:
Min 1Q Median 3Q Max
-13.623 -2.033 0.153 2.155 11.296
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.08012 0.07365 123.29 <2e-16 ***
male1 -0.96817 0.02514 -38.51 <2e-16 ***
white 2.01643 0.07240 27.85 <2e-16 ***
hsless1 -1.67254 0.02884 -57.99 <2e-16 ***
college1 1.28525 0.03475 36.98 <2e-16 ***
scale(l2age65l) -0.16666 0.01556 -10.71 <2e-16 ***
scale(l2age6574) -0.71456 0.01869 -38.22 <2e-16 ***
scale(l2age75p) -0.82379 0.01684 -48.91 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.194 on 67221 degrees of freedom
(17208 observations deleted due to missingness)
Multiple R-squared: 0.2697, Adjusted R-squared: 0.2696
F-statistic: 3546 on 7 and 67221 DF, p-value: < 2.2e-16
Found this study that uses machine learning on ELSA - Ahmadzadeh et al. (2023)
NEW MODEL
Call:
lm(formula = form.tr20.test, data = trans.notDead, na.action = na.omit)
Residuals:
Min 1Q Median 3Q Max
-13.956 -1.951 0.124 2.109 11.240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.411752 0.098328 105.888 < 2e-16 ***
male1 -1.023308 0.026110 -39.192 < 2e-16 ***
white 1.740516 0.075391 23.086 < 2e-16 ***
hsless1 -1.418525 0.029640 -47.859 < 2e-16 ***
college1 1.127947 0.035068 32.165 < 2e-16 ***
scale(l2age65l) -0.168168 0.017106 -9.831 < 2e-16 ***
scale(l2age6574) -0.723499 0.020307 -35.628 < 2e-16 ***
scale(l2age75p) -0.749246 0.017245 -43.446 < 2e-16 ***
l2hearing -0.103211 0.012208 -8.455 < 2e-16 ***
l2sight -0.128511 0.013918 -9.233 < 2e-16 ***
l2adlstat -0.091436 0.022283 -4.103 4.08e-05 ***
l2iadlstat -0.337685 0.028064 -12.033 < 2e-16 ***
l2cesd -0.101329 0.007278 -13.923 < 2e-16 ***
l2smoken -0.415386 0.037615 -11.043 < 2e-16 ***
l2employed1 0.348560 0.044238 7.879 3.34e-15 ***
l2retired 0.321511 0.043801 7.340 2.16e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.126 on 63771 degrees of freedom
(20650 observations deleted due to missingness)
Multiple R-squared: 0.2884, Adjusted R-squared: 0.2882
F-statistic: 1723 on 15 and 63771 DF, p-value: < 2.2e-16
Fine for me.
adlstat
andiadlstat