Closed ld-archer closed 1 year ago
Original Models:
[1] "Minimal model:"
Call:
polr(formula = form.sociso.min, data = trans.sociso, na.action = na.omit,
Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 -0.01243 0.013820 -0.8994
scale(l2age65l) 0.02401 0.008589 2.7949
scale(l2age6574) 0.02716 0.010226 2.6564
scale(l2age75p) 0.11449 0.008731 13.1135
Intercepts:
Value Std. Error t value
1|2 -1.1637 0.0106 -109.6257
2|3 0.4543 0.0097 47.0175
3|4 1.8360 0.0124 147.8766
4|5 3.4150 0.0222 153.5984
5|6 5.7912 0.0684 84.6463
Residual Deviance: 199639.28
AIC: 199657.28
(13692 observations deleted due to missingness)
[1] "Full model:"
Call:
polr(formula = form.sociso, data = trans.sociso, na.action = na.omit,
Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 0.059588 0.01701 3.5023
white 0.583900 0.05070 11.5162
hsless1 0.113583 0.02076 5.4705
college1 -0.109118 0.02227 -4.8999
scale(l2age65l) 0.005164 0.01034 0.4995
scale(l2age6574) -0.035266 0.01409 -2.5034
scale(l2age75p) 0.190671 0.01473 12.9485
scale(atotb) -0.124004 0.01043 -11.8889
l2employed1 -0.047135 0.02499 -1.8862
l2inactive1 -0.060840 0.03277 -1.8566
l2physact1 0.004839 0.02354 0.2056
Intercepts:
Value Std. Error t value
1|2 -0.6125 0.0579 -10.5740
2|3 1.0344 0.0580 17.8213
3|4 2.4908 0.0591 42.1649
4|5 4.1335 0.0640 64.6348
5|6 6.7321 0.1150 58.5154
Residual Deviance: 134113.41
AIC: 134145.41
(36059 observations deleted due to missingness)
The AIC of both models suggest they are pretty poor. Thinking about the loneliness model, the minimal is roughly AIC==90,000 and final improved model around 30,000. The starting point for social isolation is therefore quite a lot worse.
[1] "Test model:"
Call:
polr(formula = form.sociso.test, data = trans, na.action = na.omit,
Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 0.342027 0.02141 15.9777
hsless1 0.016901 0.02687 0.6290
college1 -0.219189 0.02641 -8.3005
scale(l2age65l) -0.008506 0.01451 -0.5861
scale(l2age6574) -0.035257 0.01712 -2.0599
scale(l2age75p) 0.050592 0.01913 2.6451
l2cohab 0.248150 0.04986 4.9767
l2widowed 1.638206 0.03955 41.4230
l2single 1.820052 0.03619 50.2926
l2employed1 0.068507 0.03065 2.2354
l2inactive1 0.061962 0.04252 1.4571
l2physact1 0.102853 0.02925 3.5159
l2anyadl -0.015019 0.03888 -0.3863
l2anyiadl 0.034461 0.03954 0.8716
l2srh5 0.140513 0.07288 1.9281
l2psyche 0.076192 0.03604 2.1142
l2hhres -0.131753 0.01523 -8.6534
l2gcareinhh1w 0.221444 0.04533 4.8856
childless -1.367005 0.03059 -44.6820
scale(l2logatotb) -0.116626 0.01324 -8.8095
Intercepts:
Value Std. Error t value
1|2 -2.1650 0.0566 -38.2580
2|3 -0.1502 0.0556 -2.7007
3|4 1.6512 0.0567 29.1361
4|5 3.5319 0.0645 54.7422
5|6 6.2361 0.1391 44.8437
Residual Deviance: 83509.54
AIC: 83559.54
(53417 observations deleted due to missingness)
Vast improvement but we can probably still do better with help from literature. Also not sure if I should have the marital status variables in here as married or cohabiting is one of the elements of the social isolation index.
Another great review paper to work with - Ejiri et al. (2021).
Variables I can include that I haven't already:
NOTE: Both self-rated eyesight and hearing is assessed in 5 level likert scale, with higher values for worse outcomes (5 = poor).
[1] "Test model:"
Call:
polr(formula = form.sociso.test, data = trans, na.action = na.omit,
Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 0.3494362 0.021866 15.98091
hsless1 0.0032698 0.026960 0.12128
college1 -0.1965958 0.026630 -7.38259
scale(l2age65l) -0.0002969 0.014634 -0.02029
scale(l2age6574) -0.0337172 0.017173 -1.96342
scale(l2age75p) 0.0518324 0.019224 2.69626
l2cohab 0.2545730 0.050039 5.08746
l2widowed 1.6404089 0.039956 41.05543
l2single 1.8278257 0.036405 50.20851
l2employed1 0.0658869 0.030708 2.14557
l2inactive1 0.0636147 0.042672 1.49080
l2physact1 0.0914745 0.029331 3.11871
l2anyadl -0.0380546 0.039139 -0.97229
l2anyiadl 0.0024966 0.039864 0.06263
l2srh5 0.0802218 0.073742 1.08787
l2hhres -0.1339939 0.015285 -8.76612
l2gcareinhh1w 0.2010271 0.045473 4.42080
childless -1.3839590 0.030697 -45.08501
scale(l2logatotb) -0.2061006 0.018734 -11.00160
l2cesd 0.0348906 0.006751 5.16857
l2sight 0.0326863 0.012002 2.72350
l2hearing 0.0096480 0.010334 0.93359
l2ahown 0.3743588 0.051814 7.22502
Intercepts:
Value Std. Error t value
1|2 -1.7446 0.0776 -22.4773
2|3 0.2758 0.0773 3.5697
3|4 2.0819 0.0782 26.6133
4|5 3.9662 0.0843 47.0717
5|6 6.6666 0.1493 44.6603
Residual Deviance: 83093.42
AIC: 83149.42
(53538 observations deleted due to missingness)
Only a very small improvement in AIC, but each variable is an improvement nonetheless.
Decided to remove the ADL variables, as well as the work status vars. I think the important ADL characteristics will be captured by sight and hearing, and removing them here allows me to use social isolation as a predictor of disability with no statistical baggage. Also removing employment status as its not mentioned in the literature, and the t values were relatively small. Removing these groups of variables has also slightly improved the AIC, which is a function of the variance captured by the predictors as well as the number of predictors included. If two sets of predictors capture the same variance, but one set is larger than the other, the smallest set will have the best AIC. I think this means we don't lose anything important by removing these variables.
[1] "Test model:"
Call:
polr(formula = form.sociso.test, data = trans, na.action = na.omit,
Hess = TRUE)
Coefficients:
Value Std. Error t value
male1 0.350109 0.021610 16.2012
hsless1 0.006486 0.026909 0.2410
college1 -0.200121 0.026569 -7.5322
scale(l2age65l) -0.010262 0.013950 -0.7356
scale(l2age6574) -0.047472 0.016121 -2.9447
scale(l2age75p) 0.052477 0.019153 2.7398
l2cohab 0.255329 0.050031 5.1034
l2widowed 1.639766 0.039930 41.0656
l2single 1.830148 0.036371 50.3189
l2physact1 0.071256 0.027129 2.6266
l2srh5 0.069969 0.072283 0.9680
l2hhres -0.131765 0.015254 -8.6379
l2gcareinhh1w 0.202383 0.045284 4.4692
childless -1.380075 0.030646 -45.0332
scale(l2logatotb) -0.206340 0.018713 -11.0268
l2cesd 0.034322 0.006655 5.1576
l2sight 0.032706 0.011982 2.7296
l2hearing 0.009218 0.010314 0.8937
l2ahown 0.373749 0.051790 7.2166
Intercepts:
Value Std. Error t value
1|2 -1.7818 0.0756 -23.5728
2|3 0.2382 0.0752 3.1691
3|4 2.0443 0.0762 26.8368
4|5 3.9282 0.0823 47.7063
5|6 6.6286 0.1482 44.7270
Residual Deviance: 83099.77
AIC: 83147.77
(53538 observations deleted due to missingness)
STATA:
oprobit
sociso
male 0.21968
white 0.25673
hsless 0.00679
college -0.10861
l2age65l -0.00090
l2age6574 -0.00591
l2age75p 0.00756
l2single 1.08313
l2cohab 0.15157
l2widowed 0.98442
l2physact 0.04232
l2srh5 0.03097
l2logatotb -0.06477
l2hhres -0.06215
l2gcareinhh1w 0.11556
childless -0.75421
l2cesd 0.02187
l2sight 0.02116
l2hearing 0.00579
l2ahown 0.21098
cut1 -1.55090
cut2 -0.33335
cut3 0.68473
cut4 1.65379
cut5 2.82138
Now test run and look at outcomes again.
Outcomes with new social isolation model:
#### Life Years
[1] "No Loneliness"
[1] "Cohort Average Lifeyears: 41.59"
[1] "Intervention Average Lifeyears: 43.68"
[1] "Increase: 2.085"
[1] "No Social Isolation"
[1] "Cohort Average Lifeyears: 41.59"
[1] "Intervention Average Lifeyears: 42.87"
[1] "Increase: 1.279"
[1] "No Loneliness OR Social Isolation"
[1] "Cohort Average Lifeyears: 41.59"
[1] "Intervention Average Lifeyears: 44.83"
[1] "Increase: 3.238"
#### Disability Free Life Years
[1] "No Loneliness"
[1] "Cohort Average Disability Free Lifeyears: 14.79"
[1] "Intervention Average Disability Free Lifeyears: 15.99"
[1] "Increase: 1.206"
[1] "No Social Isolation"
[1] "Cohort Average Disability Free Lifeyears: 14.79"
[1] "Intervention Average Disability Free Lifeyears: 15.86"
[1] "Increase: 1.071"
[1] "No Loneliness OR Social Isolation"
[1] "Cohort Average Disability Free Lifeyears: 14.79"
[1] "Intervention Average Disability Free Lifeyears: 17.06"
[1] "Increase: 2.275"
#### Disease Free Life Years
[1] "No Loneliness"
[1] "Cohort Average Disease Free Lifeyears: 5.151"
[1] "Intervention Average Disease Free Lifeyears: 5.392"
[1] "Increase: 0.2411"
[1] "No Social Isolation"
[1] "Cohort Average Disease Free Lifeyears: 5.151"
[1] "Intervention Average Disease Free Lifeyears: 5.312"
[1] "Increase: 0.1613"
[1] "No Loneliness OR Social Isolation"
[1] "Cohort Average Disease Free Lifeyears: 5.151"
[1] "Intervention Average Disease Free Lifeyears: 5.552"
[1] "Increase: 0.4019"
I'm happy with this model now, some variables to add to another issue to make fully dynamic (cesd (depression), sight, hearing, ahown (home ownership)) but this won't change the model.
A good place to start would be the predictors of loneliness from #116 .
However some of those variable can not be used as they will be too strongly correlated with the outcome, specifically thinking of socyr which is whether respondent takes part in social activities.