| author | generator | title | viewport |
|---|---|---|---|
Rich Jones |
pandoc |
Joint Model |
width=device-width, initial-scale=1.0, user-scalable=yes |
Rich Jones
July 10 2023
This document summarizes analyses conducted for 2022 ΨMCA work group 3, focusing on the effect of blood sugar (HbA1c) in memory performance change over 10 years in Black or African-American participants the Health and Retirement Study (HRS).
The goal of this analysis was to estimate a joint model (a growth curve model with a survival model) to compare to a model that uses propensity score weighted analyses (Laura Gibbons is conducting those analyses). To build up to the joint model, I estimate: a (1) survival model in Stata to compare to a (2) survival model in Mplus, a (3) growth curve model, and finally a (4) joint model. Unconditional (i.e., no covariates) and conditional (i.e., with covariates) models are analyzed for all but the joint model (only a conditional joint model is estimated). The covariates used in the adjusted or conditional models are all of those factors that Laura used in generating the selection weights.
Missing data among the covariates were handled with multiple imputation. Laura Gibbons performed the imputation and provided the analytic data set. All analyses in this report report results based on 5 imputations. The descriptive table uses the non-imputed data.
This analysis uses HRS data, but does not use sampling weights.
HbA1c (the spline version) is significantly related to risk of death (HR 1.12 [1.05, 1.19] per unit difference in HbA1c spline) in the unconditional model and this effect is attenuated (HR 1.07) when controlling for covariates but remains significant, including in the joint model.
HbA1c spline is not significantly related to level (i.e., intercept) and slope (change over 10 years follow-up) in memory performance, and this is true in the unconditional, conditional, and joint model. The point estimate suggests lower memory performance with higher HbA1c, and faster (i.e., more negative) slope with higher HbA1c, but we cannot rule out that the true effect is no effect given the sample size (confidence intervals include 0). The effect of HbA1c in intercept is relatively unchanged between the unconditional, conditional, and joint model. The effect of HbA1c in slope is about a half of the magnitude in the unconditional and joint models relative to the unconditional model.
The conditional and joint models are not appreciably different. Therefore, conditioning the growth model on survival did not influence how we assess the relationship between HbA1c and memory performance and change.
Finally, the Cox model estimated with Stata and the continuous time survival model estimated with Mplus produce slightly different parameter estimates. I don't have an explanation for that, and therefore all of the results are somewhat tenuous.
- Overview
- Contents
- Analysis
- Summary Table
- Sample descriptive table
- Detailed results and program files
Using LG's file forsas230518.dta.
. * chose variables for analysis
. use "forsas230518.dta" , clear
. gen _mi_miss=0
. gen _mi_id=0
. mi extract 0, clear
. tempfile foo
. gen m=_Imputation_
. cap drop _mi_miss
. cap drop _mi_id
. cap drop _mi_m
. * keep if race==2 // Black or African-American (regardless of ethnicity)
. * keep if wg3awave~=. // if observed in our time window
. * covariates used in generating weights
. su ///
> hba1c wg3 gender ///
> ed3 /// i.ed3
> spl_age1-spl_age4 ///
> cses_index maxparented couple usborn yrenter fullret ///
> hi25 hinpova rbmi rshlt c095 c103 rcesd ///
> smoke3 /// i.smoke3
> rhibpe /*rhearte*/ radla riadla rgrossa rmobila rlgmusa ///
> riadlza rfinea rshltc rdrink rlunge rstroke rcancre ///
> rarthre rconde routpt rdentst rdrugs rhomcar rhsptim ///
> rnrstim rhspnit rdoctim rnrsnit rnhmliv hatotb ///
> hacohort /// i.hacohort
> apoe4 ///
> gender /// i.gender#c.age0 c.age0#c.wg3 i.gender#c.wg3 i.apoe4#c.wg3 i.ed3#c.wg3
> if missing(hba1c)~=1 & missing(rtr20)~=1
Variable │ Obs Mean Std. dev. Min Max
─────────────┼─────────────────────────────────────────────────────────
hba1c │ 36,906 6.166721 1.162206 3.57 14.33
wg3awave │ 36,906 1.949602 1.566848 0 5
gender │ 36,906 1.635181 .4813858 1 2
ed3 │ 36,906 .8405137 .6886004 0 2
spl_age1 │ 36,906 71.06194 8.938769 51 101
─────────────┼─────────────────────────────────────────────────────────
spl_age2 │ 36,906 6.081149 8.093625 0 52.25806
spl_age3 │ 36,906 1.329253 2.531165 0 18.13111
spl_age4 │ 36,906 .282179 .6961501 0 5.494277
cses_index │ 36,906 -.2334339 .8882616 -3.638843 2.164723
maxparented │ 36,183 8.926131 3.554733 -5.397321 18.08955
─────────────┼─────────────────────────────────────────────────────────
couple │ 36,906 .4186311 .4933414 0 1
usborn │ 36,901 .9364245 .2439985 0 1
yrenter │ 36,906 1996.124 4.931397 1992 2008
fullret │ 36,565 .6409408 .4797312 0 1
hi25 │ 36,906 12.77513 53.9055 -25 1544.871
─────────────┼─────────────────────────────────────────────────────────
hinpova │ 36,906 .2378475 .425771 0 1
rbmi │ 36,853 29.61023 6.554338 11.1389 75.8
rshlt │ 36,896 3.176518 1.024689 .6259943 5.979667
c095 │ 36,084 3.186569 .9853053 -.5024406 6.463416
c103 │ 36,092 2.719128 1.047947 -1.310774 6.284186
─────────────┼─────────────────────────────────────────────────────────
rcesd │ 36,902 1.730675 2.074737 -.9217504 8
smoke3 │ 36,873 .7077265 .6949428 0 2
rhibpe │ 36,896 .7885137 .408368 0 1
radla │ 36,899 .4878275 1.062353 -1.975416 5
riadla │ 36,899 .1779733 .5332721 -1.014224 3
─────────────┼─────────────────────────────────────────────────────────
rgrossa │ 36,899 .758243 1.277397 -1.741531 5
rmobila │ 36,898 1.490823 1.615261 -2.341007 5
rlgmusa │ 36,899 1.586925 1.413423 -1.726378 4
riadlza │ 36,899 .3978531 .9516359 -1.462543 5
rfinea │ 36,899 .2856681 .6161086 -1.584405 3
─────────────┼─────────────────────────────────────────────────────────
rshltc │ 36,881 .0477611 .9081867 -4 4
rdrink │ 36,906 .3604292 .4801315 0 1
rlunge │ 36,893 .0898544 .285977 0 1
rstroke │ 36,906 .1216062 .3268349 0 1
rcancre │ 36,881 .1626854 .3690834 0 1
─────────────┼─────────────────────────────────────────────────────────
rarthre │ 36,895 .6863532 .463981 0 1
rconde │ 36,906 2.598277 1.470277 0 8
routpt │ 36,880 .1545282 .3614592 0 1
rdentst │ 36,882 .4285288 .4948722 0 1
rdrugs │ 36,892 .8830912 .321316 0 1
─────────────┼─────────────────────────────────────────────────────────
rhomcar │ 36,874 .128356 .3344903 0 1
rhsptim │ 36,849 .5709714 1.268112 -2.319584 20
rnrstim │ 36,873 .0446255 .284066 -2.581517 7
rhspnit │ 36,813 2.712522 9.99831 -23.43909 270
rdoctim │ 36,315 10.78737 23.93209 -93.29469 900
─────────────┼─────────────────────────────────────────────────────────
rnrsnit │ 36,873 3.78105 42.80817 -131.2104 975
rnhmliv │ 6,151 .0055276 .0741478 0 1
hatotb │ 6,151 159308.1 489753.5 -342160 1.70e+07
hacohort │ 6,151 3.395708 1.084469 0 5
apoe4 │ 36,694 .3713414 .4831701 0 1
─────────────┼─────────────────────────────────────────────────────────
gender │ 36,906 1.635181 .4813858 1 2
.
. su age0 if wg3awave==0
Variable │ Obs Mean Std. dev. Min Max
─────────────┼─────────────────────────────────────────────────────────
age0 │ 8,820 60.84694 8.019928 50 88
. gen cage0=age0-`r(mean)'
. *'
. gen female=gender==2 if missing(gender)~=1
.
. gen int1 = cage0*female
. *# None of the wg3awave-interaction covariates are going
. *# to Rich's analysis because I am only taking baseline covariates
. gen int2 = cage0*wg3awave
. gen int3 = female*wg3awave
. gen int4 = apoe4*wg3awave
(212 missing values generated)
. gen int5 = (ed3==0)*wg3awave
. gen int6 = (ed3==2)*wg3awave
.
. gen lths = ed3==0
. gen gths = ed3==2
.
. local covlist "female lths gths "
. local covlist "`covlist' spl_age1-spl_age4"
. local covlist "`covlist' cses_index maxparented couple usborn"
. center yrenter if wg3awave==0
cyrenter generated as yrenter centered at 1995.846, with 0 missing values, the mean of the group defined by if wg3awave==0
. local covlist "`covlist' cyrenter fullret hi25 hinpova rbmi rshlt c095 c103 rcesd "
. gen pastsmk = smoke3==1 if missing(smoke3)~=1
(33 missing values generated)
. gen currsmk = smoke3==2 if missing(smoke3)~=1
(33 missing values generated)
. local covlist "`covlist' pastsmk currsmk"
. local covlist "`covlist' rhibpe "
. *** local covlist "`covlist' rhearte"
. local covlist "`covlist' radla riadla rgrossa rmobila rlgmusa"
. local covlist "`covlist' riadlza rfinea rshltc rdrink rlunge rstroke rcancre"
. local covlist "`covlist' rarthre rconde routpt rdentst rdrugs rhomcar rhsptim"
. local covlist "`covlist' rnrstim rhspnit rdoctim rnrsnit hatotb " // rnhmliv <- a constant at baseline
.
. /* hacohort is not imputed (always missing when _Imputation_>0)
> gen cohort01 = hacohort==1 | hacohort==0
> gen cohort2 = hacohort==2
> gen cohort4 = hacohort==4
> gen cohort5 = hacohort==5
>
> local covlist "`covlist' cohort01 cohort2 cohort4 cohort5"
> */
.
.
. local covlist "`covlist' apoe4 int1"
.
. ** Process data
.
. cap drop k
. gen k=1
.
. * forward fill hba1c
. gsort _Imputation_ id wg3awave
. egen iid=group(_Imputation_ id)
. xfill hba1c , i(iid)
.
. * everdead
. preserve
. keep _Imputation_ id diednext
. collapse (max) diednext , by(_Imputation_ id)
. rename diednext everdead
. * Everdead is only ever 0 and 1 in _Imputation_ 0
. keep if _Imputation_ == 0
(7,350 observations deleted)
. keep id everdead
. tempfile everdead
. save `everdead'
file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000003 saved as .dta format
. *'
. restore
. merge m:1 id using `everdead'
Result Number of obs
─────────────────────────────────────────
Not matched 0
Matched 36,906 (_merge==3)
─────────────────────────────────────────
. *'
. table _Imputation_ everdead
──────────┬─────────────
│ (max)
_Imputati │ diednext
on_ │ 0 1
──────────┼─────────────
0 │ 4,889 1,262
1 │ 4,889 1,262
2 │ 4,889 1,262
3 │ 4,889 1,262
4 │ 4,889 1,262
5 │ 4,889 1,262
──────────┴─────────────
.
.
. tempfile masterdata
. save `masterdata' , replace
(file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000004 not found)
file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000004 saved as .dta format
. *'
. * longitudinal data
. keep _Imputation_ id wg3awave rtr20 diednext everdead
. tempfile longdata_long
. save `longdata_long' , replace
(file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000005 not found)
file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000005 saved as .dta format
. *'
. reshape wide rtr20 diednext , i(_Imputation_ id everdead) j(wg3awave)
(j = 0 1 2 3 4 5)
Data Long -> Wide
─────────────────────────────────────────────────────────────────────────────
Number of observations 36,906 -> 8,820
Number of variables 6 -> 15
j variable (6 values) wg3awave -> (dropped)
xij variables:
rtr20 -> rtr200 rtr201 ... rtr205
diednext -> diednext0 diednext1 ... diednext5
─────────────────────────────────────────────────────────────────────────────
. tempfile longdata_wide
. save `longdata_wide'
file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000006 saved as .dta format
. *'
. * Covariates
. use `masterdata' , clear
. *'
. keep if wg3awave ==0
(28,086 observations deleted)
. keep _Imputation_ id hba1c `covset'
. *'
. tempfile covariates_baseline
. save `covariates_baseline'
file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000007 saved as .dta format
. * wide analysis file
. *'
. use `longdata_wide' , clear
. *'
. merge 1:1 _Imputation_ id using `covariates_baseline'
Result Number of obs
─────────────────────────────────────────
Not matched 0
Matched 8,820 (_merge==3)
─────────────────────────────────────────
.
. forvalues i=1/6 {
2. local j=`i'-1
3. rename diednext`j' dead`i'
4. *'
. }
. replace everdead=0 if everdead~=1
(0 real changes made)
.
. * person years of follow-up
. gen py = 14 if missing(everdead)~=1
. replace py = 11 if dead6==1
(22 real changes made)
. replace py = 9 if dead5==1
(76 real changes made)
. replace py = 7 if dead4==1
(65 real changes made)
. replace py = 5 if dead3==1
(81 real changes made)
. replace py = 3 if dead2==1
(82 real changes made)
. replace py = 1 if dead1==1
(89 real changes made)
.
.
. local knotis = 6.5
. gen a1cspline = 0 if missing(hba1c)~=1
. replace a1cspline = max(0,hba1c-`knotis')
(2,022 real changes made)
. *'
. plot a1cspline hba1c
7.83 +
| *
| *
| *
|
| *
a | *
1 | **
c | ***
s | **
p | ***
l | ***
i | ***
n | **
e | ***
| ***
| ***
| ****
| ***
| ****
0 + * ****************
+────────────────────────────────────────────────────────────────+
3.57 A1C at wg3 baseline 14.33
.
.
. local covset "a1cspline `covlist'"
.
. * In Mplus, we use a variable TIMECENSORED
. * to indicate whether or not an event occurred.
. * The tricky thing is, time is censored for people
. * who DID NOT experience the event, and it is
. * not censored for people who DID experience the event.
. gen tc=0 // initialize to 0
. replace tc=1 if everdead==0
(6,330 real changes made)
.
.
. * mi set
.
. tempfile foo
. gen m=_Imputation_
. cap drop _mi_miss
. cap drop _mi_id
. cap drop _mi_m
. save `foo'
file /var/folders/lq/w3m6z0dj41ngkbbc0204xb7m0000gp/T//S_00831.000008 saved as .dta format
. use `foo'
. mi import flong , m(m) id(id)
.
. cap drop _merge
. merge 1:1 id _Imputation_ using covariates.dta
(label YESNO already defined)
Result Number of obs
─────────────────────────────────────────
Not matched 0
Matched 8,820 (_merge==3)
─────────────────────────────────────────
.
. mi stset py, failure(everdead)
(regular variables age0 rahispan gender ed3 yrenter hacohort unregistered because not in m=0)
(imputed variables smoke3 rdiabe unregistered because not in m=0)
(448 m=0 obs now marked as incomplete)
(7350 values of regular variable hatotb in m>0 updated to match values in m=0)
(7350 values of regular variable rnhmliv in m>0 updated to match values in m=0)
Survival-time data settings
Failure event: everdead!=0 & everdead<.
Observed time interval: (0, py]
Exit on or before: failure
──────────────────────────────────────────────────────────────────────────
1,470 total observations
0 exclusions
──────────────────────────────────────────────────────────────────────────
1,470 observations remaining, representing
415 failures in single-record/single-failure data
16,891 total analysis time at risk and under observation
At risk from t = 0
Earliest observed entry t = 0
Last observed exit t = 14
. gen iid=_mi_id
. save usingdata.dta , replace
file usingdata.dta saved
.
. use usingdata.dta , clear
. * continuous time survival, multiply imputed data
. mi estimate : stcox a1cspline
Multiple-imputation estimates Imputations = 5
Cox regression: Breslow method for ties Number of obs = 1,470
Average RVI = 0.0000
Largest FMI = 0.0000
DF adjustment: Large sample DF: min = .
avg = .
max = .
Model F test: Equal FMI F( 1, .) = 8.74
Within VCE type: OIM Prob > F = 0.0031
─────────────┬────────────────────────────────────────────────────────────────
_t │ Coefficient Std. err. t P>|t| [95% conf. interval]
─────────────┼────────────────────────────────────────────────────────────────
a1cspline │ .1319719 .0446519 2.96 0.003 .0444557 .2194881
─────────────┴────────────────────────────────────────────────────────────────
. *'
. mat B=e(b_mi)
. mat B=B'
. mat list B
symmetric B[1,1]
r1
a1cspline .13197189
. di exp(B[1,1])
1.1410762
. include model1.do
. * See MPUG ex 6.20
. cd $cwd
/Users/rnj/Library/CloudStorage/Dropbox/FH/FH2022/WG3/PsyMCA-WG3
. use usingdata.dta , clear
. cap confirm file modelinp1.out
. if _rc~=0 {
. cap erase inpfiles.dat
. rdoc init inpfiles.dat
. * run individually but only for purposes of saving inputdatafile
. forvalues i=1/5 {
2. qui runmplus tc py a1cspline if _Imputation_ == `i' , /// `
> variable(SURVIVAL = py; TIMECENSORED = tc (0 = NOT 1 = RIGHT);) ///
> model(py ON a1cspline;) ///
> log(off) saveinp(model`i') savelog(model`i') ///`
> saveinputdatafile(inp`i') // `
3. r inp`i'.dat //`
4. }
. rdoc close
. cap erase modelinp1.inp
. rdoc init modelinp1.inp
. r DATA: FILE = inpfiles.dat ;
. r TYPE = imputation ;
. r VARIABLE: NAMES = tc py a1cspline ;
. r MISSING ARE ALL (-9999) ;
. r SURVIVAL = py ;
. r TIMECENSORED = tc (0 = NOT 1 = RIGHT) ;
. r MODEL: py ON a1cspline ;
. r
. rdoc close
. type modelinp1.inp
. !$mplus_path $cwd/modelinp1.inp
. }
. runmplus , po(modelinp1.out) log(off)
(359 missing values generated)
log suppressed
. mat E1 = r(estimate)
. mat SE1 = r(se)
. mat z1 = r(z)
. runmplus_show_output_segment modelinp1.out "MODEL RESULTS" 9
MODEL RESULTS
Two-Tailed Rate of
Estimate S.E. Est./S.E. P-Value Missing
PY ON
A1CSPLINE 0.111 0.032 3.500 0.000 0.000
. eme py_on_a1cspline , mat(E1)
matrix element py_on_a1cspline column 1 is -> 0.111 (returned as r(r1))
. di exp(`py_on_a1cspline')
1.1173949
.
. include model2.do
. cap confirm file model2mi.out
. if _rc~=0 {
. cap erase inpfiles.dat
. rdoc init inpfiles.dat
. * run individually but only for purposes of saving inputdatafile
. forvalues i=1/5 {
2. cap erase inp`i'.dat
3. runmplus rtr200-rtr205 a1cspline if _Imputation_==`i' , est(MLR) ///
> model(i s | rtr200@0 rtr201@2 rtr202@4 rtr203@6 rtr204@8 rtr205@10; ///
> i s on a1cspline ;) ///
> log(off) savelog(model2`i') saveinp(model2`i') saveinputdatafile(inp`i') //`
4. r inp`i'.dat //`
5. }
. rdoc close
. cap erase model2mi.inp
. filefilter model21.inp model2mi.inp , from("FILE = inp1.dat ;") to("FILE = inpfiles.dat ; \n TYPE = imputation ;")
. type model2mi.inp
. !$mplus_path $cwd/model2mi.inp
. }
. runmplus , po(model2mi.out) log(off)
(643 missing values generated)
log suppressed
. mat E2 = r(estimate)
. mat SE2 = r(se)
. mat z2 = r(z)
. runmplus_show_output_segment model2mi.out "MODEL RESULTS" 50
MODEL RESULTS
Two-Tailed Rate of
Estimate S.E. Est./S.E. P-Value Missing
I |
RTR200 1.000 0.000 999.000 999.000 0.000
RTR201 1.000 0.000 999.000 999.000 0.000
RTR202 1.000 0.000 999.000 999.000 0.000
RTR203 1.000 0.000 999.000 999.000 0.000
RTR204 1.000 0.000 999.000 999.000 0.000
RTR205 1.000 0.000 999.000 999.000 0.000
S |
RTR200 0.000 0.000 999.000 999.000 0.000
RTR201 2.000 0.000 999.000 999.000 0.000
RTR202 4.000 0.000 999.000 999.000 0.000
RTR203 6.000 0.000 999.000 999.000 0.000
RTR204 8.000 0.000 999.000 999.000 0.000
RTR205 10.000 0.000 999.000 999.000 0.000
I ON
A1CSPLINE -0.084 0.079 -1.062 0.288 0.000
S ON
A1CSPLINE -0.017 0.012 -1.333 0.183 0.000
S WITH
I 0.041 0.044 0.927 0.354 0.000
Intercepts
RTR200 0.000 0.000 999.000 999.000 0.000
RTR201 0.000 0.000 999.000 999.000 0.000
RTR202 0.000 0.000 999.000 999.000 0.000
RTR203 0.000 0.000 999.000 999.000 0.000
RTR204 0.000 0.000 999.000 999.000 0.000
RTR205 0.000 0.000 999.000 999.000 0.000
I 8.298 0.085 97.505 0.000 0.000
S -0.127 0.011 -11.670 0.000 0.000
Residual Variances
RTR200 4.243 0.326 13.007 0.000 0.000
RTR201 7.139 0.385 18.551 0.000 0.000
RTR202 4.078 0.245 16.658 0.000 0.000
RTR203 7.208 0.456 15.812 0.000 0.000
RTR204 4.681 0.330 14.191 0.000 0.000
RTR205 6.627 0.830 7.981 0.000 0.000
I 6.401 0.389 16.473 0.000 0.000
S 0.007 0.007 1.064 0.287 0.000
. eme i_on_a1cspline , mat(E2)
matrix element i_on_a1cspline column 1 is -> -0.084 (returned as r(r1))
. eme s_on_a1cspline , mat(E2)
matrix element s_on_a1cspline column 1 is -> -0.017 (returned as r(r1))
. di "`i_on_a1cspline'"
-0.084
. di "`s_on_a1cspline'"
-0.017
.
. include model3.do
. global covset "`covset'"
. * LGC model
. * only have obserations for wg3awave
. cd $cwd
/Users/rnj/Library/CloudStorage/Dropbox/FH/FH2022/WG3/PsyMCA-WG3
. use usingdata.dta , clear
. keep py tc rtr20* `covset' _Imputation_ //`
. local i=0
. foreach x of varlist $covset {
2. if "`x'"~="a1cspline" {
3. gen cov`++i' = `x'
4. qui distinct cov`i' //`
5. if `r(ndistinct)'>2 { //`
6. qui standardize cov`i' , replace //`
7. }
8. qui distinct cov`i' //`
9. if `r(ndistinct)'==2 { //'
10. qui center cov`i' , replace //`
11. }
12. local foo : var lab `x' //`
13. drop `x' //`
14. la var cov`i' "`x' `foo'" //
15. }
16. }
(190 missing values generated)
(1 missing value generated)
(143 missing values generated)
(13 missing values generated)
(1 missing value generated)
(1 missing value generated)
(9 missing values generated)
(9 missing values generated)
(2 missing values generated)
(13 missing values generated)
(2 missing values generated)
(4 missing values generated)
(2 missing values generated)
(3 missing values generated)
(1 missing value generated)
(1 missing value generated)
(2 missing values generated)
(9 missing values generated)
(1 missing value generated)
(14 missing values generated)
(86 missing values generated)
(1 missing value generated)
(60 missing values generated)
. local covrange "cov1-cov`i'"
.
. cap confirm file model3mi.out
. if _rc~=0 {
. cap erase inpfiles.dat
. rdoc init inpfiles.dat
. * run individually but only for purposes of saving inputdatafile
. forvalues i=1/5 {
2. cap erase inp`i'.dat
3. runmplus rtr200-rtr205 a1cspline `covrange' if _Imputation_==`i' , est(MLR) ///
> model(i s | rtr200@0 rtr201@2 rtr202@4 rtr203@6 rtr204@8 rtr205@10; i s on a1cspline `covrange';) ///
> log(off) savelog(model3`i') saveinp(model3`i') saveinputdatafile(inp`i') //`
4. r inp`i'.dat //`
5. }
. rdoc close
. cap erase model3mi.inp
. filefilter model31.inp model3mi.inp , from("FILE = inp1.dat ;") to("FILE = inpfiles.dat ; \n TYPE = imputation ;")
. type model3mi.inp
. !$mplus_path $cwd/model3mi.inp
. }
. runmplus , po(model3mi.out) log(off)
(6,491 missing values generated)
log suppressed
. mat E3 = r(estimate)
. mat SE3 = r(se)
. mat z3 = r(z)
. runmplus_show_output_segment model3mi.out "MODEL RESULTS" 150
MODEL RESULTS
Two-Tailed Rate of
Estimate S.E. Est./S.E. P-Value Missing
I |
RTR200 1.000 0.000 999.000 999.000 0.000
RTR201 1.000 0.000 999.000 999.000 0.000
RTR202 1.000 0.000 999.000 999.000 0.000
RTR203 1.000 0.000 999.000 999.000 0.000
RTR204 1.000 0.000 999.000 999.000 0.000
RTR205 1.000 0.000 999.000 999.000 0.000
S |
RTR200 0.000 0.000 999.000 999.000 0.000
RTR201 2.000 0.000 999.000 999.000 0.000
RTR202 4.000 0.000 999.000 999.000 0.000
RTR203 6.000 0.000 999.000 999.000 0.000
RTR204 8.000 0.000 999.000 999.000 0.000
RTR205 10.000 0.000 999.000 999.000 0.000
I ON
A1CSPLINE -0.091 0.073 -1.256 0.209 0.003
COV1 0.934 0.148 6.316 0.000 0.004
COV2 -0.823 0.159 -5.188 0.000 0.016
COV3 0.657 0.194 3.384 0.001 0.003
COV4 0.007 0.462 0.015 0.988 0.003
COV5 0.520 1.542 0.337 0.736 0.001
COV6 -2.419 2.328 -1.039 0.299 0.001
COV7 1.725 1.156 1.493 0.136 0.002
COV8 -0.075 0.094 -0.793 0.428 0.132
COV9 0.173 0.108 1.599 0.110 0.266
COV10 -0.021 0.141 -0.148 0.882 0.002
COV11 0.487 0.249 1.954 0.051 0.008
COV12 0.102 0.155 0.657 0.511 0.003
COV13 -0.167 0.167 -1.000 0.318 0.118
COV14 0.261 0.068 3.851 0.000 0.001
COV15 -0.618 0.168 -3.685 0.000 0.001
COV16 0.312 0.073 4.249 0.000 0.010
COV17 -0.348 0.101 -3.453 0.001 0.009
COV18 0.082 0.074 1.097 0.273 0.007
COV19 -0.013 0.069 -0.191 0.848 0.004
COV20 -0.305 0.081 -3.789 0.000 0.001
COV21 -0.070 0.144 -0.490 0.624 0.040
COV22 0.005 0.203 0.023 0.982 0.023
COV23 -0.105 0.195 -0.539 0.590 0.003
COV24 0.017 0.191 0.087 0.931 0.003
COV25 -0.086 0.132 -0.653 0.514 0.005
COV26 -0.043 0.235 -0.185 0.854 0.003
COV27 -0.174 0.167 -1.043 0.297 0.003
COV28 0.169 0.088 1.920 0.055 0.002
COV29 -0.250 0.151 -1.660 0.097 0.005
COV30 0.084 0.114 0.735 0.462 0.001
COV31 0.164 0.078 2.096 0.036 0.015
COV32 0.207 0.138 1.495 0.135 0.002
COV33 0.462 0.295 1.567 0.117 0.001
COV34 -0.435 0.248 -1.749 0.080 0.001
COV35 0.446 0.233 1.918 0.055 0.003
COV36 -0.072 0.181 -0.397 0.691 0.001
COV37 0.069 0.150 0.462 0.644 0.000
COV38 -0.273 0.172 -1.583 0.113 0.003
COV39 0.281 0.138 2.042 0.041 0.003
COV40 0.191 0.208 0.918 0.359 0.004
COV41 0.047 0.243 0.195 0.845 0.001
COV42 -0.036 0.079 -0.460 0.646 0.013
COV43 0.103 0.069 1.496 0.135 0.008
COV44 -0.085 0.062 -1.384 0.166 0.045
COV45 0.138 0.058 2.384 0.017 0.097
COV46 -0.093 0.056 -1.667 0.095 0.019
COV47 0.093 0.041 2.274 0.023 0.000
COV48 -0.174 0.136 -1.277 0.202 0.034
COV49 -0.716 0.347 -2.067 0.039 0.000
COV50 0.045 0.108 0.417 0.677 0.001
S ON
A1CSPLINE -0.007 0.011 -0.671 0.502 0.001
COV1 -0.017 0.024 -0.713 0.476 0.001
COV2 -0.012 0.024 -0.476 0.634 0.014
COV3 0.015 0.030 0.502 0.616 0.001
COV4 -0.049 0.072 -0.687 0.492 0.002
COV5 -0.095 0.231 -0.414 0.679 0.002
COV6 0.162 0.366 0.444 0.657 0.001
COV7 -0.080 0.193 -0.415 0.678 0.001
COV8 -0.011 0.015 -0.716 0.474 0.160
COV9 0.017 0.017 0.968 0.333 0.277
COV10 0.019 0.022 0.872 0.383 0.004
COV11 -0.077 0.041 -1.846 0.065 0.001
COV12 -0.016 0.024 -0.660 0.509 0.002
COV13 -0.042 0.027 -1.542 0.123 0.157
COV14 -0.025 0.009 -2.783 0.005 0.001
COV15 0.039 0.028 1.404 0.160 0.001
COV16 -0.007 0.011 -0.629 0.529 0.013
COV17 -0.006 0.015 -0.395 0.693 0.002
COV18 0.007 0.012 0.560 0.576 0.001
COV19 -0.008 0.011 -0.745 0.457 0.003
COV20 0.005 0.012 0.427 0.669 0.001
COV21 -0.019 0.022 -0.862 0.389 0.008
COV22 0.021 0.032 0.664 0.507 0.006
COV23 0.045 0.031 1.475 0.140 0.001
COV24 0.047 0.031 1.526 0.127 0.003
COV25 0.002 0.020 0.080 0.937 0.001
COV26 -0.043 0.039 -1.102 0.270 0.001
COV27 0.040 0.028 1.421 0.155 0.002
COV28 -0.007 0.014 -0.506 0.613 0.001
COV29 -0.018 0.024 -0.731 0.465 0.002
COV30 -0.013 0.018 -0.699 0.485 0.002
COV31 0.011 0.013 0.833 0.405 0.010
COV32 -0.033 0.021 -1.537 0.124 0.001
COV33 0.011 0.043 0.265 0.791 0.001
COV34 -0.007 0.042 -0.160 0.873 0.002
COV35 -0.036 0.035 -1.034 0.301 0.001
COV36 0.018 0.029 0.632 0.527 0.002
COV37 -0.028 0.023 -1.220 0.222 0.003
COV38 0.026 0.029 0.900 0.368 0.003
COV39 0.015 0.022 0.656 0.512 0.001
COV40 -0.014 0.033 -0.429 0.668 0.000
COV41 -0.045 0.045 -1.001 0.317 0.001
COV42 0.001 0.013 0.075 0.940 0.003
COV43 0.014 0.012 1.162 0.245 0.036
COV44 0.032 0.009 3.747 0.000 0.051
COV45 0.012 0.010 1.289 0.197 0.073
COV46 -0.005 0.008 -0.616 0.538 0.072
COV47 0.010 0.012 0.815 0.415 0.003
COV48 0.001 0.021 0.026 0.979 0.015
COV49 0.018 0.053 0.330 0.741 0.002
COV50 -0.034 0.020 -1.730 0.084 0.001
S WITH
I 0.037 0.034 1.108 0.268 0.001
Intercepts
RTR200 0.000 0.000 999.000 999.000 0.000
RTR201 0.000 0.000 999.000 999.000 0.000
RTR202 0.000 0.000 999.000 999.000 0.000
RTR203 0.000 0.000 999.000 999.000 0.000
RTR204 0.000 0.000 999.000 999.000 0.000
RTR205 0.000 0.000 999.000 999.000 0.000
I 7.561 0.410 18.442 0.000 0.007
S -0.086 0.066 -1.315 0.188 0.009
Residual Variances
RTR200 4.370 0.312 14.025 0.000 0.001
RTR201 6.933 0.356 19.461 0.000 0.000
RTR202 4.162 0.237 17.563 0.000 0.000
RTR203 6.955 0.428 16.239 0.000 0.001
RTR204 4.753 0.320 14.872 0.000 0.000
RTR205 6.616 0.752 8.798 0.000 0.001
I 2.568 0.263 9.767 0.000 0.001
S -0.004 0.006 -0.624 0.532 0.001
. eme i_on_a1cspline , mat(E3)
matrix element i_on_a1cspline column 1 is -> -0.091 (returned as r(r1))
. eme s_on_a1cspline , mat(E3)
matrix element s_on_a1cspline column 1 is -> -0.007 (returned as r(r1))
. di "`i_on_a1cspline'"
-0.091
. di "`s_on_a1cspline'"
-0.007
.
. include model4.do
. * Joint model
. cap confirm file model4mi.out
. if _rc~=0 {
. cap erase inpfiles.dat
. rdoc init inpfiles.dat
. * run individually but only for purposes of saving inputdatafile
. forvalues i=1/5 {
2. cap erase inp`i'.dat
3. runmplus tc py rtr200-rtr205 a1cspline `covrange' if _Imputation_==`i' , ///
> variable(SURVIVAL = py; TIMECENSORED = tc (0 = NOT 1 = RIGHT);) ///
> model(py on a1cspline `covrange' ; ///
> i s | rtr200@0 rtr201@2 rtr202@4 rtr203@6 rtr204@8 rtr205@10; ///
> i s on a1cspline `covrange'; ///
> py on i s; ) ///
> log(off) savelog(model4`i') saveinp(model4`i') saveinputdatafile(inp`i') //
4. r inp`i'.dat //`
5. }
. rdoc close
. cap erase model4mi.inp
. filefilter model41.inp model4mi.inp , from("FILE = inp1.dat ;") to("FILE = inpfiles.dat ; \n TYPE = imputation ;")
. type model4mi.inp
. !$mplus_path $cwd/model4mi.inp
. }
. runmplus , po(model4mi.out) log(off)
(6,686 missing values generated)
log suppressed
. mat E4 = r(estimate)
. mat SE4 = r(se)
. mat z4 = r(z)
. runmplus_show_output_segment model4mi.out "MODEL RESULTS" 205
MODEL RESULTS
Two-Tailed Rate of
Estimate S.E. Est./S.E. P-Value Missing
I |
RTR200 1.000 0.000 999.000 999.000 0.000
RTR201 1.000 0.000 999.000 999.000 0.000
RTR202 1.000 0.000 999.000 999.000 0.000
RTR203 1.000 0.000 999.000 999.000 0.000
RTR204 1.000 0.000 999.000 999.000 0.000
RTR205 1.000 0.000 999.000 999.000 0.000
S |
RTR200 0.000 0.000 999.000 999.000 0.000
RTR201 2.000 0.000 999.000 999.000 0.000
RTR202 4.000 0.000 999.000 999.000 0.000
RTR203 6.000 0.000 999.000 999.000 0.000
RTR204 8.000 0.000 999.000 999.000 0.000
RTR205 10.000 0.000 999.000 999.000 0.000
I ON
A1CSPLINE -0.089 0.073 -1.226 0.220 0.004
COV1 0.928 0.148 6.271 0.000 0.003
COV2 -0.823 0.158 -5.197 0.000 0.010
COV3 0.656 0.194 3.373 0.001 0.004
COV4 -0.001 0.463 -0.002 0.999 0.002
COV5 0.524 1.544 0.339 0.734 0.001
COV6 -2.414 2.329 -1.036 0.300 0.001
COV7 1.719 1.156 1.487 0.137 0.002
COV8 -0.075 0.095 -0.796 0.426 0.141
COV9 0.174 0.108 1.610 0.107 0.268
COV10 -0.020 0.141 -0.143 0.886 0.002
COV11 0.481 0.250 1.925 0.054 0.013
COV12 0.099 0.155 0.643 0.521 0.003
COV13 -0.161 0.167 -0.964 0.335 0.117
COV14 0.259 0.068 3.830 0.000 0.001
COV15 -0.615 0.168 -3.667 0.000 0.001
COV16 0.313 0.074 4.249 0.000 0.010
COV17 -0.352 0.101 -3.486 0.000 0.007
COV18 0.082 0.074 1.096 0.273 0.007
COV19 -0.013 0.069 -0.185 0.854 0.004
COV20 -0.304 0.081 -3.769 0.000 0.001
COV21 -0.068 0.144 -0.472 0.637 0.034
COV22 0.010 0.202 0.048 0.962 0.019
COV23 -0.105 0.195 -0.537 0.591 0.003
COV24 0.017 0.191 0.089 0.929 0.003
COV25 -0.089 0.132 -0.677 0.498 0.006
COV26 -0.047 0.235 -0.199 0.842 0.003
COV27 -0.171 0.167 -1.023 0.306 0.002
COV28 0.168 0.088 1.905 0.057 0.001
COV29 -0.247 0.151 -1.640 0.101 0.004
COV30 0.082 0.114 0.721 0.471 0.001
COV31 0.167 0.078 2.131 0.033 0.013
COV32 0.204 0.138 1.473 0.141 0.002
COV33 0.464 0.295 1.575 0.115 0.001
COV34 -0.443 0.248 -1.785 0.074 0.001
COV35 0.441 0.233 1.897 0.058 0.003
COV36 -0.079 0.181 -0.438 0.662 0.002
COV37 0.075 0.150 0.500 0.617 0.001
COV38 -0.271 0.172 -1.572 0.116 0.002
COV39 0.284 0.138 2.064 0.039 0.002
COV40 0.187 0.208 0.900 0.368 0.003
COV41 0.043 0.243 0.177 0.859 0.001
COV42 -0.038 0.079 -0.477 0.633 0.011
COV43 0.103 0.068 1.503 0.133 0.006
COV44 -0.085 0.062 -1.381 0.167 0.035
COV45 0.138 0.058 2.388 0.017 0.095
COV46 -0.095 0.055 -1.710 0.087 0.020
COV47 0.095 0.041 2.338 0.019 0.005
COV48 -0.180 0.136 -1.321 0.186 0.032
COV49 -0.713 0.347 -2.055 0.040 0.000
COV50 0.044 0.108 0.404 0.686 0.001
S ON
A1CSPLINE -0.008 0.011 -0.712 0.476 0.003
COV1 -0.016 0.024 -0.645 0.519 0.000
COV2 -0.011 0.024 -0.435 0.664 0.004
COV3 0.016 0.030 0.524 0.600 0.001
COV4 -0.047 0.072 -0.657 0.511 0.000
COV5 -0.095 0.231 -0.411 0.681 0.002
COV6 0.157 0.367 0.429 0.668 0.002
COV7 -0.076 0.193 -0.391 0.696 0.001
COV8 -0.011 0.015 -0.705 0.481 0.175
COV9 0.017 0.018 0.958 0.338 0.279
COV10 0.019 0.022 0.867 0.386 0.004
COV11 -0.074 0.041 -1.794 0.073 0.008
COV12 -0.015 0.024 -0.634 0.526 0.000
COV13 -0.043 0.027 -1.589 0.112 0.155
COV14 -0.025 0.009 -2.759 0.006 0.007
COV15 0.038 0.028 1.367 0.171 0.000
COV16 -0.007 0.011 -0.640 0.522 0.004
COV17 -0.005 0.015 -0.316 0.752 0.000
COV18 0.007 0.012 0.546 0.585 0.001
COV19 -0.008 0.011 -0.763 0.445 0.002
COV20 0.005 0.012 0.399 0.690 0.001
COV21 -0.020 0.022 -0.894 0.371 0.007
COV22 0.020 0.032 0.632 0.528 0.003
COV23 0.046 0.031 1.492 0.136 0.001
COV24 0.047 0.031 1.531 0.126 0.000
COV25 0.003 0.021 0.138 0.890 0.005
COV26 -0.043 0.039 -1.096 0.273 0.001
COV27 0.039 0.028 1.399 0.162 0.000
COV28 -0.007 0.014 -0.491 0.624 0.000
COV29 -0.019 0.025 -0.760 0.447 0.007
COV30 -0.012 0.018 -0.671 0.502 0.000
COV31 0.010 0.013 0.773 0.439 0.004
COV32 -0.032 0.021 -1.509 0.131 0.000
COV33 0.011 0.043 0.257 0.797 0.002
COV34 -0.004 0.042 -0.090 0.928 0.000
COV35 -0.035 0.035 -1.007 0.314 0.004
COV36 0.020 0.029 0.701 0.483 0.004
COV37 -0.030 0.023 -1.290 0.197 0.009
COV38 0.026 0.029 0.882 0.378 0.004
COV39 0.014 0.022 0.622 0.534 0.000
COV40 -0.014 0.033 -0.408 0.683 0.000
COV41 -0.044 0.045 -0.964 0.335 0.000
COV42 0.001 0.013 0.074 0.941 0.003
COV43 0.014 0.012 1.196 0.232 0.021
COV44 0.032 0.009 3.782 0.000 0.022
COV45 0.012 0.010 1.283 0.200 0.071
COV46 -0.005 0.008 -0.588 0.557 0.045
COV47 0.009 0.012 0.759 0.448 0.017
COV48 0.002 0.021 0.106 0.915 0.012
COV49 0.016 0.053 0.295 0.768 0.001
COV50 -0.033 0.020 -1.676 0.094 0.000
PY ON
I -0.022 0.025 -0.878 0.380 0.002
S -0.278 0.197 -1.416 0.157 0.007
PY ON
A1CSPLINE 0.063 0.032 1.957 0.050 0.003
COV1 -0.335 0.104 -3.214 0.001 0.001
COV2 -0.012 0.094 -0.123 0.902 0.003
COV3 0.073 0.135 0.540 0.589 0.002
COV4 -0.004 0.307 -0.015 0.988 0.006
COV5 -0.695 1.042 -0.668 0.504 0.000
COV6 1.768 1.478 1.196 0.232 0.001
COV7 -1.008 0.691 -1.459 0.144 0.003
COV8 -0.011 0.056 -0.205 0.838 0.044
COV9 0.052 0.064 0.808 0.419 0.076
COV10 0.056 0.096 0.586 0.558 0.001
COV11 0.313 0.195 1.601 0.109 0.002
COV12 -0.176 0.094 -1.869 0.062 0.003
COV13 0.306 0.131 2.332 0.020 0.131
COV14 -0.304 0.149 -2.046 0.041 0.004
COV15 -0.103 0.100 -1.028 0.304 0.005
COV16 0.013 0.048 0.267 0.789 0.004
COV17 0.225 0.063 3.561 0.000 0.007
COV18 -0.010 0.042 -0.248 0.804 0.001
COV19 -0.063 0.041 -1.538 0.124 0.007
COV20 -0.017 0.047 -0.364 0.716 0.008
COV21 0.184 0.095 1.941 0.052 0.004
COV22 0.593 0.116 5.104 0.000 0.006
COV23 -0.118 0.123 -0.959 0.338 0.008
COV24 -0.016 0.099 -0.160 0.873 0.001
COV25 -0.086 0.057 -1.489 0.136 0.001
COV26 -0.121 0.126 -0.961 0.337 0.002
COV27 0.217 0.100 2.182 0.029 0.002
COV28 -0.203 0.057 -3.553 0.000 0.001
COV29 0.107 0.070 1.521 0.128 0.002
COV30 0.025 0.055 0.452 0.651 0.002
COV31 -0.130 0.049 -2.673 0.008 0.017
COV32 -0.050 0.090 -0.549 0.583 0.001
COV33 -0.026 0.137 -0.186 0.853 0.005
COV34 -0.066 0.121 -0.550 0.583 0.013
COV35 0.080 0.120 0.666 0.505 0.004
COV36 -0.267 0.114 -2.341 0.019 0.002
COV37 0.272 0.077 3.529 0.000 0.013
COV38 -0.116 0.120 -0.970 0.332 0.005
COV39 -0.169 0.091 -1.849 0.064 0.002
COV40 -0.119 0.161 -0.742 0.458 0.004
COV41 0.128 0.106 1.212 0.226 0.021
COV42 0.065 0.037 1.740 0.082 0.018
COV43 0.051 0.029 1.786 0.074 0.004
COV44 0.056 0.027 2.072 0.038 0.016
COV45 0.043 0.025 1.700 0.089 0.143
COV46 -0.097 0.056 -1.737 0.082 0.000
COV47 -0.025 0.050 -0.504 0.614 0.003
COV48 0.006 0.086 0.073 0.942 0.125
COV49 -0.005 0.201 -0.024 0.981 0.004
COV50 0.080 0.061 1.320 0.187 0.001
Intercepts
RTR200 0.000 0.000 999.000 999.000 0.000
RTR201 0.000 0.000 999.000 999.000 0.000
RTR202 0.000 0.000 999.000 999.000 0.000
RTR203 0.000 0.000 999.000 999.000 0.000
RTR204 0.000 0.000 999.000 999.000 0.000
RTR205 0.000 0.000 999.000 999.000 0.000
I 7.573 0.410 18.464 0.000 0.008
S -0.091 0.066 -1.389 0.165 0.008
Residual Variances
RTR200 4.190 0.230 18.205 0.000 0.001
RTR201 6.915 0.356 19.404 0.000 0.000
RTR202 4.170 0.237 17.572 0.000 0.000
RTR203 6.958 0.429 16.236 0.000 0.001
RTR204 4.695 0.316 14.868 0.000 0.001
RTR205 6.457 0.717 9.002 0.000 0.001
I 2.769 0.167 16.542 0.000 0.001
S 0.002 0.004 0.517 0.605 0.004
.
| Model | Unconditional | Conditional | Joint model,conditional | |||
|---|---|---|---|---|---|---|
| Parameter | Est (95% CI) | p-value | Est (95% CI) | p-value | Est (95% CI) | p-value |
| Growth curve parameters | ||||||
| Intercept | 8.298 (8.131, 8.465) | <.001 | 7.561 (6.757, 8.365) | <.001 | 7.573 (6.769, 8.377) | <.001 |
| Slope (conditional mean) | -0.127 (-0.149, -0.105) | <.001 | -0.086 (-0.215, 0.043) | .19 | -0.091 (-0.220, 0.038) | .16 |
| HbA1c effects in growth curve | ||||||
| HbA1c spline in intercept | -0.084 (-0.239, 0.071) | .29 | -0.091 (-0.234, 0.052) | .21 | -0.089 (-0.232, 0.054) | .22 |
| HbA1c spline in slope | -0.017 (-0.041, 0.007) | .18 | -0.007 (-0.029, 0.015) | .50 | -0.008 (-0.030, 0.014) | .48 |
| HbA1c effects in survival | ||||||
| Hazard ratio per unit increase in HbA1c spline | 1.117 (1.049, 1.190) | <.001 | 1.069 (1.002, 1.141) | .04 | 1.065 (1.000, 1.134) | .050 |
Tables of participant characteristics are available as PDFs, which can be read into modern versions of Word and edited.
2. table1 `set`j'are' , iqr(`set`j'iqr') missing inashell title("Table `j'. `set`j'is'") file(table`j')
3. }
Table 1. Participant sociodemographics
Mean (SD) Observed
Characteristic or n or (%) range
────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
Spl_Age1 [M (SD)] 68.4 (9.4) [51.0-96.0]
Female [n (%)]
0 552 (37.6)
1 918 (62.4)
Lths [n (%)]
0 941 (64.0)
1 529 (36.0)
Gths [n (%)]
0 1234 (83.9)
1 236 (16.1)
cses measures:Childhood SES index [M (SD)] -0.2 (0.9) [-3.6-2.2]
inpova: whether in poverty-w inst [n (%)]
0 0.hh inc above pov thresh 1140 (77.6)
1 1.hh inc below pov thresh 330 (22.4)
atotb: total all assets inc. 2nd hm--cross-wave [Median (P'tile 25-75)] 51600.0 [1800.0-159800.0] [-1.4e+05-1.5e+07]
hitot minus 25 000 divided by 1000 [Median (P'tile 25-75)] -3.0 [-13.6-20.6] [-25.0-1544.9]
Completely retired from rsayret [n (%)]
0 Not Part 578 (43.6)
1 Completely 749 (56.4)
Maximum of rameduc and rafeduc [M (SD)] 9.0 (3.6) [0.0-17.0]
Whether coupled or partnered [n (%)]
0 0.no 795 (54.1)
1 1.yes 675 (45.9)
BORN IN THE U.S. [n (%)]
0 0.no 95 (6.5)
1 1.yes 1374 (93.5)
yrenter centered at 1995.846 [M (SD)] 0.0 (4.8) [-3.8-12.2]
────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Notes: 143 missing on fullret, 190 missing on maxparented, 1 missing on usborn.
table1 results are stored in mata workspace in matrix 'results' , and in
table1.txt as tab delimited
table1.csv as CSV
table1.tex as LaTeX
table1.pdf as PDF from LaTeX
Table 2. Participant personal characteristics
Mean (SD) Observed
Characteristic or n or (%) range
─────────────────────────────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
self-reported body mass index=kg m2 [M (SD)] 29.7 (6.6) [12.3-70.9]
Any APOE-E4 alleles [n (%)]
0 0.no 879 (62.3)
1 1.yes 531 (37.7)
─────────────────────────────────────────────────────────────────────────────
Notes: 13 missing on rbmi, 60 missing on apoe4.
table1 results are stored in mata workspace in matrix 'results' , and in
table2.txt as tab delimited
table2.csv as CSV
table2.tex as LaTeX
table2.pdf as PDF from LaTeX
Table 3. Participant self-reported health and functioning
Mean (SD) Observed
Characteristic or n or (%) range
────────────────────────────────────────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
self-report of health [n (%)]
1 1.excellent 71 (4.8)
2 2.very good 332 (22.6)
3 3.good 469 (31.9)
4 4.fair 432 (29.4)
5 5.poor 165 (11.2)
cesd score [n (%)]
0 518 (35.3)
1 381 (25.9)
2 185 (12.6)
3 100 (6.8)
4 69 (4.7)
5 75 (5.1)
6 69 (4.7)
7 46 (3.1)
8 26 (1.8)
some diff-adls 0-5 [n (%)]
0 1148 (78.1)
1 161 (11.0)
2 68 (4.6)
3 46 (3.1)
4 35 (2.4)
5 12 (0.8)
some diff-iadls: w2 onwards 0-3 [n (%)]
0 1334 (90.7)
1 97 (6.6)
2 20 (1.4)
3 19 (1.3)
iadlza: some diff-iadls: w2 onwards 0-5 [n (%)]
0 1218 (82.9)
1 129 (8.8)
2 64 (4.4)
3 32 (2.2)
4 12 (0.8)
5 15 (1.0)
grossa: walk1 r clim1 bed bath 0-5 [n (%)]
0 1012 (68.8)
1 188 (12.8)
2 112 (7.6)
3 80 (5.4)
4 49 (3.3)
5 29 (2.0)
mobila: some diff-mobility 0-5 [n (%)]
0 645 (43.9)
1 296 (20.1)
2 196 (13.3)
3 126 (8.6)
4 120 (8.2)
5 87 (5.9)
lgmusa: some diff-large muscle 0-4 [n (%)]
0 521 (35.4)
1 294 (20.0)
2 243 (16.5)
3 246 (16.7)
4 166 (11.3)
finea: dime eat dress 0-3 [n (%)]
0 1202 (81.8)
1 192 (13.1)
2 65 (4.4)
3 11 (0.7)
shltc: change in self-reported hlth [n (%)]
-4 1 (0.1)
-3 11 (0.8)
-2 39 (2.7)
-1 260 (17.8)
0 769 (52.8)
1 303 (20.8)
2 62 (4.3)
3 11 (0.8)
4 1 (0.1)
────────────────────────────────────────────────────────────────────────────────────────
Notes: 1 missing on rshlt, 1 missing on rcesd, 13 missing on rshltc.
table1 results are stored in mata workspace in matrix 'results' , and in
table3.txt as tab delimited
table3.csv as CSV
table3.tex as LaTeX
table3.pdf as PDF from LaTeX
Table 4. Participant health and disease-related characteristics
Mean (SD) Observed
Characteristic or n or (%) range
──────────────────────────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
r ever had high blood pressure [n (%)]
0 0.no 388 (26.4)
1 1.yes 1080 (73.6)
lunge: r ever had lung disease [n (%)]
0 0.no 1366 (93.1)
1 1.yes 102 (6.9)
stroke: r ever had stroke [n (%)]
0 0.no 1315 (89.5)
1 1.yes 155 (10.5)
Rcancre [n (%)]
0 0.no 1282 (87.4)
1 1.yes 184 (12.6)
arthre: r ever had arthritis [n (%)]
0 0.no 551 (37.5)
1 1.yes 917 (62.5)
conde: sum of conditions ever had [n (%)]
0 142 (9.7)
1 300 (20.4)
2 402 (27.3)
3 345 (23.5)
4 163 (11.1)
5 81 (5.5)
6 30 (2.0)
7 7 (0.5)
──────────────────────────────────────────────────────────────────────────
Notes: 2 missing on rhibpe, 2 missing on rlunge, 4 missing on rcancre, 2 missing on rarthre.
table1 results are stored in mata workspace in matrix 'results' , and in
table4.txt as tab delimited
table4.csv as CSV
table4.tex as LaTeX
table4.pdf as PDF from LaTeX
Table 5. Participant treatments and services utilization characteristics
Mean (SD) Observed
Characteristic or n or (%) range
──────────────────────────────────────────────────────────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
outpt: outpatient surgry prv 2 yrs [n (%)]
0 0.no 1233 (84.0)
1 1.yes 234 (16.0)
dentst: dental visit prv 2 yrs [n (%)]
0 0.no 852 (58.0)
1 1.yes 617 (42.0)
drugs: reg take rx prv 2 yrs [n (%)]
0 0.no 218 (14.8)
1 1.yes 1251 (85.2)
homcar: home hlth care prv 2 yrs [n (%)]
0 0.no 1317 (89.7)
1 1.yes 151 (10.3)
hsptim: No. hospital stays prv 2 yrs [Median (P'tile 25-75)] 0.0 [0.0-1.0] [0.0-7.0]
nrstim: No. nurs home stays prv 2 yrs [n (%)]
0 1449 (98.6)
1 16 (1.1)
2 3 (0.2)
3 1 (0.1)
hspnit: No. nights in hosp prv 2 yrs [Median (P'tile 25-75)] 0.0 [0.0-1.0] [0.0-180.0]
doctim: No. doctor vists prv 2 yrs [Median (P'tile 25-75)] 7.0 [3.0-12.0] [0.0-350.0]
nrsnit: No. nights in nurs home prv 2 yrs [Median (P'tile 25-75)] 0.0 [0.0-0.0] [0.0-180.0]
──────────────────────────────────────────────────────────────────────────────────────────────────────────
Notes: 3 missing on routpt, 1 missing on rdentst, 1 missing on rdrugs, 2 missing on rhomcar, 9 missing on rhsptim, 1 missing on rnrstim, 14 missing on rhspnit, 86 missing on rdoctim,
table1 results are stored in mata workspace in matrix 'results' , and in
table5.txt as tab delimited
table5.csv as CSV
table5.tex as LaTeX
table5.pdf as PDF from LaTeX
Table 6. Participant reported sensory impairment
Mean (SD) Observed
Characteristic or n or (%) range
──────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
RATE EYESIGHT [n (%)]
1 1 84 (5.7)
2 2 261 (17.8)
3 3 642 (43.7)
4 4 352 (23.9)
5 5 124 (8.4)
6 6 7 (0.5)
RATE HEARING [n (%)]
1 1 252 (17.1)
2 2 377 (25.6)
3 3 556 (37.8)
4 4 224 (15.2)
5 5 61 (4.1)
──────────────────────────────────────────────────────
table1 results are stored in mata workspace in matrix 'results' , and in
table6.txt as tab delimited
table6.csv as CSV
table6.tex as LaTeX
table6.pdf as PDF from LaTeX
Table 7. Participant self-reported health behaviors
Mean (SD) Observed
Characteristic or n or (%) range
─────────────────────────────────────────────────────────────────────────
Total [n (%)] 1470 (100)
Pastsmk [n (%)]
0 869 (59.5)
1 592 (40.5)
Currsmk [n (%)]
0 1222 (83.6)
1 239 (16.4)
drink: r ever drinks any alcohol [n (%)]
0 0.no 950 (64.6)
1 1.yes 520 (35.4)
─────────────────────────────────────────────────────────────────────────
Notes: 9 missing on pastsmk, 9 missing on currsmk.
table1 results are stored in mata workspace in matrix 'results' , and in
table7.txt as tab delimited
table7.csv as CSV
table7.tex as LaTeX
table7.pdf as PDF from LaTeX
Table 1 Participant
sociodemographics
Table 2 Participant personal
characteristics
Table 3 Participant self-reported health and
functioning
Table 4 Participant health and disease-related
characteristics
Table 5 Participant treatments and services utilization
characteristics
Table 6 Participant reported sensory
impairment
Table 7 Participant self-reported health
behaviors
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