use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
ta AL_cat AL_miss,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
Alcohol |
Use | Alcohol Use Missing
Category | Not Missi Missing | Total
-----------+----------------------+----------
None-Light | 20,274 0 | 20,274
Moderate | 7,686 0 | 7,686
Heavy | 4,232 0 | 4,232
. | 0 37,998 | 37,998
-----------+----------------------+----------
Total | 32,192 37,998 | 70,190 use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta MJ AL_miss,col percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
----------------------------------------
Cannabis | Alcohol Use Missing
Use | Not Miss Missing Total
----------+-----------------------------
Never Us | 40.52 33.02 40.03
Past Use | 44.64 58.72 45.56
1-10 tim | 8.411 3.37 8.082
11-20 ti | 1.896 1.424 1.865
21-30 ti | 4.536 3.463 4.466
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(4) = 396.8634
Design-based F(3.83, 417.25) = 16.5128 P = 0.0000use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta BP_cat AL_miss,col percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
----------------------------------------
BP | Alcohol Use Missing
Category | Not Miss Missing Total
----------+-----------------------------
Normal | 52.31 46.61 51.94
Elevated | 16.25 16.52 16.27
Stage 1 | 20.64 23.42 20.82
Stage 2 | 10.79 13.45 10.97
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(3) = 70.4881
Design-based F(2.98, 324.76) = 4.6614 P = 0.0034use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta gndr AL_miss,col percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
----------------------------------------
| Alcohol Use Missing
Gender | Not Miss Missing Total
----------+-----------------------------
Female | 50.22 42.12 49.69
Male | 49.78 57.88 50.31
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(1) = 112.3311
Design-based F(1, 109) = 26.1362 P = 0.0000use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta race_eth AL_miss,col percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
----------------------------------------
Recoded |
Race & | Alcohol Use Missing
Ethnicity | Not Miss Missing Total
----------+-----------------------------
White-NH | 64.39 69.23 64.71
Black_NH | 11.81 8.897 11.62
Mex Am | 9.783 10.86 9.854
Oth | 14.01 11.01 13.82
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(3) = 79.6670
Design-based F(2.75, 299.53) = 5.4876 P = 0.0016use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta SMK_cat AL_miss,col percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,139
Number of PSUs = 214 Population size = 306,306,164
Subpop. no. obs = 20,969
Subpop. size = 141,285,572
Design df = 109
----------------------------------------
Smoking | Alcohol Use Missing
Category | Not Miss Missing Total
----------+-----------------------------
Never | 57.88 38.06 56.59
Past Smo | 19.29 30.87 20.05
Light, 1 | 12.98 12.49 12.95
Moderate | 7.671 12.05 7.957
Heavy, 2 | 2.173 6.532 2.458
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(4) = 1018.5098
Design-based F(3.62, 394.89) = 52.2495 P = 0.0000use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta EDUC_cat AL_miss,col percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,182
Number of PSUs = 214 Population size = 306,497,065
Subpop. no. obs = 21,012
Subpop. size = 141,476,473
Design df = 109
----------------------------------------
Recode |
Education | Alcohol Use Missing
Level | Not Miss Missing Total
----------+-----------------------------
Less tha | 3.685 6.953 3.898
Less tha | 9.775 16.78 10.23
High Sch | 22.09 27.19 22.42
Some Col | 33.35 29.32 33.09
College | 31.1 19.75 30.36
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(4) = 574.9431
Design-based F(3.65, 398.15) = 25.0275 P = 0.0000use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): ta sddsrvyr AL_miss,col percent
svy, subpop(if include==1): ta sddsrvyr AL_miss,row percent pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
----------------------------------------
Survey | Alcohol Use Missing
Year | Not Miss Missing Total
----------+-----------------------------
2005-200 | 12.77 14.48 12.89
2007-200 | 13.63 18.05 13.92
2009-201 | 13.76 16.96 13.97
2011-201 | 13.9 19.18 14.25
2013-201 | 14.85 15.49 14.9
2015-201 | 14.96 15.33 14.98
2017-201 | 16.12 .5154 15.1
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(6) = 875.7773
Design-based F(4.71, 513.03) = 18.9376 P = 0.0000
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
----------------------------------------
Survey | Alcohol Use Missing
Year | Not Miss Missing Total
----------+-----------------------------
2005-200 | 92.67 7.325 100
2007-200 | 91.54 8.456 100
2009-201 | 92.08 7.917 100
2011-201 | 91.22 8.777 100
2013-201 | 93.22 6.78 100
2015-201 | 93.33 6.672 100
2017-201 | 99.78 .2226 100
|
Total | 93.48 6.521 100
----------------------------------------
Key: row percentage
Pearson:
Uncorrected chi2(6) = 875.7773
Design-based F(4.71, 513.03) = 18.9376 P = 0.0000use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): mean hei2015, over(AL_miss) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 68,641
Number of PSUs = 214 Population size = 297,338,173
Subpop. no. obs = 19,471
Subpop. size = 132,317,580
Design df = 109
-------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
------------------+------------------------------------------------
c.hei2015@AL_miss |
Not Missing | 52.30551 .2258467 51.85789 52.75313
Missing | 50.05454 .5488218 48.96679 51.14229
-------------------------------------------------------------------use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): mean bmxbmi, over(AL_miss) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 70,080
Number of PSUs = 214 Population size = 305,959,182
Subpop. no. obs = 20,910
Subpop. size = 140,938,589
Design df = 109
------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-----------------+------------------------------------------------
c.bmxbmi@AL_miss |
Not Missing | 28.88579 .1014983 28.68462 29.08696
Missing | 30.07532 .280443 29.51949 30.63115
------------------------------------------------------------------use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): mean ridageyr, over(AL_miss) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
--------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------------+------------------------------------------------
c.ridageyr@AL_miss |
Not Missing | 39.21059 .1593037 38.89485 39.52632
Missing | 44.38201 .3748651 43.63904 45.12498
--------------------------------------------------------------------use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
svy, subpop(if include==1): mean indfmpir, over(AL_miss) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 68,616
Number of PSUs = 214 Population size = 298,070,653
Subpop. no. obs = 19,446
Subpop. size = 133,050,061
Design df = 109
--------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------------+------------------------------------------------
c.indfmpir@AL_miss |
Not Missing | 3.063389 .0341046 2.995795 3.130983
Missing | 2.622841 .0726115 2.478927 2.766755
--------------------------------------------------------------------Those with missing alcohol use data are: * less light MJ use * slightly more stage 2 htn * Slightly more male * Very similar race/eth * Slightly more moderate-heavy smokers * Slightly less educated * Disproportionately in 2017-2018 cycle * slightly lower HEI2015 * Similar BMI * Similar Age * slightly lower income
Most importantly: Very similar results with or without those with missing alcohol data.
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat .=3, gen(ALC_cat)
label define ALC_cat 0 "None-Light" 1 "Moderate" 2 "Heavy" 3 "Missing"
label values ALC_cat ALC_cat
label variable ALC_cat "Alcohol Use Category"
svy, subpop(if include==1): ta ALC_cat MJ_cat, col percent
ta ALC_cat MJ_cat if include==1,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(37998 differences between AL_cat and ALC_cat)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 21,020
Subpop. size = 141,524,461
Design df = 109
--------------------------------------------------
Alcohol |
Use | Cannabis Use Category
Category | Never Us Past Use Current Total
----------+---------------------------------------
None-Lig | 64.94 40.41 28.29 48.48
Moderate | 20.86 33.5 37.47 29.01
Heavy | 8.823 17.69 30.51 15.99
Missing | 5.38 8.405 3.735 6.521
|
Total | 100 100 100 100
--------------------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(6) = 6610.8899
Design-based F(5.31, 578.40) = 185.3983 P = 0.0000
Alcohol |
Use | Cannabis Use Category
Category | Never Use Past Use Current U | Total
-----------+---------------------------------+----------
None-Light | 6,237 3,305 913 | 10,455
Moderate | 1,899 2,668 1,091 | 5,658
Heavy | 969 1,557 901 | 3,427
Missing | 598 774 108 | 1,480
-----------+---------------------------------+----------
Total | 9,703 8,304 3,013 | 21,020 use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
xi: svy,subpop(if include==1 & AL_miss==0): logit BP_abn i.MJ2 gndr ridageyr, or pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
i.MJ2 _IMJ2_0-2 (naturally coded; _IMJ2_0 omitted)
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 19,540
Subpop. size = 132,295,780
Design df = 109
F( 4, 106) = 296.50
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
BP_abn | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_IMJ2_1 | 1.149771 .068934 2.33 0.022 1.020952 1.294845
_IMJ2_2 | 1.142925 .1125914 1.36 0.178 .9402058 1.389352
gndr | 2.466686 .0995826 22.36 0.000 2.277006 2.672166
ridageyr | 1.048802 .0021503 23.24 0.000 1.044549 1.053072
_cons | .0870363 .0072116 -29.47 0.000 .0738552 .1025699
------------------------------------------------------------------------------
Note: _cons estimates baseline odds.use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat 0=0 1=0 2=0 3=0 4=0 .=1, gen(AL_miss)
label define AL_miss 0 "Not Missing" 1 "Missing"
label values AL_miss AL_miss
label variable AL_miss "Alcohol Use Missing"
xi: svy,subpop(if include==1 & AL_miss==0): mlogit BP_cat i.MJ2 gndr ridageyr, rrr pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(49916 differences between AL_cat and AL_miss)
i.MJ2 _IMJ2_0-2 (naturally coded; _IMJ2_0 omitted)
(running mlogit on estimation sample)
Survey: Multinomial logistic regression
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 19,540
Subpop. size = 132,295,780
Design df = 109
F( 12, 98) = 137.58
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
BP_cat | RRR Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Normal | (base outcome)
-------------+----------------------------------------------------------------
Elevated |
_IMJ2_1 | 1.161144 .0924113 1.88 0.063 .991703 1.359536
_IMJ2_2 | 1.250721 .1614201 1.73 0.086 .9684329 1.615293
gndr | 2.323464 .1169758 16.75 0.000 2.102814 2.567268
ridageyr | 1.027802 .0029303 9.62 0.000 1.02201 1.033626
_cons | .069961 .0079452 -23.42 0.000 .0558603 .0876211
-------------+----------------------------------------------------------------
Stage_1_HTN |
_IMJ2_1 | 1.112923 .0990483 1.20 0.232 .9329513 1.327611
_IMJ2_2 | 1.020423 .1230006 0.17 0.867 .803573 1.295791
gndr | 2.450932 .1272142 17.27 0.000 2.211333 2.716492
ridageyr | 1.049218 .0026578 18.97 0.000 1.043963 1.054499
_cons | .0375769 .0039426 -31.27 0.000 .0305217 .0462629
-------------+----------------------------------------------------------------
Stage_2_HTN |
_IMJ2_1 | 1.185814 .1255485 1.61 0.110 .9613548 1.462681
_IMJ2_2 | 1.183829 .211061 0.95 0.346 .8314333 1.685585
gndr | 2.787245 .1999721 14.29 0.000 2.417797 3.213147
ridageyr | 1.08709 .0028555 31.79 0.000 1.081446 1.092765
_cons | .0037377 .0004722 -44.24 0.000 .0029098 .0048012
------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.Most observations (44k / 70k) were not in the correct age range.
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
ta AGE_exclude include,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
Meets Age | Meets Overall
Inclusion | Inclusion Criteria
Criteria | Excluded Included | Total
-------------+----------------------+----------
Age 20-59 | 5,249 21,020 | 26,269
Age Excluded | 43,921 0 | 43,921
-------------+----------------------+----------
Total | 49,170 21,020 | 70,190 mean ages are quite similar
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): mean ridageyr, over(include) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 25,348
Subpop. size = 166,131,356
Design df = 109
--------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------------+------------------------------------------------
c.ridageyr@include |
Excluded | 38.94718 .2541685 38.44342 39.45093
Included | 39.54781 .1563763 39.23788 39.85774
--------------------------------------------------------------------use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): ta gndr include, col percent
ta gndr include if AGE_exclude==0,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 25,348
Subpop. size = 166,131,356
Design df = 109
----------------------------------------
| Meets Overall Inclusion
| Criteria
Gender | Excluded Included Total
----------+-----------------------------
Female | 56.95 49.69 50.77
Male | 43.05 50.31 49.23
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(1) = 186.7592
Design-based F(1, 109) = 41.1675 P = 0.0000
| Meets Overall
| Inclusion Criteria
Gender | Excluded Included | Total
-----------+----------------------+----------
Female | 2,986 10,679 | 13,665
Male | 2,263 10,341 | 12,604
-----------+----------------------+----------
Total | 5,249 21,020 | 26,269 Included participants were a bit whiter than excluded.
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): ta race_eth include, col percent
ta race_eth include if AGE_exclude==0,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 25,348
Subpop. size = 166,131,356
Design df = 109
----------------------------------------
Recoded | Meets Overall Inclusion
Race & | Criteria
Ethnicity | Excluded Included Total
----------+-----------------------------
White-NH | 52.28 64.71 62.87
Black_NH | 15.98 11.62 12.27
Mex Am | 11 9.854 10.02
Oth | 20.74 13.82 14.84
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(3) = 652.4977
Design-based F(2.50, 272.00) = 38.9452 P = 0.0000
Recoded | Meets Overall
Race & | Inclusion Criteria
Ethnicity | Excluded Included | Total
-----------+----------------------+----------
White-NH | 1,585 8,395 | 9,980
Black_NH | 1,242 4,494 | 5,736
Mex Am | 923 3,609 | 4,532
Oth | 1,499 4,522 | 6,021
-----------+----------------------+----------
Total | 5,249 21,020 | 26,269 Included were slightly more educated; note difference in less than 9th grade education.
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): ta EDUC_cat include, col percent
ta EDUC_cat include if AGE_exclude==0,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,169
Number of PSUs = 214 Population size = 306,448,136
Subpop. no. obs = 25,332
Subpop. size = 166,034,439
Design df = 109
----------------------------------------
Recode | Meets Overall Inclusion
Education | Criteria
Level | Excluded Included Total
----------+-----------------------------
Less tha | 9.056 3.898 4.661
Less tha | 12.51 10.23 10.57
High Sch | 23.54 22.42 22.59
Some Col | 28.75 33.09 32.45
College | 26.14 30.36 29.73
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(4) = 657.2417
Design-based F(3.40, 370.45) = 40.4108 P = 0.0000
| Meets Overall
Recode Education | Inclusion Criteria
Level | Excluded Included | Total
----------------------+----------------------+----------
Less than 9th Grade | 659 1,442 | 2,101
Less than High School | 812 2,910 | 3,722
High School/GED | 1,180 4,738 | 5,918
Some College | 1,452 6,754 | 8,206
College Graduate | 1,133 5,168 | 6,301
. | 13 8 | 21
----------------------+----------------------+----------
Total | 5,249 21,020 | 26,269 Similar
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): ta SMK_cat include, col percent
ta SMK_cat include if AGE_exclude==0,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,115
Number of PSUs = 214 Population size = 306,228,409
Subpop. no. obs = 25,278
Subpop. size = 165,814,712
Design df = 109
----------------------------------------
| Meets Overall Inclusion
Smoking | Criteria
Category | Excluded Included Total
----------+-----------------------------
Never | 62.49 56.59 57.46
Past Smo | 14.78 20.05 19.27
Light, 1 | 13.98 12.95 13.1
Moderate | 6.463 7.957 7.736
Heavy, 2 | 2.285 2.458 2.432
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(4) = 214.9924
Design-based F(3.85, 419.51) = 10.5232 P = 0.0000
| Meets Overall
| Inclusion Criteria
Smoking Category | Excluded Included | Total
----------------------+----------------------+----------
Never | 3,313 12,159 | 15,472
Past Smoker | 713 3,665 | 4,378
Light, 1-10 cif/day | 791 3,121 | 3,912
Moderate, 11-20 cig/d | 317 1,566 | 1,883
Heavy, 21+ cig/day | 91 458 | 549
. | 24 51 | 75
----------------------+----------------------+----------
Total | 5,249 21,020 | 26,269 Similar
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): mean bmxbmi, over(include) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 69,915
Number of PSUs = 214 Population size = 304,934,507
Subpop. no. obs = 25,073
Subpop. size = 164,520,810
Design df = 109
------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-----------------+------------------------------------------------
c.bmxbmi@include |
Excluded | 28.70402 .1640714 28.37884 29.02921
Included | 28.96312 .1011648 28.76261 29.16362
------------------------------------------------------------------Excluded had slightly lower income.
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): mean indfmpir, over(include) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 68,059
Number of PSUs = 214 Population size = 295,209,716
Subpop. no. obs = 23,217
Subpop. size = 154,796,019
Design df = 109
--------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------------+------------------------------------------------
c.indfmpir@include |
Excluded | 2.678598 .0526744 2.574199 2.782996
Included | 3.03458 .0350647 2.965083 3.104077
--------------------------------------------------------------------use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
recode AL_cat .=3, gen(ALC_cat)
label define ALC_cat 0 "None-Light" 1 "Moderate" 2 "Heavy" 3 "Missing"
label values ALC_cat ALC_cat
label variable ALC_cat "Alcohol Use Category"
svy, subpop(if AGE_exclude==0): ta AL_cat include, col percent
svy, subpop(if AGE_exclude==0): ta ALC_cat include, col percent
ta ALC_cat include if AGE_exclude==0,m pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(37998 differences between AL_cat and ALC_cat)
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 65,011
Number of PSUs = 214 Population size = 281,890,666
Subpop. no. obs = 21,090
Subpop. size = 141,476,970
Design df = 109
----------------------------------------
Alcohol | Meets Overall Inclusion
Use | Criteria
Category | Excluded Included Total
----------+-----------------------------
None-Lig | 61.53 51.86 52.49
Moderate | 23.4 31.04 30.54
Heavy | 15.07 17.1 16.97
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(2) = 155.1160
Design-based F(1.93, 210.58) = 19.5090 P = 0.0000
(running tabulate on estimation sample)
Number of strata = 105 Number of obs = 70,190
Number of PSUs = 214 Population size = 306,545,053
Subpop. no. obs = 25,348
Subpop. size = 166,131,356
Design df = 109
----------------------------------------
Alcohol | Meets Overall Inclusion
Use | Criteria
Category | Excluded Included Total
----------+-----------------------------
None-Lig | 22.96 48.48 44.7
Moderate | 8.731 29.01 26.01
Heavy | 5.623 15.99 14.45
Missing | 62.69 6.521 14.84
|
Total | 100 100 100
----------------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(3) = 2.22e+04
Design-based F(2.21, 241.06) = 1195.6734 P = 0.0000
Alcohol | Meets Overall
Use | Inclusion Criteria
Category | Excluded Included | Total
-----------+----------------------+----------
None-Light | 980 10,455 | 11,435
Moderate | 345 5,658 | 6,003
Heavy | 225 3,427 | 3,652
Missing | 3,699 1,480 | 5,179
-----------+----------------------+----------
Total | 5,249 21,020 | 26,269 Similar
use "data\NHANES0518_new.dta", clear
svyset sdmvpsu [pw=wtmec12yr], strata(sdmvstra)
svy, subpop(if AGE_exclude==0): mean hei2015, over(include) pweight: wtmec12yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 105 Number of obs = 67,220
Number of PSUs = 214 Population size = 289,323,664
Subpop. no. obs = 22,378
Subpop. size = 148,909,968
Design df = 109
-------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
------------------+------------------------------------------------
c.hei2015@include |
Excluded | 52.44492 .3712613 51.70909 53.18074
Included | 52.15834 .2244701 51.71344 52.60323
-------------------------------------------------------------------