Vitamin D
Mean and error of plasma vitamin D levels (ng / mL)
We created this variable before df0$person<-1 “person” is a “variable” with constant value=1 I use “person” to calculate mean in total sample using svyby instead of svymean()
## person outcome se ci_l ci_u DEff.outcome
## 1 1 20.07805 0.3682582 19.35628 20.79982 3.400738## Age outcome se ci_l ci_u DEff.outcome
## 17-24 17-24 19.73892 0.6817870 18.40264 21.07520 2.743411
## 25-44 25-44 20.35761 0.4729269 19.43069 21.28453 3.391753
## 45-64 45-64 19.48566 1.0369305 17.45331 21.51800 4.502440## Educational_level outcome se ci_l ci_u DEff.outcome
## Low Low 22.68074 1.4281898 19.88154 25.47994 3.958762
## Mid Mid 20.06069 0.4159796 19.24538 20.87599 3.032236
## High High 19.60074 0.7852376 18.06170 21.13978 3.954881## Area outcome se ci_l ci_u DEff.outcome
## Urban Urban 19.54797 0.4054021 18.75339 20.34254 3.553852
## Rural Rural 24.17664 0.6420721 22.91820 25.43508 1.849969## Region outcome se ci_l ci_u DEff.outcome
## 1 1 24.40378 0.8147021 22.80699 26.00057 1.730523
## 2 2 25.37205 1.2324790 22.95643 27.78766 1.925028
## 3 3 22.83639 1.0505838 20.77729 24.89550 1.646154
## 4 4 20.79709 1.0187150 18.80044 22.79373 2.117744
## 5 5 20.66413 0.7316842 19.23005 22.09820 1.207798
## 6 6 19.31196 0.8232572 17.69840 20.92551 2.120491
## 7 7 19.90651 0.7710662 18.39525 21.41777 2.090395
## 8 8 19.81940 0.9279363 18.00067 21.63812 1.926247
## 9 9 21.36809 0.7864994 19.82657 22.90960 1.162401
## 10 10 20.42591 0.8132471 18.83198 22.01985 1.505404
## 11 11 18.70956 1.0826519 16.58760 20.83152 1.661806
## 12 12 19.38321 1.3444554 16.74812 22.01829 2.276117
## 13 13 19.00648 1.6084495 15.85398 22.15898 2.353027
## 14 14 13.82021 0.8113549 12.22998 15.41043 1.567422
## 15 15 12.81580 1.2536902 10.35861 15.27299 2.218003Mean and error of plasma vitamin D levels (ng / mL)
Generar tabla de medias, se, n e IC95%
Mean and error of plasma vitamin D levels (ng / mL)
## person outcome se ci_l ci_u DEff.outcome exposure n
## 1 1 20.07805 0.3682582 19.35628 20.79982 3.400738 All 1583
## 2 1 22.68074 1.4281898 19.88154 25.47994 3.958762 Low 113
## 3 1 20.06069 0.4159796 19.24538 20.87599 3.032236 Mid 976
## 4 1 19.60074 0.7852376 18.06170 21.13978 3.954881 High 494
## outcome_ exposure_
## 1 Mean <NA>
## 2 Mean Educ level
## 3 Mean Educ level
## 4 Mean Educ levelPrevalence of vitamin D <30, <20 and <12
Prevalence of vitamin D <30
## person outcome1 se ci_l ci_u DEff.outcome1 exposure n
## 1 1 0.8899959 0.01326272 0.8640014 0.9159903 2.843363 All 1583
## 2 1 0.8512115 0.07232704 0.7094531 0.9929699 4.627977 Low 113
## 3 1 0.8890693 0.01539621 0.8588932 0.9192453 2.344242 Mid 976
## 4 1 0.8993810 0.02483398 0.8507073 0.9480547 3.361003 High 494
## outcome1_ exposure_ CI_i CI_s
## 1 Mean <NA> 0.8640009 0.9159908
## 2 Mean Educational levels 0.7094505 0.9929725
## 3 Mean Educational levels 0.8588927 0.9192458
## 4 Mean Educational levels 0.8507064 0.9480556Prevalence of vitamin D <20
## person outcome2 se ci_l ci_u DEff.outcome2 exposure n
## 1 1 0.5222102 0.02394133 0.4752860 0.5691343 3.635619 All 1583
## 2 1 0.3645948 0.07862445 0.2104938 0.5186959 2.989858 Low 113
## 3 1 0.5030312 0.03049085 0.4432702 0.5627922 3.627249 Mid 976
## 4 1 0.5899006 0.03967783 0.5121334 0.6676677 3.209430 High 494
## outcome_ exposure_ CI_i CI_s
## 1 Mean <NA> 0.4752852 0.5691352
## 2 Mean Educational levels 0.2104909 0.5186988
## 3 Mean Educational levels 0.4432691 0.5627933
## 4 Mean Educational levels 0.5121320 0.6676691Prevalence of vitamin D <12
## person outcome3 se ci_l ci_u DEff.outcome3 exposure n
## 1 1 0.16007292 0.01613137 0.12845602 0.1916898 3.063000 All 1583
## 2 1 0.06202279 0.02318446 0.01658208 0.1074635 1.035254 Low 113
## 3 1 0.15350749 0.02081670 0.11270751 0.1943075 3.252621 Mid 976
## 4 1 0.19189542 0.03003225 0.13303330 0.2507575 2.868425 High 494
## outcome_ exposure_ CI_i CI_s
## 1 Mean <NA> 0.12845544 0.1916904
## 2 Mean Educational level 0.01658125 0.1074643
## 3 Mean Educational level 0.11270676 0.1943082
## 4 Mean Educational level 0.13303222 0.2507586## person outcome se ci_l ci_u DEff.outcome exposure n
## 1 1 0.88999586 0.01326272 0.86400141 0.9159903 2.843363 All 1583
## 2 1 0.85121154 0.07232704 0.70945314 0.9929699 4.627977 Low 113
## 3 1 0.88906925 0.01539621 0.85889324 0.9192453 2.344242 Mid 976
## 4 1 0.89938101 0.02483398 0.85070731 0.9480547 3.361003 High 494
## 5 1 0.52221016 0.02394133 0.47528602 0.5691343 3.635619 All 1583
## 6 1 0.36459484 0.07862445 0.21049376 0.5186959 2.989858 Low 113
## 7 1 0.50303119 0.03049085 0.44327022 0.5627922 3.627249 Mid 976
## 8 1 0.58990056 0.03967783 0.51213344 0.6676677 3.209430 High 494
## 9 1 0.16007292 0.01613137 0.12845602 0.1916898 3.063000 All 1583
## 10 1 0.06202279 0.02318446 0.01658208 0.1074635 1.035254 Low 113
## 11 1 0.15350749 0.02081670 0.11270751 0.1943075 3.252621 Mid 976
## 12 1 0.19189542 0.03003225 0.13303330 0.2507575 2.868425 High 494
## outcome_ exposure_ CI_i CI_s type
## 1 Mean <NA> 0.86400093 0.9159908 oucome1
## 2 Mean Educational levels 0.70945054 0.9929725 oucome1
## 3 Mean Educational levels 0.85889269 0.9192458 oucome1
## 4 Mean Educational levels 0.85070641 0.9480556 oucome1
## 5 Mean <NA> 0.47528516 0.5691352 oucome2
## 6 Mean Educational levels 0.21049092 0.5186988 oucome2
## 7 Mean Educational levels 0.44326913 0.5627933 oucome2
## 8 Mean Educational levels 0.51213201 0.6676691 oucome2
## 9 Mean <NA> 0.12845544 0.1916904 oucome3
## 10 Mean Educational level 0.01658125 0.1074643 oucome3
## 11 Mean Educational level 0.11270676 0.1943082 oucome3
## 12 Mean Educational level 0.13303222 0.2507586 oucome3Basic Plot
plot2<-ggplot(res0)+
geom_point(aes(x=exposure, y=outcome, col=type))+
geom_errorbar(aes(x=exposure, y=outcome,
ymin = CI_i, ymax = CI_s),
width = 0.1,size = 0.1, position = position_dodge(0.9))
plot2
You can change the labels, the scale into %, background color, etc.
plot2<-plot2 +
facet_wrap(type~ . , scales = "free_y", nrow = 3)
plot2
# https://ggplot2.tidyverse.org/reference/labs.html
# https://www.r-graph-gallery.com/275-add-text-labels-with-ggplot2.htmlVitamin D Ranges
- 0: Vit D >=30
- 1: Vit D 20-29.9
- 2: Vit D 12-19.9
- 3: Vit D <12
Vitamin D Ranges
## person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3
## 1.Low 1 Low 40896.22 133752.2 83165.41 17047.68
## 1.Mid 1 Mid 297973.16 1036944.1 938862.16 412339.35
## 1.High 1 High 140974.84 433605.6 557635.39 268860.05
## se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ci_l.outcome5_0
## 1.Low 22076.61 30697.49 28029.96 5876.561 -2373.128
## 1.Mid 45819.89 107061.77 92261.07 59179.669 208167.819
## 1.High 37482.16 65943.28 65035.51 48851.604 67511.154
## ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ci_u.outcome5_0
## 1.Low 73586.23 28227.71 5529.828 84165.57
## 1.Mid 827106.88 758033.79 296349.332 387778.49
## 1.High 304359.13 430168.13 173112.663 214438.54
## ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 DEff.outcome5_0
## 1.Low 193918.2 138103.1 28565.53 5.707246
## 1.Mid 1246781.3 1119690.5 528329.37 2.877615
## 1.High 562852.1 685102.7 364607.43 3.900339
## DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3
## 1.Low 5.594298 5.521860 0.8803831
## 1.Mid 6.537463 5.061053 3.6433794
## 1.High 5.112210 4.435029 3.8663595Vitamin D Ranges
## person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3
## 1.Low 1 Low 0.1487885 0.4866167 0.3025721 0.06202279
## 1.Mid 1 Mid 0.1109307 0.3860381 0.3495237 0.15350749
## 1.High 1 High 0.1006190 0.3094805 0.3980051 0.19189542
## se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ci_l.outcome5_0
## 1.Low 0.07232704 0.08151790 0.07835190 0.02318446 0.007030071
## 1.Mid 0.01539621 0.02790336 0.02756623 0.02081670 0.080754731
## 1.High 0.02483398 0.03453090 0.03593478 0.03003225 0.051945294
## ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ci_u.outcome5_0
## 1.Low 0.3268446 0.1490052 0.01658208 0.2905469
## 1.Mid 0.3313485 0.2954949 0.11270751 0.1411068
## 1.High 0.2418011 0.3275743 0.13303330 0.1492927
## ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 DEff.outcome5_0
## 1.Low 0.6463888 0.4561390 0.1074635 4.627977
## 1.Mid 0.4407276 0.4035525 0.1943075 2.344242
## 1.High 0.3771598 0.4684360 0.2507575 3.361003
## DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3
## 1.Low 2.980395 3.259624 1.035254
## 1.Mid 3.204083 3.259937 3.252621
## 1.High 2.751736 2.657961 2.868425Vitamin D Ranges
Vitamin D Ranges
## ci_l.outcome5_0 ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3
## 2 0.007030071 0.3268446 0.1490052 0.01658208
## 3 0.080754731 0.3313485 0.2954949 0.11270751
## 4 0.051945294 0.2418011 0.3275743 0.13303330
## 6 0.007030071 0.3268446 0.1490052 0.01658208
## 7 0.080754731 0.3313485 0.2954949 0.11270751
## 8 0.051945294 0.2418011 0.3275743 0.13303330
## 10 0.007030071 0.3268446 0.1490052 0.01658208
## 11 0.080754731 0.3313485 0.2954949 0.11270751
## 12 0.051945294 0.2418011 0.3275743 0.13303330
## 14 0.007030071 0.3268446 0.1490052 0.01658208
## 15 0.080754731 0.3313485 0.2954949 0.11270751
## 16 0.051945294 0.2418011 0.3275743 0.13303330
## 1 0.084009684 0.3244953 0.3191264 0.12845602
## 5 0.084009684 0.3244953 0.3191264 0.12845602
## 9 0.084009684 0.3244953 0.3191264 0.12845602
## 13 0.084009684 0.3244953 0.3191264 0.12845602
## ci_u.outcome5_0 ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3
## 2 0.2905469 0.6463888 0.4561390 0.1074635
## 3 0.1411068 0.4407276 0.4035525 0.1943075
## 4 0.1492927 0.3771598 0.4684360 0.2507575
## 6 0.2905469 0.6463888 0.4561390 0.1074635
## 7 0.1411068 0.4407276 0.4035525 0.1943075
## 8 0.1492927 0.3771598 0.4684360 0.2507575
## 10 0.2905469 0.6463888 0.4561390 0.1074635
## 11 0.1411068 0.4407276 0.4035525 0.1943075
## 12 0.1492927 0.3771598 0.4684360 0.2507575
## 14 0.2905469 0.6463888 0.4561390 0.1074635
## 15 0.1411068 0.4407276 0.4035525 0.1943075
## 16 0.1492927 0.3771598 0.4684360 0.2507575
## 1 0.1359986 0.4110761 0.4051480 0.1916898
## 5 0.1359986 0.4110761 0.4051480 0.1916898
## 9 0.1359986 0.4110761 0.4051480 0.1916898
## 13 0.1359986 0.4110761 0.4051480 0.1916898
## DEff.outcome5_0 DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3 exposure
## 2 4.627977 2.980395 3.259624 1.035254 Low
## 3 2.344242 3.204083 3.259937 3.252621 Mid
## 4 3.361003 2.751736 2.657961 2.868425 High
## 6 4.627977 2.980395 3.259624 1.035254 Low
## 7 2.344242 3.204083 3.259937 3.252621 Mid
## 8 3.361003 2.751736 2.657961 2.868425 High
## 10 4.627977 2.980395 3.259624 1.035254 Low
## 11 2.344242 3.204083 3.259937 3.252621 Mid
## 12 3.361003 2.751736 2.657961 2.868425 High
## 14 4.627977 2.980395 3.259624 1.035254 Low
## 15 2.344242 3.204083 3.259937 3.252621 Mid
## 16 3.361003 2.751736 2.657961 2.868425 High
## 1 2.843363 3.320414 3.299307 3.063000 All
## 5 2.843363 3.320414 3.299307 3.063000 All
## 9 2.843363 3.320414 3.299307 3.063000 All
## 13 2.843363 3.320414 3.299307 3.063000 All
## n exposure_ time outcome5_ se CI_i CI_s
## 2 113 Educational level 1 14.878846 7.232704 0.7027466 29.05495
## 3 976 Educational level 1 11.093075 1.539621 8.0754177 14.11073
## 4 494 Educational level 1 10.061899 2.483398 5.1944400 14.92936
## 6 113 Educational level 2 48.661669 8.151790 32.6841614 64.63918
## 7 976 Educational level 2 38.603806 2.790336 33.1347487 44.07286
## 8 494 Educational level 2 30.948045 3.453090 24.1799878 37.71610
## 10 113 Educational level 3 30.257206 7.835190 14.9002342 45.61418
## 11 976 Educational level 3 34.952370 2.756623 29.5493881 40.35535
## 12 494 Educational level 3 39.800513 3.593478 32.7572964 46.84373
## 14 113 Educational level 4 6.202279 2.318446 1.6581250 10.74643
## 15 976 Educational level 4 15.350749 2.081670 11.2706763 19.43082
## 16 494 Educational level 4 19.189542 3.003225 13.3032218 25.07586
## 1 1583 <NA> 1 11.000414 1.326272 8.4009206 13.59991
## 5 1583 <NA> 2 36.778570 2.208735 32.4494491 41.10769
## 9 1583 <NA> 3 36.213724 2.194469 31.9125640 40.51488
## 13 1583 <NA> 4 16.007292 1.613137 12.8455443 19.16904CROSSTAB
## Area
## exposure Urban Rural
## Low 215501 59360
## Mid 2283632 402487
## High 1363374 37702
##
## Pearson's X^2: Rao & Scott adjustment
##
## data: NextMethod()
## X-squared = 60.6, df = 2, p-value = 1.113e-10Linear regression: svyglm gaussian
svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)
Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad
Primero describo y luego modelo
## Area outcome se ci_l ci_u DEff.outcome
## Urban Urban 19.54797 0.4054021 18.75339 20.34254 3.553852
## Rural Rural 24.17664 0.6420721 22.91820 25.43508 1.849969Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad
##
## Call:
## svyglm(formula = outcome ~ Area + Edad, design = survey_design,
## deff = TRUE)
##
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.75537 1.17661 16.790 < 2e-16 ***
## AreaRural 4.64072 0.76353 6.078 1.93e-09 ***
## Edad -0.00660 0.03464 -0.191 0.849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 60.97116)
##
## Number of Fisher Scoring iterations: 2Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad
Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad
## Estimate Std. Error t value Pr(>|t|) name
## (Intercept) 19.755369485 1.1766104 16.7900686 4.764151e-54 (Intercept)
## AreaRural 4.640717720 0.7635330 6.0779531 1.925020e-09 AreaRural
## Edad -0.006599543 0.0346403 -0.1905163 8.489554e-01 Edad
## outcome
## (Intercept) Vit D numeric
## AreaRural Vit D numeric
## Edad Vit D numericLogistic regression: svyglm logit
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (792) clusters.
## subset(survey_design, Edad < 50 & Sexo == 2)
## Sampling variables:
## - ids: Conglomerado_
## - strata: strata_
## - weights: fexp
##
## Call: svyglm(formula = outcome1 ~ Area + Edad, design = survey_design,
## family = quasibinomial(link = "logit"), deff = TRUE)
##
## Coefficients:
## (Intercept) AreaRural Edad
## 2.292585 -0.995504 -0.001389
##
## Degrees of Freedom: 1582 Total (i.e. Null); 760 Residual
## Null Deviance: 1097
## Residual Deviance: 1075 AIC: NALogistic regression: svyglm logit
##
## Call:
## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design,
## family = quasibinomial(link = "logit"), deff = TRUE)
##
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.292585 0.438579 5.227 2.22e-07 ***
## AreaRural -0.995504 0.257065 -3.873 0.000117 ***
## Edad -0.001389 0.013088 -0.106 0.915522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.000615)
##
## Number of Fisher Scoring iterations: 4Logistic regression: VitD<30 and urbana/rural area ajusted by age
Logistic regression: VitD<30 and urbana/rural area ajusted by age
## Area outcome1 se ci_l ci_u DEff.outcome1
## Urban Urban 0.9045523 0.01423007 0.8766618 0.9324427 3.127481
## Rural Rural 0.7774459 0.03355157 0.7116861 0.8432058 1.614315## outcome OR OR_i OR_s pvalue
## AreaRural VitD<30 0.37 0.22 0.61 0.0001
## Edad VitD<30 1.00 0.97 1.02 0.9155Logistic regression: VitD<30 and urbana/rural area ajusted by age
##
## Call:
## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design,
## family = quasibinomial(link = "logit"), deff = TRUE)
##
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.292585 0.438579 5.227 2.22e-07 ***
## AreaRural -0.995504 0.257065 -3.873 0.000117 ***
## Edad -0.001389 0.013088 -0.106 0.915522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.000615)
##
## Number of Fisher Scoring iterations: 4Several linear regressions
## Estimate Std. Error t value outcome pvalue model
## 1 0.001846841 0.03498990 0.05278213 VitD 0.9579 Age
## 2 -0.006599543 0.03464030 -0.19051632 VitD 0.8490 Area + Age
## 3 4.640717720 0.76353299 6.07795313 VitD 0.0000 Area + Age
## 4 -0.013110634 0.03443705 -0.38071307 VitD 0.7035 Educational_level + Age
## 5 -2.736874939 1.47982640 -1.84945675 VitD 0.0648 Educational_level + Age
## 6 -3.196631405 1.63822151 -1.95128155 VitD 0.0514 Educational_level + Age
## 7 0.000475532 0.03495626 0.01360363 VitD 0.9891 Region_lbl + Age
## 8 0.969062990 1.47665700 0.65625463 VitD 0.5119 Region_lbl + Age
## 9 -1.567324274 1.32961034 -1.17878466 VitD 0.2389 Region_lbl + Age
## 10 -3.605663067 1.30844526 -2.75568507 VitD 0.0060 Region_lbl + Age
## 11 -3.738998074 1.09516817 -3.41408579 VitD 0.0007 Region_lbl + Age
## 12 -5.091752689 1.15872479 -4.39427268 VitD 0.0000 Region_lbl + Age
## 13 -4.496826579 1.12231413 -4.00674506 VitD 0.0001 Region_lbl + Age
## 14 -4.583892516 1.23595549 -3.70878446 VitD 0.0002 Region_lbl + Age
## 15 -3.036167852 1.13207223 -2.68195596 VitD 0.0075 Region_lbl + Age
## 16 -3.977583329 1.15071611 -3.45661567 VitD 0.0006 Region_lbl + Age
## 17 -5.693876307 1.35584148 -4.19951476 VitD 0.0000 Region_lbl + Age
## 18 -5.020303846 1.57247003 -3.19262290 VitD 0.0015 Region_lbl + Age
## 19 -5.397229515 1.80217502 -2.99484204 VitD 0.0028 Region_lbl + Age
## 20 -10.582607623 1.15299614 -9.17835477 VitD 0.0000 Region_lbl + Age
## 21 -11.587300633 1.49620478 -7.74446170 VitD 0.0000 Region_lbl + Age
## 22 0.006356137 0.03583516 0.17737152 VitD 0.8593 EN_lbl + Age
## 23 1.396059122 3.19649190 0.43674727 VitD 0.6624 EN_lbl + Age
## 24 0.358131602 0.99008325 0.36171867 VitD 0.7177 EN_lbl + Age
## 25 0.264082554 0.92190056 0.28645449 VitD 0.7746 EN_lbl + Age
## 26 -3.132028812 1.45701937 -2.14961371 VitD 0.0319 EN_lbl + Age
## 27 0.002889286 0.03530562 0.08183642 VitD 0.9348 Region num + Age
## 28 -0.356603267 0.09750369 -3.65733086 VitD 0.0003 Region num + Age
## type exposure exposure_lbl
## 1 VitD Edad Edad
## 2 VitD Edad Edad
## 3 VitD AreaRural AreaRural
## 4 VitD Edad Edad
## 5 VitD Educational_levelMid Educational_levelMid
## 6 VitD Educational_levelHigh Educational_levelHigh
## 7 VitD Edad Edad
## 8 VitD Region_lblTarapacá Region_lblTarapacá
## 9 VitD Region_lblAntofagasta Region_lblAntofagasta
## 10 VitD Region_lblAtacama Region_lblAtacama
## 11 VitD Region_lblCoquimbo Region_lblCoquimbo
## 12 VitD Region_lblValparaíso Region_lblValparaíso
## 13 VitD Region_lblMetropolitana Region_lblMetropolitana
## 14 VitD Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 15 VitD Region_lblMaule Region_lblMaule
## 16 VitD Region_lblBiobío Region_lblBiobío
## 17 VitD Region_lblAraucanía Region_lblAraucanía
## 18 VitD Region_lblLos Ríos Region_lblLos Ríos
## 19 VitD Region_lblLos Lagos Region_lblLos Lagos
## 20 VitD Region_lblAysén Region_lblAysén
## 21 VitD Region_lblMagallanes Region_lblMagallanes
## 22 VitD Edad Edad
## 23 VitD EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 24 VitD EN_lblOverW (_.) EN_lblOverW (_.)
## 25 VitD EN_lblObese (_.) EN_lblObese (_.)
## 26 VitD EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)
## 27 VitD Edad Edad
## 28 VitD Codigoregion CodigoregionSeveral linear regressions
## Estimate Std. Error t value outcome pvalue model
## 1 0.001846841 0.03498990 0.05278213 VitD 0.9579 Age
## 2 -0.006599543 0.03464030 -0.19051632 VitD 0.8490 Area + Age
## 3 4.640717720 0.76353299 6.07795313 VitD 0.0000 Area + Age
## 4 -0.013110634 0.03443705 -0.38071307 VitD 0.7035 Educational_level + Age
## 5 -2.736874939 1.47982640 -1.84945675 VitD 0.0648 Educational_level + Age
## 6 -3.196631405 1.63822151 -1.95128155 VitD 0.0514 Educational_level + Age
## 7 0.000475532 0.03495626 0.01360363 VitD 0.9891 Region_lbl + Age
## 8 0.969062990 1.47665700 0.65625463 VitD 0.5119 Region_lbl + Age
## 9 -1.567324274 1.32961034 -1.17878466 VitD 0.2389 Region_lbl + Age
## 10 -3.605663067 1.30844526 -2.75568507 VitD 0.0060 Region_lbl + Age
## 11 -3.738998074 1.09516817 -3.41408579 VitD 0.0007 Region_lbl + Age
## 12 -5.091752689 1.15872479 -4.39427268 VitD 0.0000 Region_lbl + Age
## 13 -4.496826579 1.12231413 -4.00674506 VitD 0.0001 Region_lbl + Age
## 14 -4.583892516 1.23595549 -3.70878446 VitD 0.0002 Region_lbl + Age
## 15 -3.036167852 1.13207223 -2.68195596 VitD 0.0075 Region_lbl + Age
## 16 -3.977583329 1.15071611 -3.45661567 VitD 0.0006 Region_lbl + Age
## 17 -5.693876307 1.35584148 -4.19951476 VitD 0.0000 Region_lbl + Age
## 18 -5.020303846 1.57247003 -3.19262290 VitD 0.0015 Region_lbl + Age
## 19 -5.397229515 1.80217502 -2.99484204 VitD 0.0028 Region_lbl + Age
## 20 -10.582607623 1.15299614 -9.17835477 VitD 0.0000 Region_lbl + Age
## 21 -11.587300633 1.49620478 -7.74446170 VitD 0.0000 Region_lbl + Age
## 22 0.006356137 0.03583516 0.17737152 VitD 0.8593 EN_lbl + Age
## 23 1.396059122 3.19649190 0.43674727 VitD 0.6624 EN_lbl + Age
## 24 0.358131602 0.99008325 0.36171867 VitD 0.7177 EN_lbl + Age
## 25 0.264082554 0.92190056 0.28645449 VitD 0.7746 EN_lbl + Age
## 26 -3.132028812 1.45701937 -2.14961371 VitD 0.0319 EN_lbl + Age
## 27 0.002889286 0.03530562 0.08183642 VitD 0.9348 Region num + Age
## 28 -0.356603267 0.09750369 -3.65733086 VitD 0.0003 Region num + Age
## type exposure exposure_lbl
## 1 VitD Edad Edad
## 2 VitD Edad Edad
## 3 VitD AreaRural AreaRural
## 4 VitD Edad Edad
## 5 VitD Educational_levelMid Educational_levelMid
## 6 VitD Educational_levelHigh Educational_levelHigh
## 7 VitD Edad Edad
## 8 VitD Region_lblTarapacá Region_lblTarapacá
## 9 VitD Region_lblAntofagasta Region_lblAntofagasta
## 10 VitD Region_lblAtacama Region_lblAtacama
## 11 VitD Region_lblCoquimbo Region_lblCoquimbo
## 12 VitD Region_lblValparaíso Region_lblValparaíso
## 13 VitD Region_lblMetropolitana Region_lblMetropolitana
## 14 VitD Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 15 VitD Region_lblMaule Region_lblMaule
## 16 VitD Region_lblBiobío Region_lblBiobío
## 17 VitD Region_lblAraucanía Region_lblAraucanía
## 18 VitD Region_lblLos Ríos Region_lblLos Ríos
## 19 VitD Region_lblLos Lagos Region_lblLos Lagos
## 20 VitD Region_lblAysén Region_lblAysén
## 21 VitD Region_lblMagallanes Region_lblMagallanes
## 22 VitD Edad Edad
## 23 VitD EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 24 VitD EN_lblOverW (_.) EN_lblOverW (_.)
## 25 VitD EN_lblObese (_.) EN_lblObese (_.)
## 26 VitD EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)
## 27 VitD Edad Edad
## 28 VitD Codigoregion CodigoregionSeveral logistic regressions
## outcome OR OR_i OR_s pvalue model type CI_i CI_s
## 1 VitD<30 1.00 0.97 1.02 0.7734 Age VitD<30 0.97 1.02
## 2 VitD<20 1.00 0.98 1.02 0.9421 Age VitD<20 0.98 1.02
## 3 VitD<12 0.99 0.96 1.01 0.3112 Age VitD<12 0.96 1.01
## 4 VitD<30 0.37 0.22 0.61 0.0001 Area + Age VitD<30 0.22 0.61
## 5 VitD<30 1.00 0.97 1.02 0.9155 Area + Age VitD<30 0.97 1.02
## 6 VitD<20 0.28 0.17 0.46 0.0000 Area + Age VitD<20 0.17 0.46
## 7 VitD<20 1.00 0.99 1.02 0.8468 Area + Age VitD<20 0.99 1.02
## 8 VitD<12 0.29 0.11 0.73 0.0091 Area + Age VitD<12 0.11 0.73
## 9 VitD<12 0.99 0.96 1.01 0.3696 Area + Age VitD<12 0.96 1.01
## 10 VitD<30 1.00 0.98 1.02 0.8905 Educational_level + Age VitD<30 0.98 1.02
## 11 VitD<30 1.38 0.45 4.23 0.5722 Educational_level + Age VitD<30 0.45 4.23
## 12 VitD<30 1.54 0.46 5.18 0.4854 Educational_level + Age VitD<30 0.46 5.18
## 13 VitD<20 1.00 0.99 1.02 0.7164 Educational_level + Age VitD<20 0.99 1.02
## 14 VitD<20 1.81 0.89 3.71 0.1036 Educational_level + Age VitD<20 0.89 3.71
## 15 VitD<20 2.58 1.22 5.43 0.0130 Educational_level + Age VitD<20 1.22 5.43
## 16 VitD<12 0.99 0.96 1.02 0.4769 Educational_level + Age VitD<12 0.96 1.02
## 17 VitD<12 2.51 1.08 5.83 0.0326 Educational_level + Age VitD<12 1.08 5.83
## 18 VitD<12 3.29 1.32 8.19 0.0106 Educational_level + Age VitD<12 1.32 8.19
## 19 VitD<30 1.00 0.97 1.02 0.7734 Region_lbl + Age VitD<30 0.97 1.02
## 20 VitD<30 0.48 0.20 1.12 0.0894 Region_lbl + Age VitD<30 0.20 1.12
## 21 VitD<30 0.91 0.32 2.60 0.8666 Region_lbl + Age VitD<30 0.32 2.60
## 22 VitD<30 1.33 0.35 5.13 0.6760 Region_lbl + Age VitD<30 0.35 5.13
## 23 VitD<30 2.22 0.84 5.85 0.1083 Region_lbl + Age VitD<30 0.84 5.85
## 24 VitD<30 1.89 0.76 4.73 0.1741 Region_lbl + Age VitD<30 0.76 4.73
## 25 VitD<30 1.27 0.55 2.93 0.5802 Region_lbl + Age VitD<30 0.55 2.93
## 26 VitD<30 3.92 1.20 12.74 0.0236 Region_lbl + Age VitD<30 1.20 12.74
## 27 VitD<30 1.49 0.37 6.06 0.5753 Region_lbl + Age VitD<30 0.37 6.06
## 28 VitD<30 0.97 0.45 2.13 0.9485 Region_lbl + Age VitD<30 0.45 2.13
## 29 VitD<30 1.06 0.39 2.91 0.9101 Region_lbl + Age VitD<30 0.39 2.91
## 30 VitD<30 0.95 0.26 3.55 0.9450 Region_lbl + Age VitD<30 0.26 3.55
## 31 VitD<30 1.00 0.28 3.55 0.9981 Region_lbl + Age VitD<30 0.28 3.55
## 32 VitD<30 7.64 1.73 33.71 0.0074 Region_lbl + Age VitD<30 1.73 33.71
## 33 VitD<30 7.54 0.91 62.72 0.0619 Region_lbl + Age VitD<30 0.91 62.72
## 34 VitD<20 1.00 0.98 1.02 0.9810 Region_lbl + Age VitD<20 0.98 1.02
## 35 VitD<20 1.82 0.88 3.75 0.1043 Region_lbl + Age VitD<20 0.88 3.75
## 36 VitD<20 2.04 0.94 4.43 0.0729 Region_lbl + Age VitD<20 0.94 4.43
## 37 VitD<20 3.58 1.67 7.64 0.0010 Region_lbl + Age VitD<20 1.67 7.64
## 38 VitD<20 2.23 1.02 4.90 0.0460 Region_lbl + Age VitD<20 1.02 4.90
## 39 VitD<20 3.92 1.97 7.80 0.0001 Region_lbl + Age VitD<20 1.97 7.80
## 40 VitD<20 4.76 2.51 9.03 0.0000 Region_lbl + Age VitD<20 2.51 9.03
## 41 VitD<20 3.02 1.06 8.63 0.0391 Region_lbl + Age VitD<20 1.06 8.63
## 42 VitD<20 3.09 1.58 6.04 0.0010 Region_lbl + Age VitD<20 1.58 6.04
## 43 VitD<20 4.19 2.19 8.02 0.0000 Region_lbl + Age VitD<20 2.19 8.02
## 44 VitD<20 4.31 2.08 8.90 0.0001 Region_lbl + Age VitD<20 2.08 8.90
## 45 VitD<20 5.16 2.28 11.69 0.0001 Region_lbl + Age VitD<20 2.28 11.69
## 46 VitD<20 5.36 2.37 12.08 0.0001 Region_lbl + Age VitD<20 2.37 12.08
## 47 VitD<20 24.00 9.41 61.21 0.0000 Region_lbl + Age VitD<20 9.41 61.21
## 48 VitD<20 17.86 5.62 56.73 0.0000 Region_lbl + Age VitD<20 5.62 56.73
## 49 VitD<12 0.99 0.96 1.01 0.3268 Region_lbl + Age VitD<12 0.96 1.01
## 50 VitD<12 0.73 0.14 3.68 0.7026 Region_lbl + Age VitD<12 0.14 3.68
## 51 VitD<12 0.83 0.17 3.96 0.8101 Region_lbl + Age VitD<12 0.17 3.96
## 52 VitD<12 1.43 0.31 6.59 0.6500 Region_lbl + Age VitD<12 0.31 6.59
## 53 VitD<12 1.55 0.39 6.09 0.5311 Region_lbl + Age VitD<12 0.39 6.09
## 54 VitD<12 3.68 1.00 13.46 0.0496 Region_lbl + Age VitD<12 1.00 13.46
## 55 VitD<12 2.95 0.92 9.48 0.0696 Region_lbl + Age VitD<12 0.92 9.48
## 56 VitD<12 2.59 0.63 10.61 0.1850 Region_lbl + Age VitD<12 0.63 10.61
## 57 VitD<12 1.18 0.26 5.50 0.8286 Region_lbl + Age VitD<12 0.26 5.50
## 58 VitD<12 2.90 0.87 9.72 0.0843 Region_lbl + Age VitD<12 0.87 9.72
## 59 VitD<12 7.32 2.28 23.55 0.0009 Region_lbl + Age VitD<12 2.28 23.55
## 60 VitD<12 3.98 1.06 14.97 0.0415 Region_lbl + Age VitD<12 1.06 14.97
## 61 VitD<12 4.57 1.29 16.16 0.0188 Region_lbl + Age VitD<12 1.29 16.16
## 62 VitD<12 14.97 4.44 50.47 0.0000 Region_lbl + Age VitD<12 4.44 50.47
## 63 VitD<12 23.64 6.65 84.04 0.0000 Region_lbl + Age VitD<12 6.65 84.04
## 64 VitD<30 1.00 0.97 1.02 0.7731 Region num + Age VitD<30 0.97 1.02
## 65 VitD<30 1.00 0.93 1.08 0.9460 Region num + Age VitD<30 0.93 1.08
## 66 VitD<20 1.00 0.98 1.02 0.9187 Region num + Age VitD<20 0.98 1.02
## 67 VitD<20 1.09 1.04 1.14 0.0005 Region num + Age VitD<20 1.04 1.14
## 68 VitD<12 0.99 0.96 1.01 0.3033 Region num + Age VitD<12 0.96 1.01
## 69 VitD<12 1.15 1.08 1.23 0.0000 Region num + Age VitD<12 1.08 1.23
## 70 VitD<30 0.99 0.97 1.02 0.5791 EN_lbl + Age VitD<30 0.97 1.02
## 71 VitD<30 0.27 0.05 1.53 0.1401 EN_lbl + Age VitD<30 0.05 1.53
## 72 VitD<30 1.00 0.49 2.04 0.9930 EN_lbl + Age VitD<30 0.49 2.04
## 73 VitD<30 0.99 0.52 1.89 0.9827 EN_lbl + Age VitD<30 0.52 1.89
## 74 VitD<30 2.52 0.65 9.81 0.1840 EN_lbl + Age VitD<30 0.65 9.81
## 75 VitD<20 1.00 0.98 1.01 0.5698 EN_lbl + Age VitD<20 0.98 1.01
## 76 VitD<20 1.03 0.20 5.28 0.9682 EN_lbl + Age VitD<20 0.20 5.28
## 77 VitD<20 1.20 0.77 1.89 0.4222 EN_lbl + Age VitD<20 0.77 1.89
## 78 VitD<20 1.31 0.85 2.03 0.2257 EN_lbl + Age VitD<20 0.85 2.03
## 79 VitD<20 1.52 0.64 3.61 0.3441 EN_lbl + Age VitD<20 0.64 3.61
## 80 VitD<12 0.99 0.96 1.02 0.3539 EN_lbl + Age VitD<12 0.96 1.02
## 81 VitD<12 0.10 0.01 0.89 0.0394 EN_lbl + Age VitD<12 0.01 0.89
## 82 VitD<12 0.77 0.40 1.47 0.4289 EN_lbl + Age VitD<12 0.40 1.47
## 83 VitD<12 0.64 0.32 1.27 0.2043 EN_lbl + Age VitD<12 0.32 1.27
## 84 VitD<12 3.01 1.10 8.23 0.0324 EN_lbl + Age VitD<12 1.10 8.23
## exposure exposure_lbl
## 1 Edad Edad
## 2 Edad Edad
## 3 Edad Edad
## 4 AreaRural AreaRural
## 5 Edad Edad
## 6 AreaRural AreaRural
## 7 Edad Edad
## 8 AreaRural AreaRural
## 9 Edad Edad
## 10 Edad Edad
## 11 Educational_levelMid Educational_levelMid
## 12 Educational_levelHigh Educational_levelHigh
## 13 Edad Edad
## 14 Educational_levelMid Educational_levelMid
## 15 Educational_levelHigh Educational_levelHigh
## 16 Edad Edad
## 17 Educational_levelMid Educational_levelMid
## 18 Educational_levelHigh Educational_levelHigh
## 19 Edad Edad
## 20 Region_lblTarapacá Region_lblTarapacá
## 21 Region_lblAntofagasta Region_lblAntofagasta
## 22 Region_lblAtacama Region_lblAtacama
## 23 Region_lblCoquimbo Region_lblCoquimbo
## 24 Region_lblValparaíso Region_lblValparaíso
## 25 Region_lblMetropolitana Region_lblMetropolitana
## 26 Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 27 Region_lblMaule Region_lblMaule
## 28 Region_lblBiobío Region_lblBiobío
## 29 Region_lblAraucanía Region_lblAraucanía
## 30 Region_lblLos Ríos Region_lblLos Ríos
## 31 Region_lblLos Lagos Region_lblLos Lagos
## 32 Region_lblAysén Region_lblAysén
## 33 Region_lblMagallanes Region_lblMagallanes
## 34 Edad Edad
## 35 Region_lblTarapacá Region_lblTarapacá
## 36 Region_lblAntofagasta Region_lblAntofagasta
## 37 Region_lblAtacama Region_lblAtacama
## 38 Region_lblCoquimbo Region_lblCoquimbo
## 39 Region_lblValparaíso Region_lblValparaíso
## 40 Region_lblMetropolitana Region_lblMetropolitana
## 41 Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 42 Region_lblMaule Region_lblMaule
## 43 Region_lblBiobío Region_lblBiobío
## 44 Region_lblAraucanía Region_lblAraucanía
## 45 Region_lblLos Ríos Region_lblLos Ríos
## 46 Region_lblLos Lagos Region_lblLos Lagos
## 47 Region_lblAysén Region_lblAysén
## 48 Region_lblMagallanes Region_lblMagallanes
## 49 Edad Edad
## 50 Region_lblTarapacá Region_lblTarapacá
## 51 Region_lblAntofagasta Region_lblAntofagasta
## 52 Region_lblAtacama Region_lblAtacama
## 53 Region_lblCoquimbo Region_lblCoquimbo
## 54 Region_lblValparaíso Region_lblValparaíso
## 55 Region_lblMetropolitana Region_lblMetropolitana
## 56 Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 57 Region_lblMaule Region_lblMaule
## 58 Region_lblBiobío Region_lblBiobío
## 59 Region_lblAraucanía Region_lblAraucanía
## 60 Region_lblLos Ríos Region_lblLos Ríos
## 61 Region_lblLos Lagos Region_lblLos Lagos
## 62 Region_lblAysén Region_lblAysén
## 63 Region_lblMagallanes Region_lblMagallanes
## 64 Edad Edad
## 65 Codigoregion Codigoregion
## 66 Edad Edad
## 67 Codigoregion Codigoregion
## 68 Edad Edad
## 69 Codigoregion Codigoregion
## 70 Edad Edad
## 71 EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 72 EN_lblOverW (_.) EN_lblOverW (_.)
## 73 EN_lblObese (_.) EN_lblObese (_.)
## 74 EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)
## 75 Edad Edad
## 76 EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 77 EN_lblOverW (_.) EN_lblOverW (_.)
## 78 EN_lblObese (_.) EN_lblObese (_.)
## 79 EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)
## 80 Edad Edad
## 81 EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 82 EN_lblOverW (_.) EN_lblOverW (_.)
## 83 EN_lblObese (_.) EN_lblObese (_.)
## 84 EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)Several logistic regressions
## outcome OR OR_i OR_s pvalue model type CI_i CI_s
## 1 VitD<30 1.00 0.97 1.02 0.7734 Age VitD<30 0.97 1.02
## 2 VitD<20 1.00 0.98 1.02 0.9421 Age VitD<20 0.98 1.02
## 3 VitD<12 0.99 0.96 1.01 0.3112 Age VitD<12 0.96 1.01
## 4 VitD<30 0.37 0.22 0.61 0.0001 Area + Age VitD<30 0.22 0.61
## 5 VitD<30 1.00 0.97 1.02 0.9155 Area + Age VitD<30 0.97 1.02
## 6 VitD<20 0.28 0.17 0.46 0.0000 Area + Age VitD<20 0.17 0.46
## 7 VitD<20 1.00 0.99 1.02 0.8468 Area + Age VitD<20 0.99 1.02
## 8 VitD<12 0.29 0.11 0.73 0.0091 Area + Age VitD<12 0.11 0.73
## 9 VitD<12 0.99 0.96 1.01 0.3696 Area + Age VitD<12 0.96 1.01
## 10 VitD<30 1.00 0.98 1.02 0.8905 Educational_level + Age VitD<30 0.98 1.02
## 11 VitD<30 1.38 0.45 4.23 0.5722 Educational_level + Age VitD<30 0.45 4.23
## 12 VitD<30 1.54 0.46 5.18 0.4854 Educational_level + Age VitD<30 0.46 5.18
## 13 VitD<20 1.00 0.99 1.02 0.7164 Educational_level + Age VitD<20 0.99 1.02
## 14 VitD<20 1.81 0.89 3.71 0.1036 Educational_level + Age VitD<20 0.89 3.71
## 15 VitD<20 2.58 1.22 5.43 0.0130 Educational_level + Age VitD<20 1.22 5.43
## 16 VitD<12 0.99 0.96 1.02 0.4769 Educational_level + Age VitD<12 0.96 1.02
## 17 VitD<12 2.51 1.08 5.83 0.0326 Educational_level + Age VitD<12 1.08 5.83
## 18 VitD<12 3.29 1.32 8.19 0.0106 Educational_level + Age VitD<12 1.32 8.19
## 19 VitD<30 1.00 0.97 1.02 0.7734 Region_lbl + Age VitD<30 0.97 1.02
## 20 VitD<30 0.48 0.20 1.12 0.0894 Region_lbl + Age VitD<30 0.20 1.12
## 21 VitD<30 0.91 0.32 2.60 0.8666 Region_lbl + Age VitD<30 0.32 2.60
## 22 VitD<30 1.33 0.35 5.13 0.6760 Region_lbl + Age VitD<30 0.35 5.13
## 23 VitD<30 2.22 0.84 5.85 0.1083 Region_lbl + Age VitD<30 0.84 5.85
## 24 VitD<30 1.89 0.76 4.73 0.1741 Region_lbl + Age VitD<30 0.76 4.73
## 25 VitD<30 1.27 0.55 2.93 0.5802 Region_lbl + Age VitD<30 0.55 2.93
## 26 VitD<30 3.92 1.20 12.74 0.0236 Region_lbl + Age VitD<30 1.20 12.74
## 27 VitD<30 1.49 0.37 6.06 0.5753 Region_lbl + Age VitD<30 0.37 6.06
## 28 VitD<30 0.97 0.45 2.13 0.9485 Region_lbl + Age VitD<30 0.45 2.13
## 29 VitD<30 1.06 0.39 2.91 0.9101 Region_lbl + Age VitD<30 0.39 2.91
## 30 VitD<30 0.95 0.26 3.55 0.9450 Region_lbl + Age VitD<30 0.26 3.55
## 31 VitD<30 1.00 0.28 3.55 0.9981 Region_lbl + Age VitD<30 0.28 3.55
## 32 VitD<30 7.64 1.73 33.71 0.0074 Region_lbl + Age VitD<30 1.73 33.71
## 33 VitD<30 7.54 0.91 62.72 0.0619 Region_lbl + Age VitD<30 0.91 62.72
## 34 VitD<20 1.00 0.98 1.02 0.9810 Region_lbl + Age VitD<20 0.98 1.02
## 35 VitD<20 1.82 0.88 3.75 0.1043 Region_lbl + Age VitD<20 0.88 3.75
## 36 VitD<20 2.04 0.94 4.43 0.0729 Region_lbl + Age VitD<20 0.94 4.43
## 37 VitD<20 3.58 1.67 7.64 0.0010 Region_lbl + Age VitD<20 1.67 7.64
## 38 VitD<20 2.23 1.02 4.90 0.0460 Region_lbl + Age VitD<20 1.02 4.90
## 39 VitD<20 3.92 1.97 7.80 0.0001 Region_lbl + Age VitD<20 1.97 7.80
## 40 VitD<20 4.76 2.51 9.03 0.0000 Region_lbl + Age VitD<20 2.51 9.03
## 41 VitD<20 3.02 1.06 8.63 0.0391 Region_lbl + Age VitD<20 1.06 8.63
## 42 VitD<20 3.09 1.58 6.04 0.0010 Region_lbl + Age VitD<20 1.58 6.04
## 43 VitD<20 4.19 2.19 8.02 0.0000 Region_lbl + Age VitD<20 2.19 8.02
## 44 VitD<20 4.31 2.08 8.90 0.0001 Region_lbl + Age VitD<20 2.08 8.90
## 45 VitD<20 5.16 2.28 11.69 0.0001 Region_lbl + Age VitD<20 2.28 11.69
## 46 VitD<20 5.36 2.37 12.08 0.0001 Region_lbl + Age VitD<20 2.37 12.08
## 47 VitD<20 24.00 9.41 61.21 0.0000 Region_lbl + Age VitD<20 9.41 61.21
## 48 VitD<20 17.86 5.62 56.73 0.0000 Region_lbl + Age VitD<20 5.62 56.73
## 49 VitD<12 0.99 0.96 1.01 0.3268 Region_lbl + Age VitD<12 0.96 1.01
## 50 VitD<12 0.73 0.14 3.68 0.7026 Region_lbl + Age VitD<12 0.14 3.68
## 51 VitD<12 0.83 0.17 3.96 0.8101 Region_lbl + Age VitD<12 0.17 3.96
## 52 VitD<12 1.43 0.31 6.59 0.6500 Region_lbl + Age VitD<12 0.31 6.59
## 53 VitD<12 1.55 0.39 6.09 0.5311 Region_lbl + Age VitD<12 0.39 6.09
## 54 VitD<12 3.68 1.00 13.46 0.0496 Region_lbl + Age VitD<12 1.00 13.46
## 55 VitD<12 2.95 0.92 9.48 0.0696 Region_lbl + Age VitD<12 0.92 9.48
## 56 VitD<12 2.59 0.63 10.61 0.1850 Region_lbl + Age VitD<12 0.63 10.61
## 57 VitD<12 1.18 0.26 5.50 0.8286 Region_lbl + Age VitD<12 0.26 5.50
## 58 VitD<12 2.90 0.87 9.72 0.0843 Region_lbl + Age VitD<12 0.87 9.72
## 59 VitD<12 7.32 2.28 23.55 0.0009 Region_lbl + Age VitD<12 2.28 23.55
## 60 VitD<12 3.98 1.06 14.97 0.0415 Region_lbl + Age VitD<12 1.06 14.97
## 61 VitD<12 4.57 1.29 16.16 0.0188 Region_lbl + Age VitD<12 1.29 16.16
## 62 VitD<12 14.97 4.44 50.47 0.0000 Region_lbl + Age VitD<12 4.44 50.47
## 63 VitD<12 23.64 6.65 84.04 0.0000 Region_lbl + Age VitD<12 6.65 84.04
## 64 VitD<30 1.00 0.97 1.02 0.7731 Region num + Age VitD<30 0.97 1.02
## 65 VitD<30 1.00 0.93 1.08 0.9460 Region num + Age VitD<30 0.93 1.08
## 66 VitD<20 1.00 0.98 1.02 0.9187 Region num + Age VitD<20 0.98 1.02
## 67 VitD<20 1.09 1.04 1.14 0.0005 Region num + Age VitD<20 1.04 1.14
## 68 VitD<12 0.99 0.96 1.01 0.3033 Region num + Age VitD<12 0.96 1.01
## 69 VitD<12 1.15 1.08 1.23 0.0000 Region num + Age VitD<12 1.08 1.23
## 70 VitD<30 0.99 0.97 1.02 0.5791 EN_lbl + Age VitD<30 0.97 1.02
## 71 VitD<30 0.27 0.05 1.53 0.1401 EN_lbl + Age VitD<30 0.05 1.53
## 72 VitD<30 1.00 0.49 2.04 0.9930 EN_lbl + Age VitD<30 0.49 2.04
## 73 VitD<30 0.99 0.52 1.89 0.9827 EN_lbl + Age VitD<30 0.52 1.89
## 74 VitD<30 2.52 0.65 9.81 0.1840 EN_lbl + Age VitD<30 0.65 9.81
## 75 VitD<20 1.00 0.98 1.01 0.5698 EN_lbl + Age VitD<20 0.98 1.01
## 76 VitD<20 1.03 0.20 5.28 0.9682 EN_lbl + Age VitD<20 0.20 5.28
## 77 VitD<20 1.20 0.77 1.89 0.4222 EN_lbl + Age VitD<20 0.77 1.89
## 78 VitD<20 1.31 0.85 2.03 0.2257 EN_lbl + Age VitD<20 0.85 2.03
## 79 VitD<20 1.52 0.64 3.61 0.3441 EN_lbl + Age VitD<20 0.64 3.61
## 80 VitD<12 0.99 0.96 1.02 0.3539 EN_lbl + Age VitD<12 0.96 1.02
## 81 VitD<12 0.10 0.01 0.89 0.0394 EN_lbl + Age VitD<12 0.01 0.89
## 82 VitD<12 0.77 0.40 1.47 0.4289 EN_lbl + Age VitD<12 0.40 1.47
## 83 VitD<12 0.64 0.32 1.27 0.2043 EN_lbl + Age VitD<12 0.32 1.27
## 84 VitD<12 3.01 1.10 8.23 0.0324 EN_lbl + Age VitD<12 1.10 8.23
## exposure exposure_lbl
## 1 Edad Edad
## 2 Edad Edad
## 3 Edad Edad
## 4 AreaRural AreaRural
## 5 Edad Edad
## 6 AreaRural AreaRural
## 7 Edad Edad
## 8 AreaRural AreaRural
## 9 Edad Edad
## 10 Edad Edad
## 11 Educational_levelMid Educational_levelMid
## 12 Educational_levelHigh Educational_levelHigh
## 13 Edad Edad
## 14 Educational_levelMid Educational_levelMid
## 15 Educational_levelHigh Educational_levelHigh
## 16 Edad Edad
## 17 Educational_levelMid Educational_levelMid
## 18 Educational_levelHigh Educational_levelHigh
## 19 Edad Edad
## 20 Region_lblTarapacá Region_lblTarapacá
## 21 Region_lblAntofagasta Region_lblAntofagasta
## 22 Region_lblAtacama Region_lblAtacama
## 23 Region_lblCoquimbo Region_lblCoquimbo
## 24 Region_lblValparaíso Region_lblValparaíso
## 25 Region_lblMetropolitana Region_lblMetropolitana
## 26 Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 27 Region_lblMaule Region_lblMaule
## 28 Region_lblBiobío Region_lblBiobío
## 29 Region_lblAraucanía Region_lblAraucanía
## 30 Region_lblLos Ríos Region_lblLos Ríos
## 31 Region_lblLos Lagos Region_lblLos Lagos
## 32 Region_lblAysén Region_lblAysén
## 33 Region_lblMagallanes Region_lblMagallanes
## 34 Edad Edad
## 35 Region_lblTarapacá Region_lblTarapacá
## 36 Region_lblAntofagasta Region_lblAntofagasta
## 37 Region_lblAtacama Region_lblAtacama
## 38 Region_lblCoquimbo Region_lblCoquimbo
## 39 Region_lblValparaíso Region_lblValparaíso
## 40 Region_lblMetropolitana Region_lblMetropolitana
## 41 Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 42 Region_lblMaule Region_lblMaule
## 43 Region_lblBiobío Region_lblBiobío
## 44 Region_lblAraucanía Region_lblAraucanía
## 45 Region_lblLos Ríos Region_lblLos Ríos
## 46 Region_lblLos Lagos Region_lblLos Lagos
## 47 Region_lblAysén Region_lblAysén
## 48 Region_lblMagallanes Region_lblMagallanes
## 49 Edad Edad
## 50 Region_lblTarapacá Region_lblTarapacá
## 51 Region_lblAntofagasta Region_lblAntofagasta
## 52 Region_lblAtacama Region_lblAtacama
## 53 Region_lblCoquimbo Region_lblCoquimbo
## 54 Region_lblValparaíso Region_lblValparaíso
## 55 Region_lblMetropolitana Region_lblMetropolitana
## 56 Region_lblL. B. O'Higgins Region_lblL. B. O'Higgins
## 57 Region_lblMaule Region_lblMaule
## 58 Region_lblBiobío Region_lblBiobío
## 59 Region_lblAraucanía Region_lblAraucanía
## 60 Region_lblLos Ríos Region_lblLos Ríos
## 61 Region_lblLos Lagos Region_lblLos Lagos
## 62 Region_lblAysén Region_lblAysén
## 63 Region_lblMagallanes Region_lblMagallanes
## 64 Edad Edad
## 65 Codigoregion Codigoregion
## 66 Edad Edad
## 67 Codigoregion Codigoregion
## 68 Edad Edad
## 69 Codigoregion Codigoregion
## 70 Edad Edad
## 71 EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 72 EN_lblOverW (_.) EN_lblOverW (_.)
## 73 EN_lblObese (_.) EN_lblObese (_.)
## 74 EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)
## 75 Edad Edad
## 76 EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 77 EN_lblOverW (_.) EN_lblOverW (_.)
## 78 EN_lblObese (_.) EN_lblObese (_.)
## 79 EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)
## 80 Edad Edad
## 81 EN_lblUnderweight (<.) EN_lblUnderweight (<.)
## 82 EN_lblOverW (_.) EN_lblOverW (_.)
## 83 EN_lblObese (_.) EN_lblObese (_.)
## 84 EN_lblMorbid_obese(+) EN_lblMorbid_obese(+)## outcome OR OR_i OR_s pvalue model
## Educational_levelMid VitD<12 2.74 1.19 6.31 0.0179 Region num + Age
## Educational_levelHigh VitD<12 3.59 1.51 8.52 0.0039 Region num + Age## outcome OR OR_i OR_s pvalue model
## Educational_levelMid VitD<12 2.51 1.08 5.83 0.0326 Region num + Age
## Educational_levelHigh VitD<12 3.29 1.32 8.19 0.0106 Region num + Age
## Edad VitD<12 0.99 0.96 1.02 0.4769 Region num + Age## outcome OR OR_i OR_s pvalue model
## Educational_levelMid VitD<12 2.33 1.01 5.33 0.0465 Region num + Age
## Educational_levelHigh VitD<12 3.06 1.23 7.59 0.0164 Region num + Age
## Edad VitD<12 0.99 0.96 1.02 0.4324 Region num + Age
## EN_lblUnderweight (<18.5) VitD<12 0.11 0.01 0.95 0.0455 Region num + Age
## EN_lblOverW (25_29.9) VitD<12 0.79 0.42 1.48 0.4565 Region num + Age
## EN_lblObese (30_39.9) VitD<12 0.70 0.36 1.37 0.3033 Region num + Age
## EN_lblMorbid_obese(40+) VitD<12 3.19 1.15 8.87 0.0263 Region num + Age## outcome OR OR_i OR_s pvalue model
## Educational_levelMid VitD<12 2.61 1.11 6.10 0.0276 Region num + Age
## Educational_levelHigh VitD<12 3.41 1.34 8.68 0.0102 Region num + Age
## Edad VitD<12 0.99 0.96 1.02 0.4049 Region num + Age
## factor(ta3)2 VitD<12 0.39 0.15 1.01 0.0529 Region num + Age
## factor(ta3)3 VitD<12 0.47 0.23 0.94 0.0321 Region num + Age
## factor(ta3)4 VitD<12 0.76 0.42 1.39 0.3786 Region num + Age## outcome OR OR_i OR_s pvalue
## Educational_levelMid VitD<12 1.85 0.65 5.29 0.2483
## Educational_levelHigh VitD<12 2.26 0.74 6.86 0.1525
## Edad VitD<12 0.98 0.95 1.01 0.1602
## factor(m7p4)-4444 VitD<12 23076.30 1528.19 348462.40 0.0000
## factor(m7p4)0 VitD<12 3387694.74 273357.77 41983353.40 0.0000
## factor(m7p4)0.5 VitD<12 1776021.25 202180.44 15601170.08 0.0000
## factor(m7p4)1 VitD<12 961902.80 124582.52 7426860.39 0.0000
## factor(m7p4)1.5 VitD<12 60788.31 4656.43 793573.67 0.0000
## factor(m7p4)2 VitD<12 517415.99 65405.65 4093214.01 0.0000
## factor(m7p4)2.5 VitD<12 23762.18 1092.56 516806.09 0.0000
## factor(m7p4)3 VitD<12 881886.07 106760.10 7284772.24 0.0000
## factor(m7p4)3.5 VitD<12 19033388.41 558700.97 648414611.63 0.0000
## factor(m7p4)4 VitD<12 1160162.26 118208.06 11386502.98 0.0000
## factor(m7p4)4.5 VitD<12 0.78 0.05 12.68 0.8583
## factor(m7p4)5 VitD<12 168469.12 11741.92 2417138.72 0.0000
## factor(m7p4)6 VitD<12 1677682.52 104407.85 26957921.99 0.0000
## factor(m7p4)7 VitD<12 26100.88 1158.50 588049.64 0.0000
## factor(m7p4)8 VitD<12 0.43 0.04 4.61 0.4894
## factor(m7p4)9 VitD<12 0.67 0.04 11.50 0.7851
## factor(m7p4)10 VitD<12 0.37 0.04 3.68 0.3970
## model
## Educational_levelMid Region num + Age
## Educational_levelHigh Region num + Age
## Edad Region num + Age
## factor(m7p4)-4444 Region num + Age
## factor(m7p4)0 Region num + Age
## factor(m7p4)0.5 Region num + Age
## factor(m7p4)1 Region num + Age
## factor(m7p4)1.5 Region num + Age
## factor(m7p4)2 Region num + Age
## factor(m7p4)2.5 Region num + Age
## factor(m7p4)3 Region num + Age
## factor(m7p4)3.5 Region num + Age
## factor(m7p4)4 Region num + Age
## factor(m7p4)4.5 Region num + Age
## factor(m7p4)5 Region num + Age
## factor(m7p4)6 Region num + Age
## factor(m7p4)7 Region num + Age
## factor(m7p4)8 Region num + Age
## factor(m7p4)9 Region num + Age
## factor(m7p4)10 Region num + Age## outcome OR OR_i OR_s pvalue model
## Educational_levelMid VitD<12 1.99 0.69 5.71 0.2009 Region num + Age
## Educational_levelHigh VitD<12 2.30 0.75 7.08 0.1473 Region num + Age
## Edad VitD<12 0.98 0.95 1.01 0.2026 Region num + Age
## m7p4_valida VitD<12 0.84 0.67 1.06 0.1409 Region num + Age## outcome OR OR_i OR_s pvalue model
## Educational_levelMid VitD<12 1.88 0.67 5.30 0.2347 Region num + Age
## Educational_levelHigh VitD<12 2.23 0.72 6.92 0.1665 Region num + Age
## Edad VitD<12 0.99 0.95 1.02 0.4504 Region num + Age
## m7p10b VitD<12 1.00 0.87 1.15 0.9661 Region num + Age##
## Call:
## svyglm(formula = outcome3 ~ Educational_level, design = survey_design,
## family = quasibinomial(link = "logit"), deff = TRUE)
##
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.7162 0.3985 -6.816 1.91e-11 ***
## Educational_levelMid 1.0089 0.4251 2.373 0.01788 *
## Educational_levelHigh 1.2785 0.4411 2.899 0.00386 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.000632)
##
## Number of Fisher Scoring iterations: 5##
## Call:
## svyglm(formula = outcome3 ~ Educational_level + Edad, design = survey_design,
## family = quasibinomial(link = "logit"), deff = TRUE)
##
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.327532 0.674255 -3.452 0.000587 ***
## Educational_levelMid 0.920381 0.429801 2.141 0.032559 *
## Educational_levelHigh 1.191802 0.464929 2.563 0.010557 *
## Edad -0.009784 0.013748 -0.712 0.476881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9994508)
##
## Number of Fisher Scoring iterations: 5## Stratified 1 - level Cluster Sampling design (with replacement)
## With (792) clusters.
## subset(survey_design, Edad < 50 & Sexo == 2)
## Sampling variables:
## - ids: Conglomerado_
## - strata: strata_
## - weights: fexp
##
## Call: svyglm(formula = outcome3 ~ Educational_level + Edad + EN_lbl,
## design = survey_design, family = quasibinomial(link = "logit"),
## deff = TRUE)
##
## Coefficients:
## (Intercept) Educational_levelMid
## -2.09051 0.84388
## Educational_levelHigh Edad
## 1.11678 -0.01159
## EN_lblUnderweight (<18.5) EN_lblOverW (25_29.9)
## -2.19212 -0.24172
## EN_lblObese (30_39.9) EN_lblMorbid_obese(40+)
## -0.35034 1.16086
##
## Degrees of Freedom: 1581 Total (i.e. Null); 755 Residual
## (1 observation deleted due to missingness)
## Null Deviance: 1392
## Residual Deviance: 1342 AIC: NA