Hello, stata community. I would like your help once again,
I am using mixed effects linear regression to evaluate food consumption as an exposure in the first years of life and child growth at age 5 as an outcome. In this example, I have as an exposure variable food consumption up to the first 3 months of life (cenario - variable with 3 categories) and as an outcome variable, repeated measurements of height for age (Variable z_altidade) in these children up to 5 years of age (however, the amount of measurements per child (id) varies (2-46 measurements). I want to know how food consumption in the first 3 months of life affects the time at 5 years. I used the following syntax:
mixed z_altidade i.cenario c.idade_meses_en#c.idade_meses_en#c.idade_meses_en || id: idade_meses_en, cov(un)
P.S.1: Also note that age is the variable that marks time (variable idade_meses_en).
P.S.2: For age as a function of height/age, the cubic model was the best fit
The question is: I'm not sure if I should add another interaction term referring to the cenario Variable with time
Below is the baseline's model, as well as the regression output. I'm using Stata version 17
Thank you for all your help
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I am using mixed effects linear regression to evaluate food consumption as an exposure in the first years of life and child growth at age 5 as an outcome. In this example, I have as an exposure variable food consumption up to the first 3 months of life (cenario - variable with 3 categories) and as an outcome variable, repeated measurements of height for age (Variable z_altidade) in these children up to 5 years of age (however, the amount of measurements per child (id) varies (2-46 measurements). I want to know how food consumption in the first 3 months of life affects the time at 5 years. I used the following syntax:
mixed z_altidade i.cenario c.idade_meses_en#c.idade_meses_en#c.idade_meses_en || id: idade_meses_en, cov(un)
P.S.1: Also note that age is the variable that marks time (variable idade_meses_en).
P.S.2: For age as a function of height/age, the cubic model was the best fit
The question is: I'm not sure if I should add another interaction term referring to the cenario Variable with time
Below is the baseline's model, as well as the regression output. I'm using Stata version 17
Thank you for all your help
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Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long id byte sexo_ca float(cenario idade_meses_en z_altidade) 10242453 0 1 1.8069816 .16006504 10242453 0 1 2.7268994 .4580684 10450108 1 2 1.8069816 -.8858762 10450108 1 2 3.0554416 -1.1882422 10450108 1 2 3.7125256 -.2099427 10450108 1 2 6.40657 -.7585881 10450108 1 2 8.279261 .1816332 10450108 1 2 9.52772 -.4318246 10450108 1 2 18.201231 -.8374036 10450108 1 2 32.722794 -.3916168 10450108 1 2 41.88912 .1921666 10576959 0 1 0 -.07929426 10576959 0 1 .952772 2.604984 10576959 0 1 2.956879 .9491886 10576959 0 1 5.749487 .8883988 10576959 0 1 7.162218 .7397134 10576959 0 1 10.38193 .6152673 10576959 0 1 13.601643 -.08274923 11055885 1 0 3.022587 .27920705 11055885 1 0 4.106776 .5353787 11055885 1 0 5.026694 .04612643 11055885 1 0 6.012321 -.7585881 11055885 1 0 6.866529 .6430932 11532240 1 1 1.5112936 1.682775 11532240 1 1 2.3326488 .28748262 11532240 1 1 3.022587 .27920705 11532240 1 1 4.6324434 1.0159707 11532240 1 1 9.067762 1.3512913 11532240 1 1 12.747434 2.209877 11673070 1 0 .16427104 .06116878 11673070 1 0 13.010267 1.2701492 17647115 0 0 .3285421 2.604984 17647115 0 0 1.5112936 2.1045804 17647115 0 0 2.49692 2.4225495 17650918 0 0 .3285421 .9944171 17650918 0 0 18.299795 .10058954 17658588 1 1 .3285421 -.4670638 17658588 1 1 1.3470227 -.8858762 27974818 1 0 1.0841889 -2.940797 27974818 1 0 2.4640656 -1.712049 27974818 1 0 4.36961 -1.3869892 27974818 1 0 5.420945 -2.3218367 27974818 1 0 7.030801 -.5363694 29640336 1 2 11.07187 -.23131172 29640336 1 2 17.511293 .6632237 29640336 1 2 20.04107 .2844399 29640336 1 2 27.597536 2.2812006 29640336 1 2 31.04723 1.5428514 29640336 1 2 32.29569 1.3168868 31190159 1 0 3.811088 .27920705 31190159 1 0 6.537988 .6430932 31190159 1 0 7.819302 -.07576776 31190159 1 0 8.870637 1.0884466 31190159 1 0 14.160164 -.423727 31190159 1 0 15.967146 -.05762093 31190159 1 0 17.577002 .6632237 31190159 1 0 22.07803 -.016256064 32832552 0 0 .3285421 -.07929426 32832552 0 0 1.0184804 -1.8867936 32832552 0 0 2.2669406 -.03305185 32832552 0 0 4.0410676 -2.3515866 33695767 1 0 .3613963 .06116878 33695767 1 0 1.2156057 -1.6564716 33695767 1 0 2.0041068 -3.461639 33695767 1 0 3.581109 -1.9219668 33695767 1 0 9.724846 -.4318246 33718216 0 0 1.577002 -1.375079 33718216 0 0 3.482546 -.3814132 34955417 0 0 .3613963 .9944171 34955417 0 0 1.8069816 1.695209 34955417 0 0 7.589323 1.1716125 34955417 0 0 17.839836 .11547854 36421115 0 0 1.4455853 .16006504 36421115 0 0 3.088296 -.8564585 36421115 0 0 10.644764 -.6005127 36421115 0 0 13.404518 -.4630341 37565537 1 1 3.12115 -.6990924 37565537 1 1 6.702259 1.5775474 39098684 1 1 1.281314 -4.481988 39098684 1 1 2.858316 -4.711346 39121618 1 0 2.529774 .28748262 39121618 1 0 3.909651 .7683568 39121618 1 0 5.749487 .51971906 39121618 1 0 6.866529 .6430932 39121618 1 0 8.279261 -.27177352 39121618 1 0 9.199179 -.8776035 39121618 1 0 10.414784 .3145875 39121618 1 0 11.498974 .6273015 39121618 1 0 12.41889 .5265452 39223276 1 0 .952772 1.6458665 39223276 1 0 2.792608 -.9622246 40501789 1 2 1.4784395 -.8858762 40501789 1 2 4.533881 -1.8675812 40501789 1 2 5.388091 -2.795429 40502209 1 0 3.5482545 3.2141056 40502209 1 0 4.2710476 2.9383385 40502209 1 0 5.223819 2.4140894 40502209 1 0 9.002053 2.242849 40502209 1 0 10.579056 .752242 40502209 1 0 21.355236 .9968844 end label values sexo_ca sexo label def sexo 0 "feminino", modify label def sexo 1 "masculino", modify label values cenario cenario label def cenario 0 "LM exclusivo", modify label def cenario 1 "Substitutos do leite materno", modify label def cenario 2 "IA precoce", modify
