Recently I read a paper published in JAMA Pediatrics(doi:10.1001/jamapediatrics.2019.1212), the authors provide the P value of the mediating variables using the -khb- command in Stata (Table 2, listed below). Array
I want to estimate the the P value in Summary of confounding part(Conf_Pct column) and Components of Difference part(P_Reduced column), as illustrated in Table 2, but didnot know how.
The -khb- is user-written program and can be installed by command:
HTML Code:
. net sj 13-1 st0236_2 . net install st0236_2 // INSTALLATION FILES . net get st0236_2 // ANCILLARY FILES, including dlsy_khb.dta and khb.do
HTML Code:
. use dlsy_khb.dta
. khb logit univ fses || abil intact boy, disentangle summary verbose
(omitted)
Logistic regression Number of obs = 1,896
LR chi2(4) = 216.87
Prob > chi2 = 0.0000
Log likelihood = -468.31516 Pseudo R2 = 0.1880
------------------------------------------------------------------------------
univ | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
fses | .3817324 .0778061 4.91 0.000 .2292353 .5342295
abil | 1.065516 .106775 9.98 0.000 .8562405 1.274791
intact | 1.08391 .7386558 1.47 0.142 -.3638292 2.531648
boy | .9821406 .1848351 5.31 0.000 .6198704 1.344411
_cons | -4.462997 .7479123 -5.97 0.000 -5.928878 -2.997116
------------------------------------------------------------------------------
(omitted)
Logistic regression Number of obs = 1,896
LR chi2(4) = 216.87
Prob > chi2 = 0.0000
Log likelihood = -468.31516 Pseudo R2 = 0.1880
------------------------------------------------------------------------------
univ | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
fses | .5805281 .0786111 7.38 0.000 .4264531 .7346031
__000001 | 1.065516 .106775 9.98 0.000 .8562405 1.274791
__000002 | 1.08391 .7386558 1.47 0.142 -.3638292 2.531648
__000003 | .9821406 .1848351 5.31 0.000 .6198704 1.344411
_cons | -2.945969 .124697 -23.63 0.000 -3.190371 -2.701568
------------------------------------------------------------------------------
Decomposition using the KHB-Method
Model-Type: logit Number of obs = 1896
Variables of Interest: fses Pseudo R2 = 0.19
Z-variable(s): abil intact boy
------------------------------------------------------------------------------
univ | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
fses |
Reduced | .5805281 .0786111 7.38 0.000 .4264531 .7346031
Full | .3817324 .0778061 4.91 0.000 .2292353 .5342295
Diff | .1987956 .0359394 5.53 0.000 .1283557 .2692355
------------------------------------------------------------------------------
Summary of confounding
Variable | Conf_ratio Conf_Pct Resc_Fact
-------------+-------------------------------------
fses | 1.5207722 34.24 1.1317064
---------------------------------------------------
Components of Difference
Z-Variable | Coef Std_Err P_Diff P_Reduced
-------------+---------------------------------------------
fses |
abil | .1661177 .0301003 83.56 28.61
intact | .020142 .0144611 10.13 3.47
boy | .0125359 .011524 6.31 2.16
-----------------------------------------------------------
The user-written program -khb-, created by Ulrich Kohler, Kristian Bernt Karlson, and Anders Holm, and detailed in the following article:
Kohler, U., K.B. Karlson, and A. Holm. 2011. "Comparing Coefficients of Nested Nonlinear Probability Models." Stata Journal, 11(3): 420-38.
https://journals.sagepub.com/doi/pdf...867X1101100306
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