Mediation analysis confidence intervals versus p-values

[Anna Warren]

Hello,

I am running a mediation analysis with a binary predictor, a binary mediator and a binary outcome. I used paramed on stata to run this. I got the following output, but am confused as the confidence intervals suggest each of the effects should be significant, but the p values do not

| Estimate Std Err P>|z| [95% Conf Interval]
--------------------------------------------------------------------
cde | .68979196 .11338915 (0.001) [.5523309, .86146358]
nde | .68979196 .2267783 (0.102) [.44226294, 1.0758599]
nie | 1.1819007 .05215623 (0.001) [1.0670499, 1.3091135]
mte | .81526564 .2319472 (0.379) [.51744202, 1.2845073]

cde: controlled direct effect, nde: natural direct effect, nie: natural indirect effect, mte: marginal total effect

I am unsure how to interpret the results in this case. I would be grateful if anyone has any insight into this. Could it be related to the fact that my variables are binary?

Thanks for your help

[eric_a_booth]

These are odds ratios (to be fair, I dont see it mentioned in the help file that the estimates are ORs for binary vars). So you get a significant p-value when the CI does not include 1 . This might help: https://stats.idre.ucla.edu/stata/fa...ic-regression/

[Anna Warren]

These are odds ratios (to be fair, I dont see it mentioned in the help file that the estimates are ORs for binary vars). So you get a significant p-value when the CI does not include 1 . This might help: https://stats.idre.ucla.edu/stata/fa...ic-regression/

Great thanks that makes a lot more sense now.

I'm also a little confused about how these confidence intervals have been calculated. I thought CI's for 95% level were the coefficient +/- 1.96 x SE, but that doesn't seem to be the case here. Is some form of transformation needed in the case of binary variables as it is a logistic regression?

[eric_a_booth]

You'd want to get your critical value from the t-distribution via invttail() in lieu of just using 1.96. Here's a quick example:

sysuse auto, clear

regress mpg price turn
 di _b[price] - invttail(71,0.025) * _se[price]

 
 paramed mpg, avar(for) mvar(price) a0(0) a1(1) m(1200) yreg(linear) mreg(linear)
  di  312.2587  - invttail(72,0.025) * 754.4488