### Mediator model

Call:
glm(formula = bili_bin ~ trt, family = binomial(link = "logit"), 
    data = data)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  0.08577    0.38245   0.224    0.823
trt         -0.08577    0.24091  -0.356    0.722

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 382.49  on 275  degrees of freedom
Residual deviance: 382.36  on 274  degrees of freedom
AIC: 386.36

Number of Fisher Scoring iterations: 3

### Outcome model

Call:
survival::survreg(formula = Surv(time, status) ~ trt + bili_bin, 
    data = data, dist = "weibull")
              Value Std. Error     z       p
(Intercept)  8.8180     0.2476 35.61 < 2e-16
trt          0.0621     0.1316  0.47    0.64
bili_bin    -1.2703     0.1668 -7.61 2.6e-14
Log(scale)  -0.2917     0.0735 -3.97 7.3e-05

Scale= 0.747 

Weibull distribution
Loglik(model)= -1162.4   Loglik(intercept only)= -1203.8
	Chisq= 82.77 on 2 degrees of freedom, p= 1.1e-18 
Number of Newton-Raphson Iterations: 5 
n= 276 

### Mediation analysis 
            est         se         Z         p      lower     upper exp(est)
cde  0.07453720 0.15791407 0.4720112 0.6369188 -0.2349687 0.3840431 1.077385
pnde 0.07453720 0.15791407 0.4720112 0.6369188 -0.2349687 0.3840431 1.077385
tnie 0.02839131 0.07937794 0.3576726 0.7205884 -0.1271866 0.1839692 1.028798
tnde 0.07453720 0.15791407 0.4720112 0.6369188 -0.2349687 0.3840431 1.077385
pnie 0.02839131 0.07937794 0.3576726 0.7205884 -0.1271866 0.1839692 1.028798
te   0.10292851 0.17669777 0.5825117 0.5602221 -0.2433927 0.4492498 1.108412
pm   0.28619246 0.71391251 0.4008789 0.6885093 -1.1130503 1.6854353       NA
     exp(lower) exp(upper)
cde   0.7905956   1.468209
pnde  0.7905956   1.468209
tnie  0.8805694   1.201979
tnde  0.7905956   1.468209
pnie  0.8805694   1.201979
te    0.7839636   1.567136
pm           NA         NA

Evaluated at:
avar: trt
 a1 (intervened value of avar) = 2.3
 a0 (reference value of avar)  = 1.1
mvar: bili_bin
 m_cde (intervend value of mvar for cde) = 1.4
cvar: 
 c_cond (covariate vector value) = 

Note that effect estimates do not vary over m_cde and c_cond values when interaction = FALSE.