### 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: MASS::glm.nb(formula = platelet ~ trt + bili_bin + trt:bili_bin, data = data, init.theta = 7.84536718, link = log) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.48429 0.09753 56.229 <2e-16 *** trt 0.09494 0.06102 1.556 0.1197 bili_bin 0.09973 0.13860 0.720 0.4718 trt:bili_bin -0.15251 0.08730 -1.747 0.0806 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for Negative Binomial(7.8454) family taken to be 1) Null deviance: 294.66 on 275 degrees of freedom Residual deviance: 282.37 on 272 degrees of freedom AIC: 3280.8 Number of Fisher Scoring iterations: 1 Theta: 7.845 Std. Err.: 0.677 2 x log-likelihood: -3270.762 ### Mediation analysis est se Z p lower upper cde -0.142288731 0.108906168 -1.3065259 0.1913738 -0.355740897 0.07116343 pnde 0.030090423 0.052979075 0.5679681 0.5700566 -0.073746655 0.13392750 tnie 0.006394558 0.018043844 0.3543900 0.7230466 -0.028970726 0.04175984 tnde 0.034738909 0.053738206 0.6464471 0.5179898 -0.070586038 0.14006386 pnie 0.001746071 0.005106238 0.3419487 0.7323895 -0.008261971 0.01175411 te 0.036484981 0.054658491 0.6675080 0.5044477 -0.070643692 0.14361365 pm 0.177912755 0.501670080 0.3546410 0.7228586 -0.805342534 1.16116804 exp(est) exp(lower) exp(upper) cde 0.8673708 0.7006541 1.073757 pnde 1.0305477 0.9289070 1.143310 tnie 1.0064150 0.9714449 1.042644 tnde 1.0353494 0.9318476 1.150347 pnie 1.0017476 0.9917721 1.011823 te 1.0371587 0.9317938 1.154438 pm NA 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 can vary over m_cde and c_cond values when interaction = TRUE.