This is my mediation analysis:
# Set seed for reproducibility
set.seed(123)
# 500 boots are used for illustration
mediation_analysis <- cmest(data = mediator_df,
model = "gformula",
exposure = "Treatment_type_tumor_near_gallbladder",
outcome = "complications",
mediator = "treatment_approach_Open",
mval = list("Yes"), # a list specifying a value for each variable in mediator at which the variable is controlled
mreg = list("logistic"), # a list specifying the type of regression model for each variable in mediator
basec = predictors_log_regression,
EMint = TRUE, # a logical value indicating the existence of exposure-mediator interaction
yreg = "logistic",
yval = list("Yes"), # the value of the outcome at which causal effects on the risk/odds ratio scale are estimated (used when the outcome is categorical
astar = "Resection", # the control value for the exposure.
a = "MWA", # the active value for the exposure.
estimation = "imputation",
inference = "bootstrap",
nboot = 1000, # Increase to 100 or 200
)
Output:
# Effect decomposition on the odds ratio scale via the g-formula approach
Direct counterfactual imputation estimation with
bootstrap standard errors, percentile confidence intervals and p-values
Estimate Std.error 95% CIL 95% CIU P.val
Rcde 0.28803 0.23732 0.07998 0.892 0.034 *
Rpnde 0.36401 0.21550 0.14060 0.920 0.034 *
Rtnde 0.55629 0.39298 0.20651 1.587 0.264
Rpnie 0.63179 0.21784 0.29288 1.140 0.140
Rtnie 0.96551 0.29564 0.55420 1.722 0.840
Rte 0.35146 0.16038 0.16959 0.803 0.012 *
ERcde -0.57169 0.23402 -0.95550 -0.060 0.034 *
ERintref -0.06430 0.12692 -0.25461 0.229 0.682
ERintmed 0.35566 0.25017 -0.22605 0.742 0.220
ERpnie -0.36821 0.21784 -0.70712 0.140 0.140
ERcde(prop) 0.88150 0.74668 0.10666 1.799 0.038 *
ERintref(prop) 0.09915 0.40216 -0.58888 0.408 0.670
ERintmed(prop) -0.54839 0.81268 -1.73027 0.426 0.224
ERpnie(prop) 0.56775 0.68300 -0.28519 1.691 0.148
pm 0.01936 0.88462 -0.33611 0.735 0.836
int -0.44925 0.94900 -2.20855 0.757 0.436
pe 0.11850 0.74668 -0.79871 0.893 0.694
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Rcde: controlled direct effect odds ratio; Rpnde: pure natural direct effect odds ratio; Rtnde: total natural direct effect odds ratio; Rpnie: pure natural indirect effect odds ratio; Rtnie: total natural indirect effect odds ratio; Rte: total effect odds ratio; ERcde: excess relative risk due to controlled direct effect; ERintref: excess relative risk due to reference interaction; ERintmed: excess relative risk due to mediated interaction; ERpnie: excess relative risk due to pure natural indirect effect; ERcde(prop): proportion ERcde; ERintref(prop): proportion ERintref; ERintmed(prop): proportion ERintmed; ERpnie(prop): proportion ERpnie; pm: overall proportion mediated; int: overall proportion attributable to interaction; pe: overall proportion eliminated)
The estimates are expressed as OR.
However, If I perform the sensitivity analysis, the estimates are expressed as RR.
# Perform the sensitivity analysis
cmsens(mediation_analysis, sens = 'uc') # uc: unmeasured confounding
Output:
Sensitivity Analysis For Unmeasured Confounding
Evalues on the risk or rate ratio scale:
estRR lowerRR upperRR Evalue.estRR Evalue.lowerRR Evalue.upperRR
Rcde 0.2880281 0.0799762 0.8915158 6.401405 NA 1.491134
Rpnde 0.3640097 0.1406019 0.9203564 4.938028 NA 1.393169
Rtnde 0.5562854 0.2065078 1.5872507 2.995079 NA 1.000000
Rpnie 0.6317902 0.2928753 1.1399729 2.543254 NA 1.000000
Rtnie 0.9655119 0.5541981 1.7223137 1.228063 NA 1.000000
Rte 0.3514557 0.1695936 0.8030456 5.136700 NA 1.797900
Is it normal or am I doing something wrong?
Thank you for your help.
P.s. The event of my interest is not rare.
This is my mediation analysis:
The estimates are expressed as OR.
However, If I perform the sensitivity analysis, the estimates are expressed as RR.
Is it normal or am I doing something wrong?
Thank you for your help.
P.s. The event of my interest is not rare.