This document aims to illustrate the usage of the functions
exactmed(), exactmed_c() and
exactmed_cat(), as well as their behavior via additional
examples. All functions compute natural direct and indirect effects, and
controlled direct effects for a binary outcome. However each function
handles a specific type of mediator: exactmed()
accommodates a binary mediator, exactmed_c() a
continuous mediator and exactmed_cat() a
categorical mediator. Details on the use of the function
exactmed() are provided next. Usage of
exactmed_c() and exactmed_cat() is similar to
that of exactmed(), but differs on some aspects described
thereafter.
In exactmed(), the user can specify the high levels of
the outcome and mediator variables using the input parameters
hvalue_m and hvalue_y, respectively (see the
function help). Controlled direct effects are obtained for both possible
mediator values (\(m=0\) and \(m=1\)). Natural and controlled effects can
either be unadjusted (crude) or adjusted for covariates (that is,
conditional effects). By default, adjusted effects estimates are
obtained for covariates fixed at their sample-specific mean values (for
numerical covariates and categorical covariates through associated
dummies). Alternatively, adjusted effects estimates can be obtained for
specific values of the covariates that are user-provided. Also, by
default, exactmed() incorporates a mediator-exposure
interaction term in the outcome model, which can be removed by setting
interaction=FALSE. Concerning interval estimates,
exactmed() generates, by default, \(95\%\) confidence intervals obtained by the
delta method. Alternatively, percentile bootstrap confidence intervals,
instead of delta method confidence intervals, can be obtained by
specifying boot=TRUE in the function call. In this case,
1000 bootstrap data sets are generated by default.
In exactmed_c() and exactmed_cat(), only
the high level of the outcome variable can be specified (using the input
parameter hvalue_y). Moreover, for each scale, the
controlled direct effect is computed at a mediator value or level
specified by the user using the parameter mf. By default,
this parameter is fixed at the sample-specific mean of the mediator in
exactmed_c(), whereas it is fixed at the reference level of
the mediator in exactmed_cat(). In order to use
exactmed_cat(), the mediator must be coded as a factor
variable in the data set. By default, the reference level of the
mediator is the first level of the corresponding factor variable. The
extra input parameter blevel_m of the
exactmed_cat() function allows the user to change the
default reference level to any other level. It is worth noting that
parameter blevel_m only potentially impacts the value of
the controlled direct effect (not the natural direct and indirect
effects).
Due to the similarity between exactmed(),
exactmed_c() and exactmed_cat() in terms of
use and options offered to the user, most examples will be presented
with the exactmed() function. In all the
exactmed() examples presented below we use the data set
datamed, available after loading the
ExactMed package. Some of the features of this data set
can be found in its corresponding help file
(help(datamed)). A user interested in the
exactmed_c() or exactmed_cat() functions for
the continuous or categorical mediator cases, respectively, will only
need to change the name of the function (and data set) in the calling of
these examples to understand their use. The data sets
datamed_c and datamed_cat, which feature a
continuous and a categorical mediator, respectively, are presented at
the end of the document along with a few calling examples.
Lastly, we recall that all ExactMed functions only work on data frames with named columns and no missing values.
> library(ExactMed)
>
> head(datamed)
X M Y C1 C2
1 1 1 0 0 0.3753731
2 1 0 0 1 0.1971635
3 1 0 1 1 -0.5971041
4 0 1 0 0 0.7576990
5 0 1 0 0 1.2056864
6 0 1 0 0 -0.5983204The following command verifies whether the data set contains any missing values:
>
> as.logical(sum(is.na(datamed)))
[1] FALSESuppose that one wishes to obtain unadjusted (crude) mediation effects estimates for a change in exposure from \(0\) to \(1\), assuming there is no exposure-mediator interaction and using the delta method to construct \(95\%\) confidence intervals.
In this case, a valid call to exactmed() would be:
>
> results1 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y',
+ a1 = 1, a0 = 0, interaction = FALSE
+ )
'exactmed' will compute unadjusted natural effects
>
> results1
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.04899 0.27469 1.57554 2.66472 8.7484e-08
Indirect effect 0.88069 0.03097 0.82204 0.94352 0.00030218
Total effect 1.80452 0.23756 1.39414 2.33571 7.3256e-06
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.30183 0.06489 1.18065 1.43543 1.2130e-07
Indirect effect 0.96248 0.01014 0.94281 0.98257 0.00028454
Total effect 1.25298 0.06379 1.13399 1.38446 9.4328e-06
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.16514 0.03021 0.10593 0.22435 4.5850e-08
Indirect effect -0.02672 0.00732 -0.04107 -0.01238 0.00026137
Total effect 0.13842 0.03048 0.07868 0.19815 5.5775e-06
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.07495 0.28607 1.58363 2.71870 1.1937e-07
RR scale 1.26843 0.05652 1.16234 1.38420 9.5079e-08
RD scale 0.15878 0.02892 0.10210 0.21546 4.0057e-08
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.07495 0.28607 1.58363 2.71870 1.1937e-07
RR scale 1.40138 0.09725 1.22317 1.60555 1.1566e-06
RD scale 0.17947 0.03343 0.11395 0.24499 7.9365e-08
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.81241 0.09811 -8.281 < 2e-16 ***
X 0.90623 0.13212 6.859 6.92e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.3 on 999 degrees of freedom
Residual deviance: 1310.8 on 998 degrees of freedom
AIC: 1314.8
Number of Fisher Scoring iterations: 4
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3702 0.1015 3.648 0.000264 ***
X 0.7299 0.1379 5.294 1.19e-07 ***
M -0.5825 0.1388 -4.198 2.69e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1291.9 on 997 degrees of freedom
AIC: 1297.9
Number of Fisher Scoring iterations: 4Mediation effects estimates adjusted for covariates are obtained
through the use of the character vectors m_cov and
y_cov, which contain the names of the covariates to be
adjusted for in the mediator and outcome models, respectively. The
following call to exactmed() incorporates covariates
C1 and C2 in both the mediator and outcome
models:
>
> results2 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ interaction = FALSE
+ )
>
> results2
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.91812 0.27925 1.44197 2.55150 7.6751e-06
Indirect effect 0.99263 0.05626 0.88826 1.10927 0.89619
Total effect 1.90399 0.26714 1.44622 2.50664 4.4407e-06
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.27053 0.06748 1.14492 1.40991 6.5375e-06
Indirect effect 0.99782 0.01667 0.96568 1.03103 0.89593
Total effect 1.26776 0.06670 1.14354 1.40547 6.5043e-06
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.15019 0.03272 0.08605 0.21432 4.4402e-06
Indirect effect -0.00154 0.01178 -0.02463 0.02155 0.89604
Total effect 0.14865 0.03195 0.08602 0.21127 3.2871e-06
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 1.91814 0.27936 1.44182 2.55183 7.7378e-06
RR scale 1.26961 0.06625 1.14619 1.40633 4.7657e-06
RD scale 0.15000 0.03236 0.08657 0.21342 3.5640e-06
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 1.91814 0.27936 1.44182 2.55183 7.7378e-06
RR scale 1.27393 0.07778 1.13025 1.43587 7.3286e-05
RD scale 0.15087 0.03454 0.08318 0.21857 1.2534e-05
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6082 0.1404 -4.333 1.47e-05 ***
X 1.4678 0.1718 8.542 < 2e-16 ***
C1 -1.2652 0.1681 -7.527 5.19e-14 ***
C2 1.6482 0.1153 14.292 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 929.49 on 996 degrees of freedom
AIC: 937.49
Number of Fisher Scoring iterations: 5
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.40420 0.13526 -2.988 0.002805 **
X 0.65136 0.14564 4.472 7.74e-06 ***
M -0.02255 0.17259 -0.131 0.896063
C1 1.27063 0.14615 8.694 < 2e-16 ***
C2 -0.29998 0.08356 -3.590 0.000331 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1205.3 on 995 degrees of freedom
AIC: 1215.3
Number of Fisher Scoring iterations: 4The exactmed() function also allows for the
specification of two different sets of covariates in the mediator and
outcome models. For example, the following specification of
m_cov and y_cov means that the mediator model
is adjusted for C1 and C2, while the outcome
model is adjusted for C1 only.
However, we advise against this practice unless it is known that excluded covariates are independent of the dependent variable (mediator or outcome) being modeled given the rest of covariates.
>
> results3 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1'),
+ interaction = FALSE
+ )
>
> results3
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.07747 0.29467 1.57326 2.74328 2.5397e-07
Indirect effect 0.88888 0.04509 0.80476 0.98180 0.020229
Total effect 1.84663 0.25710 1.40563 2.42600 1.0554e-05
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.29629 0.06554 1.17400 1.43133 2.8560e-07
Indirect effect 0.96677 0.01378 0.94013 0.99416 0.017754
Total effect 1.25321 0.06518 1.13176 1.38771 1.4271e-05
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.16572 0.03132 0.10433 0.22710 1.2175e-07
Indirect effect -0.02409 0.01021 -0.04410 -0.00409 0.018258
Total effect 0.14162 0.03174 0.07941 0.20384 8.1377e-06
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.08515 0.29879 1.57458 2.76128 2.9258e-07
RR scale 1.28132 0.06150 1.16628 1.40771 2.4082e-07
RD scale 0.16264 0.03059 0.10269 0.22259 1.0544e-07
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.08515 0.29879 1.57458 2.76128 2.9258e-07
RR scale 1.36112 0.09014 1.19544 1.54976 3.2297e-06
RD scale 0.17702 0.03443 0.10954 0.24450 2.7259e-07
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6082 0.1404 -4.333 1.47e-05 ***
X 1.4678 0.1718 8.542 < 2e-16 ***
C1 -1.2652 0.1681 -7.527 5.19e-14 ***
C2 1.6482 0.1153 14.292 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 929.49 on 996 degrees of freedom
AIC: 937.49
Number of Fisher Scoring iterations: 5
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2629 0.1282 -2.050 0.0403 *
X 0.7348 0.1433 5.128 2.93e-07 ***
M -0.3543 0.1460 -2.426 0.0153 *
C1 1.1916 0.1428 8.345 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1218.5 on 996 degrees of freedom
AIC: 1226.5
Number of Fisher Scoring iterations: 4By default, the adjusted parameter is TRUE.
If the adjusted parameter is set to FALSE,
exactmed() ignores the values of the vectors
m_cov and y_cov and computes unadjusted
(crude) effects estimates as in the first example above:
>
> results4 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1'),
+ adjusted = FALSE, interaction = FALSE
+ )
'exactmed' will compute unadjusted natural effects
>
> results4
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.04899 0.27469 1.57554 2.66472 8.7484e-08
Indirect effect 0.88069 0.03097 0.82204 0.94352 0.00030218
Total effect 1.80452 0.23756 1.39414 2.33571 7.3256e-06
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.30183 0.06489 1.18065 1.43543 1.2130e-07
Indirect effect 0.96248 0.01014 0.94281 0.98257 0.00028454
Total effect 1.25298 0.06379 1.13399 1.38446 9.4328e-06
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.16514 0.03021 0.10593 0.22435 4.5850e-08
Indirect effect -0.02672 0.00732 -0.04107 -0.01238 0.00026137
Total effect 0.13842 0.03048 0.07868 0.19815 5.5775e-06
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.07495 0.28607 1.58363 2.71870 1.1937e-07
RR scale 1.26843 0.05652 1.16234 1.38420 9.5079e-08
RD scale 0.15878 0.02892 0.10210 0.21546 4.0057e-08
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.07495 0.28607 1.58363 2.71870 1.1937e-07
RR scale 1.40138 0.09725 1.22317 1.60555 1.1566e-06
RD scale 0.17947 0.03343 0.11395 0.24499 7.9365e-08
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.81241 0.09811 -8.281 < 2e-16 ***
X 0.90623 0.13212 6.859 6.92e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.3 on 999 degrees of freedom
Residual deviance: 1310.8 on 998 degrees of freedom
AIC: 1314.8
Number of Fisher Scoring iterations: 4
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3702 0.1015 3.648 0.000264 ***
X 0.7299 0.1379 5.294 1.19e-07 ***
M -0.5825 0.1388 -4.198 2.69e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1291.9 on 997 degrees of freedom
AIC: 1297.9
Number of Fisher Scoring iterations: 4To perform an adjusted mediation analysis allowing for
exposure-mediator interaction (by default, the interaction parameter is
TRUE) and using bootstrap based on \(100\) resamples with initial random seed
\(= 1991\) to construct \(97\%\) confidence intervals, one should
call exactmed() as follows:
>
> results5 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ boot = TRUE, nboot = 100, bootseed = 1991, confcoef = 0.97
+ )>
> results5
Natural effects on OR scale:
Estimate Std.error 1.5% 98.5%
Direct effect 2.24870 0.37516 1.68812 3.32099
Indirect effect 0.88856 0.07284 0.75874 1.07384
Total effect 1.99810 0.28480 1.51926 2.71363
Natural effects on RR scale:
Estimate Std.error 1.5% 98.5%
Direct effect 1.33700 0.07272 1.20829 1.48229
Indirect effect 0.96726 0.02151 0.92984 1.02289
Total effect 1.29323 0.06769 1.16111 1.44939
Natural effects on RD scale:
Estimate Std.error 1.5% 98.5%
Direct effect 0.18403 0.03365 0.12012 0.25767
Indirect effect -0.02390 0.01591 -0.05367 0.01544
Total effect 0.16013 0.03112 0.09568 0.22870
Controlled direct effect (m=0):
Estimate Std.error 1.5% 98.5%
OR scale 2.61843 0.55335 1.82091 4.25427
RR scale 1.41151 0.09319 1.25366 1.59634
RD scale 0.21741 0.04099 0.14197 0.30413
Controlled direct effect (m=1):
Estimate Std.error 1.5% 98.5%
OR scale 1.30748 0.30771 0.79211 2.15864
RR scale 1.10061 0.09454 0.92150 1.35743
RD scale 0.06150 0.05285 -0.05274 0.18411
First bootstrap replications of natural effects on OR scale:
Direct effect Indirect effect Total effect
[1,] 2.456120 0.9408412 2.310819
[2,] 2.133962 0.9077365 1.937075
[3,] 1.811932 1.0971231 1.987913
First bootstrap replications of natural effects on RR scale:
Direct effect Indirect effect Total effect
[1,] 1.427061 0.9818925 1.401220
[2,] 1.303377 0.9735270 1.268873
[3,] 1.262116 1.0294193 1.299246
First bootstrap replications of natural effects on RD scale:
Direct effect Indirect effect Total effect
[1,] 0.2114903 -0.01279682 0.1986934
[2,] 0.1704897 -0.01939046 0.1510993
[3,] 0.1406345 0.01992190 0.1605564
First bootstrap replications of controlled direct effect (m=0) on OR scale:
[1] 2.779720 2.412692 1.871147
First bootstrap replications of controlled direct effect (m=0) on RR scale:
[1] 1.503371 1.356003 1.291167
First bootstrap replications of controlled direct effect (m=0) on RD scale:
[1] 0.2401263 0.1963779 0.1501348
First bootstrap replications of controlled direct effect (m=1) on OR scale:
[1] 1.643801 1.491866 1.593185
First bootstrap replications of controlled direct effect (m=1) on RR scale:
[1] 1.210986 1.154799 1.163927
First bootstrap replications of controlled direct effect (m=1) on RD scale:
[1] 0.11712924 0.09186107 0.10191851
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6082 0.1404 -4.333 1.47e-05 ***
X 1.4678 0.1718 8.542 < 2e-16 ***
C1 -1.2652 0.1681 -7.527 5.19e-14 ***
C2 1.6482 0.1153 14.292 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 929.49 on 996 degrees of freedom
AIC: 937.49
Number of Fisher Scoring iterations: 5
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.5207 0.1443 -3.609 0.000308 ***
X 0.9626 0.1991 4.835 1.33e-06 ***
M 0.3393 0.2308 1.470 0.141501
X:M -0.6945 0.2929 -2.371 0.017746 *
C1 1.2771 0.1468 8.700 < 2e-16 ***
C2 -0.3091 0.0838 -3.689 0.000225 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1199.6 on 994 degrees of freedom
AIC: 1211.6
Number of Fisher Scoring iterations: 4In the situation where we believe that we are facing a problem of
separation or quasi-separation, Firth’s penalization can be used by
setting the Firth parameter to TRUE (Firth
penalized mediation analysis). If this is the case, Firth’s penalization
is applied to both the mediator model and the outcome model.
The Firth parameter implements Firth’s penalization to
reduce the bias of the regression coefficients estimators under scarce
or sparse data (see details in exactmed() help page):
>
> results6 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'), Firth = TRUE,
+ boot = TRUE, nboot = 100, bootseed = 1991, confcoef = 0.97
+ )>
> results6
Natural effects on OR scale:
Estimate Std.error 1.5% 98.5%
Direct effect 2.23246 0.36864 1.68036 3.28209
Indirect effect 0.88991 0.07197 0.76166 1.07288
Total effect 1.98668 0.28090 1.51385 2.69182
Natural effects on RR scale:
Estimate Std.error 1.5% 98.5%
Direct effect 1.33452 0.07217 1.20680 1.47867
Indirect effect 0.96751 0.02135 0.93035 1.02269
Total effect 1.29116 0.06719 1.16007 1.44614
Natural effects on RD scale:
Estimate Std.error 1.5% 98.5%
Direct effect 0.18263 0.03343 0.11919 0.25573
Indirect effect -0.02367 0.01576 -0.05316 0.01527
Total effect 0.15896 0.03093 0.09498 0.22710
Controlled direct effect (m=0):
Estimate Std.error 1.5% 98.5%
OR scale 2.59835 0.54383 1.81264 4.20506
RR scale 1.40883 0.09257 1.25203 1.59214
RD scale 0.21597 0.04077 0.14098 0.30221
Controlled direct effect (m=1):
Estimate Std.error 1.5% 98.5%
OR scale 1.30556 0.30467 0.79391 2.14590
RR scale 1.10034 0.09396 0.92202 1.35523
RD scale 0.06124 0.05249 -0.05229 0.18295
First bootstrap replications of natural effects on OR scale:
Direct effect Indirect effect Total effect
[1,] 2.440099 0.9413288 2.296936
[2,] 2.118584 0.9089399 1.925665
[3,] 1.804247 1.0958134 1.977118
First bootstrap replications of natural effects on RR scale:
Direct effect Indirect effect Total effect
[1,] 1.424154 0.9819734 1.398481
[2,] 1.301066 0.9737439 1.266905
[3,] 1.260400 1.0291349 1.297121
First bootstrap replications of natural effects on RD scale:
Direct effect Indirect effect Total effect
[1,] 0.2101089 -0.01271723 0.1973917
[2,] 0.1691184 -0.01918931 0.1499291
[3,] 0.1397075 0.01970156 0.1594091
First bootstrap replications of controlled direct effect (m=0) on OR scale:
[1] 2.761083 2.393397 1.862962
First bootstrap replications of controlled direct effect (m=0) on RR scale:
[1] 1.500221 1.353409 1.289262
First bootstrap replications of controlled direct effect (m=0) on RD scale:
[1] 0.2387231 0.1948956 0.1491569
First bootstrap replications of controlled direct effect (m=1) on OR scale:
[1] 1.638567 1.487659 1.589117
First bootstrap replications of controlled direct effect (m=1) on RR scale:
[1] 1.209995 1.154090 1.163587
First bootstrap replications of controlled direct effect (m=1) on RD scale:
[1] 0.11647760 0.09132799 0.10154966
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data,
method = brglmFit, type = "MPL_Jeffreys")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4703 -0.7139 -0.2851 0.7728 2.6103
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6042 0.1400 -4.314 1.60e-05 ***
X 1.4589 0.1714 8.514 < 2e-16 ***
C1 -1.2578 0.1677 -7.502 6.28e-14 ***
C2 1.6365 0.1147 14.263 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 929.51 on 996 degrees of freedom
AIC: 937.51
Type of estimator: MPL_Jeffreys (maximum penalized likelihood with Jeffreys'-prior penalty)
Number of Fisher Scoring iterations: 2
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data,
method = brglmFit, type = "MPL_Jeffreys")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2271 -1.0771 0.6198 0.9125 1.6972
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.51661 0.14418 -3.583 0.000339 ***
X 0.95488 0.19866 4.807 1.54e-06 ***
M 0.33581 0.23059 1.456 0.145307
X:M -0.68824 0.29254 -2.353 0.018639 *
C1 1.26829 0.14656 8.654 < 2e-16 ***
C2 -0.30649 0.08368 -3.663 0.000250 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1199.6 on 994 degrees of freedom
AIC: 1211.6
Type of estimator: MPL_Jeffreys (maximum penalized likelihood with Jeffreys'-prior penalty)
Number of Fisher Scoring iterations: 2The following call to exactmed() returns mediation
effects adjusted for the covariates C1 and C2,
when the values of the covariates C1 and C2
are \(0.1\) and \(0.4\), respectively, assuming an
exposure-mediator interaction and using the delta method to construct
\(95\%\) confidence intervals:
>
> results7 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = c(C1 = 0.1, C2 = 0.4), y_cov_cond = c(C1 = 0.1, C2 = 0.4)
+ )
>
> results7
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.86610 0.27725 1.39466 2.49690 2.6816e-05
Indirect effect 0.89347 0.06371 0.77694 1.02747 0.11413480
Total effect 1.66730 0.25143 1.24065 2.24066 0.00069917
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.37432 0.10503 1.18315 1.59639 3.1743e-05
Indirect effect 0.95099 0.03022 0.89357 1.01210 0.11377623
Total effect 1.30697 0.10409 1.11809 1.52777 0.00077525
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.15465 0.03625 0.08360 0.22571 1.9913e-05
Indirect effect -0.02783 0.01759 -0.06230 0.00664 0.11360218
Total effect 0.12683 0.03702 0.05427 0.19938 0.00061244
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.61843 0.52125 1.77253 3.86803 1.3290e-06
RR scale 1.63173 0.16066 1.34536 1.97906 6.5954e-07
RD scale 0.23603 0.04712 0.14369 0.32838 5.4583e-07
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 1.30748 0.28346 0.85486 1.99974 0.21622
RR scale 1.14677 0.12957 0.91897 1.43104 0.22549
RD scale 0.06689 0.05389 -0.03873 0.17251 0.21448
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6082 0.1404 -4.333 1.47e-05 ***
X 1.4678 0.1718 8.542 < 2e-16 ***
C1 -1.2652 0.1681 -7.527 5.19e-14 ***
C2 1.6482 0.1153 14.292 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 929.49 on 996 degrees of freedom
AIC: 937.49
Number of Fisher Scoring iterations: 5
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.5207 0.1443 -3.609 0.000308 ***
X 0.9626 0.1991 4.835 1.33e-06 ***
M 0.3393 0.2308 1.470 0.141501
X:M -0.6945 0.2929 -2.371 0.017746 *
C1 1.2771 0.1468 8.700 < 2e-16 ***
C2 -0.3091 0.0838 -3.689 0.000225 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1199.6 on 994 degrees of freedom
AIC: 1211.6
Number of Fisher Scoring iterations: 4Common adjustment covariates in vectors m_cov and
y_cov must have the same values; otherwise, the execution
of the exactmed() function is aborted and an error message
is displayed in the R console. Example:
> exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = c(C1 = 0.3, C2 = 0.4), y_cov_cond = c(C1 = 0.1, C2 = 0.4)
+ ) Error in .check_input_param(data = data, a = a, m = m, y = y, a1 = a1, : Covariate C1 has two different values specifiedIf the covariates specified in m_cov_cond
(y_cov_cond) constitute some proper subset of
m_cov (y_cov) then the other covariates are
set to their sample-specific mean levels. Hence, the call
>
> results8 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = c(C1 = 0.1), y_cov_cond = c(C1 = 0.1)
+ )is equivalent to:
>
> mc2 <- mean(datamed$C2)
> mc2
[1] -0.04769712
>
> results9 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = c(C1 = 0.1, C2 = mc2), y_cov_cond = c(C1 = 0.1, C2 = mc2)
+ )This can be checked by comparing the two outputs:
>
> all.equal(results8, results9)
[1] TRUEWith this in mind, an error is easily predicted if one makes this call:
> exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = c(C1 = 0.1), y_cov_cond = c(C1 = 0.1, C2 = 0.4)
+ ) Error in .check_input_param(data = data, a = a, m = m, y = y, a1 = a1, : Covariate C2 has two different values specified (one implicitly)The exactmed() function also allows for categorical
covariates. Covariates of this type must appear in the data frame as
factor, character, or logical columns. To illustrate how
exactmed() works with categorical covariates, we replace
the covariate C1 in the data set datamed by a
random factor column:
>
> cate <- factor(sample(c("a", "b", "c"), nrow(datamed), replace =TRUE))
> datamed$C1 <- cateIt is possible to estimate mediation effects at specific values of
categorical covariates using the input parameters
m_cov_cond and y_cov_cond. Note that if the
targeted covariates are a mixture of numerical and categorical
covariates, the above parameters require to be list-type vectors,
instead of atomic vectors as when covariates are only numerical or only
categorical.
Hence, if one wants to estimate mediation effects at level ‘a’ for
C1 and at value \(0.4\)
for C2, assuming an exposure-mediator interaction and using
the delta method to construct \(95\%\)
confidence intervals, exactmed() should be called as
follows:
>
> results10 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = list(C1 = 'a', C2 = 0.4), y_cov_cond = list(C1 = 'a', C2 = 0.4)
+ )
>
> results10
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.93666 0.26913 1.47490 2.54298 1.9726e-06
Indirect effect 0.80707 0.05473 0.70663 0.92179 0.0015722
Total effect 1.56302 0.22095 1.18478 2.06201 0.0015806
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.28784 0.07186 1.15443 1.43667 5.7977e-06
Indirect effect 0.93157 0.02153 0.89031 0.97474 0.0021622
Total effect 1.19971 0.07055 1.06911 1.34627 0.0019593
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.15482 0.03215 0.09180 0.21784 1.4724e-06
Indirect effect -0.04740 0.01505 -0.07691 -0.01790 0.0016367
Total effect 0.10742 0.03377 0.04123 0.17361 0.0014684
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.61643 0.50061 1.79824 3.80690 4.9841e-07
RR scale 1.39356 0.09416 1.22072 1.59089 9.0296e-07
RD scale 0.21365 0.04027 0.13473 0.29258 1.1236e-07
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 1.37503 0.28624 0.91436 2.06781 0.12605
RR scale 1.14656 0.10575 0.95694 1.37375 0.13812
RD scale 0.07787 0.05103 -0.02214 0.17789 0.12699
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.8950 0.1615 -5.540 3.02e-08 ***
X 1.3943 0.1656 8.419 < 2e-16 ***
C1b -0.5073 0.1934 -2.623 0.00871 **
C1c -0.2790 0.1906 -1.463 0.14334
C2 1.5664 0.1109 14.128 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 983.59 on 995 degrees of freedom
AIC: 993.59
Number of Fisher Scoring iterations: 5
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.25324 0.15193 1.667 0.0956 .
X 0.96181 0.19133 5.027 4.98e-07 ***
M -0.04639 0.21774 -0.213 0.8313
X:M -0.64333 0.28142 -2.286 0.0223 *
C1b 0.00162 0.16759 0.010 0.9923
C1c -0.13997 0.16069 -0.871 0.3837
C2 -0.20336 0.07922 -2.567 0.0103 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1279.5 on 993 degrees of freedom
AIC: 1293.5
Number of Fisher Scoring iterations: 4If one does not specify a value for the categorical covariate
C1, exactmed() computes the effects by
assigning each dummy variable, created internally by
exactmed() for each non-reference level of C1,
to a value equal to the proportion of observations in the corresponding
category (equivalent to setting each dummy variable to its mean
value):
>
> results11 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ m_cov_cond = c(C2 = 0.4), y_cov_cond = c(C2 = 0.4)
+ )
>
> results11
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.01817 0.28018 1.53741 2.64928 4.2356e-07
Indirect effect 0.79861 0.05731 0.69383 0.91921 0.00172510
Total effect 1.61173 0.22105 1.23183 2.10879 0.00050099
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.31391 0.07090 1.18203 1.46049 4.2158e-07
Indirect effect 0.92786 0.02211 0.88552 0.97223 0.0016799
Total effect 1.21912 0.06962 1.09003 1.36350 0.0005212
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.16525 0.03184 0.10286 0.22765 2.0946e-07
Indirect effect -0.04990 0.01579 -0.08084 -0.01896 0.00157225
Total effect 0.11536 0.03280 0.05107 0.17964 0.00043667
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.61643 0.50061 1.79824 3.80690 4.9841e-07
RR scale 1.40827 0.09257 1.23803 1.60191 1.9076e-07
RD scale 0.21668 0.04026 0.13777 0.29559 7.3675e-08
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 1.37503 0.28624 0.91436 2.06781 0.12605
RR scale 1.15094 0.10884 0.95622 1.38531 0.13713
RD scale 0.07836 0.05129 -0.02217 0.17889 0.12656
Mediator model:
Call:
glm(formula = Mform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.8950 0.1615 -5.540 3.02e-08 ***
X 1.3943 0.1656 8.419 < 2e-16 ***
C1b -0.5073 0.1934 -2.623 0.00871 **
C1c -0.2790 0.1906 -1.463 0.14334
C2 1.5664 0.1109 14.128 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1359.28 on 999 degrees of freedom
Residual deviance: 983.59 on 995 degrees of freedom
AIC: 993.59
Number of Fisher Scoring iterations: 5
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.25324 0.15193 1.667 0.0956 .
X 0.96181 0.19133 5.027 4.98e-07 ***
M -0.04639 0.21774 -0.213 0.8313
X:M -0.64333 0.28142 -2.286 0.0223 *
C1b 0.00162 0.16759 0.010 0.9923
C1c -0.13997 0.16069 -0.871 0.3837
C2 -0.20336 0.07922 -2.567 0.0103 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1330.1 on 999 degrees of freedom
Residual deviance: 1279.5 on 993 degrees of freedom
AIC: 1293.5
Number of Fisher Scoring iterations: 4exactmed() can also compute mediation effects with a
binary outcome and a binary mediator when the data come from a classical
case-control study wherein the probability of being selected only
depends on the outcome status. To do so, the true outcome prevalence
(that is, the population prevalence \(P(Y =
hvalue\_y))\) must be known and the yprevalence
parameter set to this value. exactmed() accounts for the
ascertainment in the sample by employing weighted regression techniques
that use inverse-probability weighting (IPW) with robust standard errors
(see details in the documentation).
The following call to exactmed() returns mediation
effects supposing that the data have been obtained from a case-control
study and that the true outcome prevalence is \(0.1\):
>
> results12 <- exactmed(
+ data = datamed, a = 'X', m = 'M', y = 'Y',
+ a1 = 1, a0 = 0, interaction = FALSE, yprevalence = 0.1
+ )
'exactmed' will compute unadjusted natural effects
>
> results12
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.19582 0.31893 1.65183 2.91896 6.1132e-08
Indirect effect 0.82180 0.04229 0.74295 0.90901 0.00013694
Total effect 1.80452 0.24723 1.37956 2.36039 1.6439e-05
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.01152 0.25650 1.56670 2.58265 4.2319e-08
Indirect effect 0.84501 0.03675 0.77597 0.92019 0.00010767
Total effect 1.69975 0.20855 1.33643 2.16184 1.5354e-05
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.07750 0.01607 0.04601 0.10900 1.4134e-06
Indirect effect -0.02389 0.00700 -0.03760 -0.01018 0.00063914
Total effect 0.05361 0.01309 0.02796 0.07927 4.2084e-05
Controlled direct effect (m=0):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.20913 0.32572 1.65469 2.94934 7.6324e-08
RR scale 1.99136 0.24919 1.55824 2.54488 3.7019e-08
RD scale 0.08966 0.01974 0.05097 0.12835 5.5656e-06
Controlled direct effect (m=1):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.20913 0.32572 1.65469 2.94934 7.6324e-08
RR scale 2.08516 0.28792 1.59077 2.73322 1.0270e-07
RD scale 0.05335 0.00992 0.03390 0.07281 7.5963e-08
Mediator model:
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.68616 0.13294 -5.1614 2.451e-07 ***
X 1.27375 0.19468 6.5428 6.037e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Outcome model:
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.308276 0.099612 -23.1727 < 2.2e-16 ***
X 0.792597 0.147443 5.3756 7.632e-08 ***
M -0.653826 0.149353 -4.3777 1.199e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Of note, the same optional parameters described in the previous sections are available in the case-control study context.
As mentioned in the introduction, in the case of a continuous
mediator, the ExactMed package allows the user to
obtain estimates of the different mediation effects using the
exactmed_c() function, which essentially offers the same
options as exactmed(). The only difference is the absence
of the hvalue_m parameter and the addition of the
mf parameter, the latter allowing to set the value of the
mediator in the calculation of the controlled direct effect (by default
fixed at the sample-specific mean of the mediator).
For illustration, the package also makes available to the user the
datamed_c data set containing a continuous mediator
variable. Some of the features of this data set can be found in its
corresponding help file (help(datamed_c)). We recall that
the exactmed_c() function only works on data frames with
named columns and no missing values.
>
> library(ExactMed)
>
> head(datamed_c)
X M Y C1 C2
1 1 -0.6559769 1 0 0.3753731
2 1 0.8445136 1 1 0.1971635
3 1 -1.9282753 1 1 -0.5971041
4 0 0.9826885 0 0 0.7576990
5 0 -0.5505834 0 0 1.2056864
6 0 0.3611274 1 0 -0.5983204We provide below an example of call to exactmed_c() that
allows to obtain estimates of conditional mediation effects supposing no
exposure-mediator interaction in the outcome regression model:
>
> results13 <- exactmed_c(
+ data = datamed_c, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ interaction = FALSE
+ )
>
> results13
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.39946 0.34660 1.80783 3.18470 1.368e-09
Indirect effect 1.32343 0.07327 1.18734 1.47512 4.159e-07
Total effect 3.17552 0.43497 2.42784 4.15345 < 2.22e-16
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.52926 0.10643 1.33427 1.75275 1.0366e-09
Indirect effect 1.10184 0.02380 1.05617 1.14948 7.1014e-06
Total effect 1.68500 0.10868 1.48491 1.91205 6.6613e-16
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.21520 0.03435 0.14788 0.28252 3.7132e-10
Indirect effect 0.06332 0.01308 0.03768 0.08896 1.2951e-06
Total effect 0.27853 0.03130 0.21718 0.33987 < 2.22e-16
Controlled direct effect (m=-0.5107948):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.46943 0.35947 1.85647 3.28476 5.2965e-10
RR scale 1.50025 0.10062 1.31545 1.71100 1.4658e-09
RD scale 0.21993 0.03428 0.15273 0.28712 1.4081e-10
Mediator model:
Call:
lm(formula = Mform, data = data)
Residuals:
Min 1Q Median 3Q Max
-6.1821 -1.4324 0.0568 1.2604 7.1676
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.62373 0.10799 -5.776 1.02e-08 ***
X 1.53357 0.12476 12.292 < 2e-16 ***
C1 -1.22692 0.12473 -9.837 < 2e-16 ***
C2 1.61857 0.06236 25.956 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.971 on 996 degrees of freedom
Multiple R-squared: 0.4774, Adjusted R-squared: 0.4758
F-statistic: 303.3 on 3 and 996 DF, p-value: < 2.2e-16
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.77921 0.12238 -6.367 1.93e-10 ***
X 0.90399 0.14557 6.210 5.30e-10 ***
M 0.18828 0.03601 5.228 1.71e-07 ***
C1 1.26049 0.14987 8.410 < 2e-16 ***
C2 -0.44883 0.09123 -4.920 8.67e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1377.4 on 999 degrees of freedom
Residual deviance: 1218.1 on 995 degrees of freedom
AIC: 1228.1
Number of Fisher Scoring iterations: 4To perform an adjusted mediation analysis allowing for
exposure-mediator interaction, using bootstrap based on \(100\) resamples with initial random seed
\(= 1885\) to construct \(95\%\) confidence intervals and computing
the controlled direct effect when the mediator is set at the value \(2\), one should call
exactmed_c() as follows:
>
> results14 <- exactmed_c(
+ data = datamed_c, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ boot = TRUE, nboot = 100, bootseed = 1885, confcoef = 0.95,
+ mf = 2
+ )>
> results14
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5%
Direct effect 4.31959 0.77257 3.06977 6.12301
Indirect effect 0.82693 0.06038 0.72646 0.95294
Total effect 3.57197 0.54375 2.82364 4.80002
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5%
Direct effect 1.84144 0.12202 1.62966 2.11563
Indirect effect 0.94962 0.01716 0.92646 0.98644
Total effect 1.74867 0.11280 1.56681 2.01742
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5%
Direct effect 0.34112 0.03489 0.27026 0.41113
Indirect effect -0.03761 0.01341 -0.05775 -0.00970
Total effect 0.30351 0.03150 0.24864 0.36907
Controlled direct effect (m=2):
Estimate Std.error 2.5% 97.5%
OR scale 0.60828 0.12292 0.43865 0.81843
RR scale 0.86843 0.04549 0.78712 0.94488
RD scale -0.10062 0.03721 -0.16694 -0.04072
First bootstrap replications of natural effects on OR scale:
Direct effect Indirect effect Total effect
[1,] 4.317548 0.8193258 3.537479
[2,] 2.967199 0.8876172 2.633736
[3,] 4.949753 0.7552281 3.738193
First bootstrap replications of natural effects on RR scale:
Direct effect Indirect effect Total effect
[1,] 1.793859 0.9498774 1.703946
[2,] 1.616215 0.9618525 1.554560
[3,] 1.963302 0.9267452 1.819480
First bootstrap replications of natural effects on RD scale:
Direct effect Indirect effect Total effect
[1,] 0.3366462 -0.03812869 0.2985175
[2,] 0.2618394 -0.02619802 0.2356414
[3,] 0.3709888 -0.05538875 0.3156000
First bootstrap replications of controlled direct effect on OR scale:
[1] 0.4826180 0.4767809 0.6314553
First bootstrap replications of controlled direct effect on RR scale:
[1] 0.8329215 0.8063200 0.8673280
First bootstrap replications of controlled direct effect on RD scale:
[1] -0.13581560 -0.15128670 -0.09790019
Mediator model:
Call:
lm(formula = Mform, data = data)
Residuals:
Min 1Q Median 3Q Max
-6.1821 -1.4324 0.0568 1.2604 7.1676
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.62373 0.10799 -5.776 1.02e-08 ***
X 1.53357 0.12476 12.292 < 2e-16 ***
C1 -1.22692 0.12473 -9.837 < 2e-16 ***
C2 1.61857 0.06236 25.956 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.971 on 996 degrees of freedom
Multiple R-squared: 0.4774, Adjusted R-squared: 0.4758
F-statistic: 303.3 on 3 and 996 DF, p-value: < 2.2e-16
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.51704 0.13059 -3.959 7.52e-05 ***
X 0.74814 0.15871 4.714 2.43e-06 ***
M 0.49722 0.05253 9.465 < 2e-16 ***
X:M -0.62263 0.06358 -9.793 < 2e-16 ***
C1 1.39342 0.16191 8.606 < 2e-16 ***
C2 -0.53693 0.09816 -5.470 4.50e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1377.4 on 999 degrees of freedom
Residual deviance: 1102.9 on 994 degrees of freedom
AIC: 1114.9
Number of Fisher Scoring iterations: 4As mentioned in the introduction, in the case of a categorical
mediator (coded as factor), the ExactMed package allows
the user to obtain estimates of mediation effects through the
exactmed_cat() function, which basically offers the same
options as exactmed(). The only difference is the absence
of the hvalue_m parameter and the addition of two extra
parameters: blevel_m and mf. The first one
allows to set the reference level of the mediator, which by default
corresponds to the first level of the corresponding factor variable. The
second parameter allows to specify the level of the mediator in the
calculation of the controlled direct effect. Parameter
blevel_m will thus impact the mediator regression model and
associated output by fixing the reference level of the dependent
variable. Parameter blevel_m will not impact the values of
the natural effects and will impact the controlled direct effect only if
the value of the parameter mf is not specified by the user.
In this case, the value of the parameter mf will by default
correspond to the value of parameter blevel_m.
For illustration, the package also makes available to the user the
datamed_cat data set containing a categorical mediator
variable. Some of the features of this data set can be found in its
corresponding help file (help(datamed_cat)). We recall that
the exactmed_cat() function only works on data frames with
named columns and no missing values.
>
> head(datamed_cat)
X M Y C1 C2
1 1 c 1 0 0.3753731
2 1 d 1 1 0.1971635
3 1 b 1 1 -0.5971041
4 0 d 0 0 0.7576990
5 0 c 0 0 1.2056864
6 0 d 1 0 -0.5983204We provide below an example of call to exactmed_cat() to
obtain estimates of conditional mediation effects supposing no
exposure-mediator interaction in the outcome regression model:
>
> results15 <- exactmed_cat(
+ data = datamed_cat, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ interaction = FALSE
+ )
>
> results15
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 2.37922 0.33703 1.80242 3.14061 9.4267e-10
Indirect effect 1.39828 0.09855 1.21787 1.60540 1.9687e-06
Total effect 3.32681 0.45882 2.53882 4.35936 < 2.22e-16
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 1.51375 0.10234 1.32589 1.72822 8.6512e-10
Indirect effect 1.11869 0.02906 1.06315 1.17713 1.5812e-05
Total effect 1.69342 0.10787 1.49465 1.91862 2.2204e-16
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5% P.val
Direct effect 0.21297 0.03366 0.14700 0.27894 2.4890e-10
Indirect effect 0.07448 0.01621 0.04270 0.10626 4.3589e-06
Total effect 0.28745 0.03118 0.22634 0.34856 < 2.22e-16
Controlled direct effect (m='a'):
Estimate Std.error 2.5% 97.5% P.val
OR scale 2.52115 0.36610 1.89669 3.35121 1.9134e-10
RR scale 1.86361 0.17886 1.54405 2.24932 8.8040e-11
RD scale 0.20031 0.03548 0.13078 0.26985 1.6391e-08
Mediator model:
Call:
mlogit(formula = M ~ 1 | X + C1 + C2, data = data_long, reflevel = ref_level,
method = "nr")
Frequencies of alternatives:choice
a b c d e
0.2 0.2 0.2 0.2 0.2
nr method
6 iterations, 0h:0m:0s
g'(-H)^-1g = 2.06E-05
successive function values within tolerance limits
Coefficients :
Estimate Std. Error z-value Pr(>|z|)
(Intercept):b 0.539165 0.202979 2.6563 0.007901 **
(Intercept):c 0.622236 0.209098 2.9758 0.002922 **
(Intercept):d 0.435369 0.217929 1.9978 0.045743 *
(Intercept):e -0.089258 0.244928 -0.3644 0.715541
X:b 1.024819 0.228423 4.4865 7.241e-06 ***
X:c 1.688852 0.247460 6.8248 8.808e-12 ***
X:d 2.222046 0.259408 8.5658 < 2.2e-16 ***
X:e 2.750817 0.286159 9.6129 < 2.2e-16 ***
C1:b -0.654903 0.220269 -2.9732 0.002947 **
C1:c -1.167718 0.237979 -4.9068 9.257e-07 ***
C1:d -1.537355 0.248169 -6.1948 5.836e-10 ***
C1:e -2.271721 0.276774 -8.2079 2.220e-16 ***
C2:b 0.828863 0.135509 6.1167 9.555e-10 ***
C2:c 1.662424 0.156101 10.6497 < 2.2e-16 ***
C2:d 1.997098 0.164942 12.1079 < 2.2e-16 ***
C2:e 2.934705 0.188921 15.5341 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log-Likelihood: -1325.6
McFadden R^2: 0.17635
Likelihood ratio test : chisq = 567.66 (p.value = < 2.22e-16)
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.82245 0.21526 -8.466 < 2e-16 ***
X 0.92471 0.14521 6.368 1.91e-10 ***
Mb 0.75708 0.22720 3.332 0.000861 ***
Mc 1.32271 0.24801 5.333 9.64e-08 ***
Md 1.19699 0.25554 4.684 2.81e-06 ***
Me 1.44786 0.28800 5.027 4.97e-07 ***
C1 1.24728 0.14993 8.319 < 2e-16 ***
C2 -0.42296 0.08824 -4.793 1.64e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1377.4 on 999 degrees of freedom
Residual deviance: 1211.0 on 992 degrees of freedom
AIC: 1227
Number of Fisher Scoring iterations: 4To perform an adjusted mediation analysis allowing for
exposure-mediator interaction, using bootstrap based on \(100\) resamples with initial random seed
\(= 1875\) to construct \(95\%\) confidence intervals and computing
the controlled direct effect at the level ‘c’ of the mediator, one
should call exactmed_cat() as follows:
>
> results16 <- exactmed_cat(
+ data = datamed_cat, a = 'X', m = 'M', y = 'Y', a1 = 1, a0 = 0,
+ m_cov = c('C1', 'C2'), y_cov = c('C1', 'C2'),
+ boot = TRUE, nboot = 100, bootseed = 1875, confcoef = 0.95,
+ mf = 'c'
+ )>
> results16
Natural effects on OR scale:
Estimate Std.error 2.5% 97.5%
Direct effect 4.41078 0.80943 3.36925 6.33146
Indirect effect 0.75882 0.07040 0.60896 0.87894
Total effect 3.34699 0.47761 2.62808 4.35731
Natural effects on RR scale:
Estimate Std.error 2.5% 97.5%
Direct effect 1.85089 0.13401 1.63145 2.11986
Indirect effect 0.92653 0.02200 0.88034 0.96343
Total effect 1.71491 0.11612 1.51320 1.94403
Natural effects on RD scale:
Estimate Std.error 2.5% 97.5%
Direct effect 0.34503 0.03611 0.28452 0.41486
Indirect effect -0.05514 0.01765 -0.09385 -0.02648
Total effect 0.28989 0.03127 0.23214 0.34706
Controlled direct effect (m='c'):
Estimate Std.error 2.5% 97.5%
OR scale 2.23556 1.08567 1.36293 4.23663
RR scale 1.33131 0.18071 1.10223 1.62581
RD scale 0.18213 0.06992 0.06808 0.30585
First bootstrap replications of natural effects on OR scale:
Direct effect Indirect effect Total effect
[1,] 3.766006 0.8060753 3.035684
[2,] 4.613071 0.6838449 3.154625
[3,] 3.624178 0.8161027 2.957701
First bootstrap replications of natural effects on RR scale:
Direct effect Indirect effect Total effect
[1,] 1.745950 0.9390725 1.639573
[2,] 1.857263 0.9011499 1.673672
[3,] 1.679460 0.9448718 1.586874
First bootstrap replications of natural effects on RD scale:
Direct effect Indirect effect Total effect
[1,] 0.3120241 -0.04449625 0.2675279
[2,] 0.3520571 -0.07539621 0.2766609
[3,] 0.2998179 -0.04085426 0.2589637
First bootstrap replications of controlled direct effect on OR scale:
[1] 1.770563 1.379440 1.611341
First bootstrap replications of controlled direct effect on RR scale:
[1] 1.229423 1.122744 1.184028
First bootstrap replications of controlled direct effect on RD scale:
[1] 0.13105003 0.07395983 0.10863869
Mediator model:
Call:
multinom(formula = Mform, data = data, trace = FALSE)
Coefficients:
(Intercept) X C1 C2
b 0.53916078 1.024813 -0.6549007 0.8288642
c 0.62222777 1.688853 -1.1677146 1.6624211
d 0.43535996 2.222050 -1.5373555 1.9970966
e -0.08927925 2.750826 -2.2717164 2.9347141
Std. Errors:
(Intercept) X C1 C2
b 0.2029782 0.2284233 0.2202692 0.1355087
c 0.2090972 0.2474595 0.2379791 0.1561005
d 0.2179288 0.2594080 0.2481688 0.1649421
e 0.2449284 0.2861596 0.2767736 0.1889210
Residual Deviance: 2651.22
AIC: 2683.22
Outcome model:
Call:
glm(formula = Yform, family = binomial(link = "logit"), data = data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.67124 0.27264 -9.798 < 2e-16 ***
X 3.81300 0.52911 7.206 5.75e-13 ***
Mb 1.06895 0.31187 3.428 0.000609 ***
Mc 2.19709 0.33371 6.584 4.59e-11 ***
Md 2.78578 0.36477 7.637 2.22e-14 ***
Me 3.37526 0.40984 8.236 < 2e-16 ***
X:Mb -1.69550 0.63866 -2.655 0.007936 **
X:Mc -3.00850 0.61553 -4.888 1.02e-06 ***
X:Md -4.11908 0.61667 -6.680 2.40e-11 ***
X:Me -4.49990 0.62393 -7.212 5.50e-13 ***
C1 1.34154 0.16052 8.358 < 2e-16 ***
C2 -0.48389 0.09375 -5.162 2.45e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1377.4 on 999 degrees of freedom
Residual deviance: 1112.0 on 988 degrees of freedom
AIC: 1136
Number of Fisher Scoring iterations: 5One can note from the previous output that the reference level for
the mediator model is by default the first level of the mediator factor
variable (blevel_m = 'a'). However, the controlled direct
effect is computed at the level ‘c’ of the categorical mediator, as
requested by the parameter mf (that is,
mf = 'c').