gm.si {gmvalid} | R Documentation |
Computes the synergy index of two discrete variables in relation to a binary group variable.
gm.si(X,Y,group,data=0,reference=c(1,1,2),conf.level = 0.95)
X |
Index of the variable's position in data or a vector. |
Y |
Index of the variable's position in data or a vector. |
group |
Binary group or outcome variable addressed as variable index in
data or as vector. |
data |
Data frame or table. |
reference |
Vector to define the reference categories of X , Y and group .
By default, 2 is the reference category for group. |
conf.level |
Confidence level of the interval (default is 0.95). |
Rothman's synergy index (S) is an interaction measure between two discrete variables on a dichotomous outcome. The index equals 1 under additivity, S > 1 in the case of synergy and S < 1 in the presence of antagonism.
The synergy index is originally constructed on (2 x 2)-tables, but (i x j)-tables can also be analyzed. Then (i-1) x (j-1) synergy indices are computed and combined to an overall synergy index.
The confidence intervalls are calculated using the asymptotic variance given in Rothman (1974).
A list containing:
ratio |
A rate ratio table, more precise [P( group=reference,X,Y ) / P( group=NOT reference,X,Y )] / [min(P( group=reference,X,Y ) / P( group=NOT reference,X,Y ))] |
covariance |
Covariances matrix of the single synergy indices.
Not written if X or Y are binary. |
measure |
Matrix containing the estimate(s), standard deviation(s),
confidence interval(s) and p-value(s). Figures in brackets show the
reference category respectively the category under consideration. If both factors X and Y are binary, confidence
intervals for case-control as well as cohort designs are computed.
If at least one factor has more than 2 categories, the overall synergy index
with its corresponding confidence interval is computed that follows a
case-control design. |
It can occur that certain combinations of categories lead to a negative synergy indices.
In that case no confidence intervals can be computed. If so, use the reference
option
to re-order the categories of the variable(s) in question (see example below).
Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de
Rothman K (1974) The estimation of synergy or antagonism. American Journal of Epidemiology, 103(5):506-511
Rothman K (1986) Modern Epidemiology. Little, Brown and Company, Boston/Toronto.
data(wynder) gm.si(1,2,3,wynder) # Smoking and alcohol in relation to oral cancer among male veterans under age 60. # (from "Modern Epidemiology") oral <- array(c(20,3,18,8,12,6,166,225),dim=c(2,2,2), dimnames=list(Group=c("control","cases"), Smoker=c("no","yes"),Alcohol=c("no","yes"))) oral.df <- expand.table(oral) # grouping variable is first in data frame gm.si(2,3,1,oral.df) # Effects must be ascending in respect to the reference category show.effect <- array(c(1,7,2,7,7,12,106,48),dim=c(2,2,2), dimnames=list(A=1:2,B=1:2,C=1:2)) # produces NaN gm.si(1,2,3,expand.table(show.effect)) # > re-ordering variable B helps gm.si(1,2,3,expand.table(show.effect),reference=c(1,2,2))