effx {Epi}R Documentation

Function to calculate effects

Description

The function calculates the effects of an exposure on a response, possibly stratified by a stratifying variable, and/or controlled for one of more confounding variables.

Usage

effx( response, type = "metric",
                 fup = NULL,     
            exposure,     
              strata = NULL,  
             control = NULL,
             weights = NULL,
               alpha = 0.05,   
                base = 1,             
              digits = 3,     
                data = NULL )    

Arguments

response The response variable - must be numeric
type The type of responsetype - must be one of "metric", "binary", "failure", or "count"
fup The fup variable contains the follow-up time for a failure response
exposure The exposure variable can be numeric or a factor
strata The strata stratifying variable - must be a factor
control The control variable(s) - these are passed as a list of there are more than one.
weights Weights
base Baseline for the effects of a categorical exposure, default 1
digits Number of significant digits for the effects, default 3
alpha 1 - confidence level
data data refers to the data used to evaluate the function

Details

The function is a wrapper for glm. Effects are calculated as differences in means for a metric response, odds ratios for a binary response, and rate ratios for a failure or count response.

The k-1 effects for a categorical exposure with k levels are relative to a baseline which, by default, is the first level. The effect of a metric (quantitative) exposure is calculated per unit of exposure.

The exposure variable can be numeric or a factor, but if it is an ordered factor the order will be ignored.

Value

comp1 Effects of exposure
comp2 Tests of significance

Warning

The function attaches the frame specified in the data= argument, so there is a possibility that a variable in the Global environment is masked by the attached frame. Watch out for this warning in the output!

Author(s)

Michael Hills

References

www.mhills.pwp.blueyonder.co.uk

Examples

data(births)
births$hyp <- factor(births$hyp,labels=c("normal","hyper"))
births$sex <- factor(births$sex,labels=c("M","F"))

# bweight is the birth weight of the baby in gms, and is a metric
# response (the default) 

# effect of hypertension on birth weight
effx(bweight,exposure=hyp,data=births) 
# effect of hypertension on birth weight stratified by sex
effx(bweight,exposure=hyp,strata=sex,data=births) 
# effect of hypertension on birth weight controlled for sex
effx(bweight,exposure=hyp,control=sex,data=births) 
# effect of gestation time on birth weight
effx(bweight,exposure=gestwks,data=births) 
# effect of gestation time on birth weight stratified by sex
effx(bweight,exposure=gestwks,strata=sex,data=births) 
# effect of gestation time on birth weight controlled for sex
effx(bweight,exposure=gestwks,control=sex,data=births) 

# lowbw is a binary response coded 1 for low birth weight and 0 otherwise
# effect of hypertension on low birth weight
effx(lowbw,type="binary",exposure=hyp,data=births)
# etc.

[Package Epi version 0.9.0 Index]