effx.match {Epi}

Function to calculate effects for individually matched case-control studies

Description

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

Usage

effx.match(response,
exposure,
match,
strata=NULL,
control=NULL,
base=1,
digits=3,
alpha=0.05,
data=NULL) 

Arguments

response

The response variable - must be numeric

exposure

The exposure variable can be numeric or a factor

match

The variable which identifies the matched sets

strata

The strata stratifying variable - must be a factor

control

The control variable(s). These are passed as a list if there are more than one of them.

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

Effects are calculated odds ratios. The function is a wrapper for clogit, from the survival package. 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

Author(s)

Michael Hills

References

www.mhills.pwp.blueyonder.co.uk

Examples

library(Epi)
library(survival)
data(bdendo)

# d is the case-control variable, set is the matching variable.
# The variable est is a factor and refers to estrogen use (no,yes)
# The variable hyp is a factor with 2 levels and refers to hypertension (no, yes)
# effect of est on the odds of being a case
effx.match(d,exposure=est,match=set,data=bdendo)
# effect of est on the odds of being a case, stratified by hyp
effx.match(d,exposure=est,match=set,strata=hyp,data=bdendo)
# effect of est on the odds of being a case, controlled for hyp
effx.match(d,exposure=est,match=set,control=hyp,data=bdendo)