Epi (version 2.56)

effx: 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 or more confounding variables.

Usage

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

Value

comp1

Effects of exposure

comp2

Tests of significance

Arguments

response

The response variable - must be numeric or logical. If logical, TRUE is considered the outcome.

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. This must be numeric.

exposure

The exposure variable can be numeric or a factor

strata

The strata stratifying variable - must be a factor

control

The control variable(s) (confounders) - these are passed as a list if there are more than one.

weights

Frequency weights for binary response only

eff

How should effects be measured. If response is binomial, the default is "OR" (odds-ratio) with "RR" (relative risk) as an option. If response is failure, the default is "RR" (rate-ratio) with "RD" (rate difference) as an option.

base

Baseline for the effects of a categorical exposure, either a number or a name of the level. Defaults to 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

Author

Michael Hills (*1934-Jun-07, +2021-Jan-07)

Details

The function is a wrapper for glm. Effects are calculated as differences in means for a metric response, odds ratios/relative risks for a binary response, and rate ratios/rate differences 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.

Examples

library(Epi)
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)

print( options('na.action') )
# 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)
effx(lowbw,type="binary",exposure=hyp,eff="RR",data=births)