R: Function to calculate effects

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 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 )

### Arguments

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

### Details

The function is a wrapper for glm. Effects are calculated as differences in means for a metric response, odds ratios/relatiev 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.

### Value

`comp1` | Effects of exposure
`comp2` | Tests of significance

### Author(s)

Michael Hills

### References

www.mhills.pwp.blueyonder.co.uk

### 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)

[Package Epi version 2.37 Index]