rrcalc: Estimation of relative risk
In HEAT: Health Effects of Air Pollution and Temperature (HEAT)

Description Usage Arguments Details Value Author(s) Examples

View source: R/rrcalc.R

rrcalc caculates relative risks below and above a threshold. Relative risks and the 95% C.I.s of lower unit and upper unit based on the threshold are estimated.

1	rrcalc(object, rrunit = 1)

`object`	a fitted `threshpt` object produced by `threshpt()`.
`rrunit`	Unit of relative risk.

In GLM with log link, the coefficients of the exposure are equal to log values of RR. rrcalc gives relative risks in log link GLM, particularly, Poisson regression model.

The results of "<Threshold" mean that the relative risk and 95% confidence interval when the exposure increases by rrunit below threshold. The results of ">=Threshold" mean those when the exposure increases by rrunit above threshold.
RR = exp(beta*rrunit) and 95% C.I = exp((beta-1.96*s.e(beta), beta+1.96*s.e(beta))*rrunit)

Youn-Hee Lim, Il-Sang Ohn, and Ho Kim

# read the Seoul data set and create lag variables
data(mort)
seoul = read6city(mort, 11)
seoul_lag = lagdata(seoul, c("meantemp", "mintemp", "meanpm10", "meanhumi"), 5)

# find a optimal threshold and conduct piecewise linear regression
mythresh = threshpt(nonacc ~ meantemp_m3 + meanpm10_m2 +  meanhumi + ns(sn, 4*10) + factor(dow), 
			     expvar = "meantemp_m3", family = "poisson", data = seoul_lag,
			     startrng = 23, endrng = 33, searchunit = 0.2)


# calculate relative risks
rrcalc(mythresh)