Interrupted Time Series, estimates before and after jump

data analysis models
1

Professor Harrell,

In this example, after fitting spline functions and jump(day of the ban), how do we generate one estimate to indicate, “average”, if that’s the right word.

beta, before the jump (day of ban) , including SE and C.Is
beta, after the jump(day of ban), including SE and C.Is

require(rms)  
require(ggplot2)

getHdata(sicily)   # fetch dataset from hbiostat.org/data
d    <- sicily
dd   <- datadist(d);  options(datadist='dd')

off <- list(stdpop=mean(d$stdpop))    # offset for prediction (383464.4)
w   <- geom_point(aes(x=time, y=rate), data=d)
v   <- geom_vline(aes(xintercept=37, col=I('red')))
yl  <- ylab('Acute Coronary Cases Per 100,000')


h <- function(x) {
  z <- cbind(rcspline.eval(x, k),
             sin=sin(2*pi*x/12), cos=cos(2*pi*x/12),
             jump=x >= 37)
  attr(z, 'nonlinear') <- 2 : ncol(z)
  z
}
f <- Glm(aces ~ offset(log(stdpop)) + gTrans(time, h),
         data=d, family=poisson, x=TRUE, y=TRUE) # x, y for LRTs

f
1 Like
2

If I understand your question I don’t think averages are very good summaries for curves that aren’t flat on both sides of the jump. I would concentrate on contrast()’s of a specific pair of times.

1 Like
3

That makes sense. Thanks Professor Harrell.