[R-meta] Changing reference level dose-response association

[Stens, Niels]

Dear members of the R Sig Meta-analysis community,

I am currently performing a meta-analysis on the association between step count versus mortality and the incidence of cardiovascular diseases in the general population. For this, I have modelled a non-linear dose response association with cubic splines (see down below for reproducible example). Within the analysis I would ideally change the reference level. For example, current reference levels in the analysis are ~7000 and ~5000 steps/day for mortality and incident CVD respectively, but ideally I want to change this to the same reference level (e.g. 2500 steps/day). I have tried performing this using datadist (from rms package), but this did not change the reference level. How can I achieve this?

set.seed(42)
library("metafor")
library("rms")

steps_day = seq(1500, 16000, 500) #number of steps someone takes on a day, ranging from 1500 to 16000, split up per 500 step increment
lnHR_AC = runif(1, 0, 1.5)*exp(-runif(1,0.01,0.05)*steps_day) + rnorm(30, 0, 0.5) #ln-transformed hazard ratios for all-cause mortality
SE_AC = rnorm(lnHR_AC, 0.5) #corresponding SE
lnHR_CVD = runif(1, 0, 1.5)*exp(-runif(1,0.01,0.05)*steps_day) + rnorm(30, 0, 0.5) - 0.25 #ln-transformed hazard ratios for cardiovascular diseases
SE_CVD = rnorm(lnHR_CVD, 0.6) #corresponding SE

dat = data.frame(steps_day, lnHR_AC, SE_AC, lnHR_CVD, SE_CVD) #make dataframe

#All-cause Mortality
xs <- seq(1500, 16000, length=30) #range of step counts, per 500 step increment
knots = quantile(dat$steps_day, p = c(0.05, 0.5, 0.95)) #specify the number and location of knots
res.rcs_AC <- rma(yi = lnHR_AC, sei = SE_AC, mods = ~rcs(steps_day, knots), data= dat, method = "REML") #create cubic spline object

#change reference level
#ddist = datadist(dat$steps_day)
#ddist$limits["Adjust to",2500]
#options(datadist = "ddist")

layout(matrix(c(1,2),1,2, byrow = TRUE))

plot(dat$steps_day, dat$lnHR_AC, col="white", xlab="Steps per day", ylab="Hazard Ratio", main="Cubic Spline Model for Mortality", ylim = c(0, 2), xlim = c(0, 15000)) #for making empty plot

sav_AC <- predict(res.rcs_AC, newmods=rcspline.eval(xs, knots = knots, inclx=TRUE), transf = exp)
polygon(c(xs, rev(xs)), c(sav_AC$ci.lb, rev(sav_AC$ci.ub)), col=rgb(0,0,0,.2), border=NA)
lines(xs, sav_AC$pred, lwd=3)

#CVD
res.rcs_CVD <- rma(yi = lnHR_CVD, sei = SE_CVD, mods = ~rcs(steps_day, knots), data=dat, method = "REML")

plot(dat$steps_day, dat$lnHR_CVD, col="white", xlab="Steps per day", ylab="Hazard Ratio", main="Cubic Spline Model for CVD", ylim = c(0, 2)) #for making empty plot

sav_CVD <- predict(res.rcs_CVD, newmods=rcspline.eval(xs, knots = knots, inclx=TRUE), transf = exp)
polygon(c(xs, rev(xs)), c(sav_CVD$ci.lb, rev(sav_CVD$ci.ub)), col=rgb(0,0,0,.2), border=NA)
lines(xs, sav_CVD$pred, lwd=3)


Many thanks in advance.

Kind regards,

Niels Stens, MSc
PhD candidate, departments of Cardiology and Physiology, Radboud University Medical Center, Nijmegen, The Netherlands


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