# Power calculations, logistic regression with continuous exposure--cohort [duplicate]

I'm trying to estimate power in a logistic regression with a continuous exposure in a cohort study (ie, the ratio of the sampling probabilities is 1). I have population cumulative incidence (probability) and population exposure variability and exposure mean and an expected odds ratio. I also have a total sample size.

I'm using R and it seems like Hmisc::bpower is only for logistic regression with binary exposure and I can't seem to find any packages that estimate binomial power with continuous exposure.

I've attempted the following simulation but it's quite slow given my total sample size and I'm not sure if it's right:

p <- vector()
betahat <- vector()
for(i in 1:1000){
n <- 40000  #total sample size
intercept = log(0.008662265)  #where exp(intercept) = P(D=1)
beta <- log(1.4) #where exp(beta)=OR corresponding to a one unit change in xtest
xtest <- rnorm(n,1.2,.31)  #xtest is vector length 40,000 with mean 1.2 and sd .31
linpred <- intercept + xtest*beta #linear predictor
prob <- exp(linpred)/(1 + exp(linpred)) #link function
runis <- runif(n,0,1) #generate a vector length n from a uniform distribution 0,1
ytest <- ifelse(runis < prob,1,0)  #if a random value from a uniform distribution 0,1 is less than prob, then the outcome is 1.  otherwise the outcome is 0
coefs <- coef(summary(glm(ytest~xtest, family="binomial")))  #run a logistic regression
p[i] <- coefs[2,4] #store the p value
betahat[i] <- coefs[2,1] #store the unexponentiated betahat
}

mean(p < .05)
#power

exp(mean(betahat))
#sanity check, should equal 1.4--it does


Is there anything wrong with this approach?

One concern of mine is that the cumulative incidence (ie, probability of event over the given time period) comes from a population that did not have 0 exposure. In fact, it's reasonable to assume that the value i'm using for an intercept is actually from a population that has an exposure variability similar to mine. In that case, how would I estimate the unexposed probability given an odds ratio (and other information that I would find in say, a published paper) to use in my power calculation?

• Mar 12, 2019 at 11:26