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History Greek & Roman Civilization (hist 1421).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. 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. Is there anything wrong with this approach? otherwise the outcome is 0Ĭoefs <- coef(summary(glm(ytest~xtest, family="binomial"))) #run a logistic regressionīetahat <- coefs #store the unexponentiated betahat 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. Runis <- runif(n,0,1) #generate a vector length n from a uniform distribution 0,1 Prob <- exp(linpred)/(1 + exp(linpred)) #link function Linpred <- intercept + xtest*beta #linear predictor Xtest <- rnorm(n,1.2.31) #xtest is vector length 40,000 with mean 1.2 and sd. Intercept = log(0.008662265) #where exp(intercept) = P(D=1)īeta <- log(1.4) #where exp(beta)=OR corresponding to a one unit change in xtest 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() 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 have population cumulative incidence (probability) and population exposure variability and exposure mean and an expected odds ratio. 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).
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