![]() When on the society site, please use the credentials provided by that society.If you see ‘Sign in through society site’ in the sign in pane within a journal: Many societies offer single sign-on between the society website and Oxford Academic. Society member access to a journal is achieved in one of the following ways: If you cannot sign in, please contact your librarian. If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.Įnter your library card number to sign in. Following successful sign in, you will be returned to Oxford Academic.Do not use an Oxford Academic personal account. When on the institution site, please use the credentials provided by your institution.Select your institution from the list provided, which will take you to your institution's website to sign in.Click Sign in through your institution.Shibboleth / Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.Ĭhoose this option to get remote access when outside your institution. Typically, access is provided across an institutional network to a range of IP addresses. If you are a member of an institution with an active account, you may be able to access content in one of the following ways: I haven’t tested my simulation against any packages which calculate power for logistic regression, but if anyone can it would be great to hear from you.Get help with access Institutional accessĪccess to content on Oxford Academic is often provided through institutional subscriptions and purchases. ![]() This looks like this (each line represents an event rate):Īs ever, if anyone can spot an error or suggest a simpler way to do this then let me know. We can also keep the odds ratio constant, but adjust the proportion of events per trial. The plot of these looks like this (each line represents an odd ratio):. ![]() I simulated a range of odds ratios and a range of sample sizes. We can do some interesting things with R. It seemed to work pretty well calculating the power to be within ~ 1% of the power of the examples given in table II of that paper. I checked it against the examples given in Hsieh, 1999. Summary(model <- glm(ytest ~ xtest, family = "binomial"))$coefficients <. The proportion of the time that the simulation correctly get's the p < 0.05 is essentially the power of the logistic regression for your number of cases, odds ratio and intercept. We use R’s inbuilt function replicate to do this 10,000 times, and count the proportion where it gets it right (i.e. When you’re happy that the proportion of events is right (with some prior knowledge of the dataset), you can then fit a model and calculate a p value for that model. This plot shows how the intercept and odds ratio affect the overall proportion of events per trial: Prop <- length(which(ytest <= 0.5))/length(ytest) The independent variable is assumed to be normally distributed with mean 0 and variance 1. To change the number of events adjust odds.ratio. We then initially calculate the overall proportion of events. In this code we use the approach which Kleinman and Horton use to simulate data for a logistic regression. If it does 95% of the time, then you have 95% power. One approach with R is to simulate a dataset a few thousand times, and see how often your dataset gets the p value right. Power calculations for logistic regression are discussed in some detail in Hosmer and Lemeshow (Ch 8.5). It’s based on the approach which Stephen Kolassa described. I thought I’d post it in a little more depth here, with a few illustrative figures. So, I posted an answer on cross validation regarding logistic regression. I’ll post again with a correction or a more full explanation when I’ve sorted it. Please note – I’ve spotted a problem with the approach taken in this post – it seems to underestimate power in certain circumstances.
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