Note 4 - Stats for Clinical Trials Math 150 Jo Hardin Info...

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Stats for Clinical Trials, Math 150 Jo Hardin Info on ROC curves Truth Significant Null Test Significant True Positive False Positive P* Null False Negative True Negative N* P N type I error = FP type II error = FN sensitivity = power = true positive rate (TPR) = TP / P = TP / (TP+FN) false positive rate (FPR) = FP / N = FP / (FP + TN) specificity = 1 - FPR = TN / (FP + TN) accuracy (acc) = (TP+TN) / (P+N) positive predictive value (PPV) = precision = TP / (TP + FP) negative predictive value (NPV) = TN / (TN + FN) false discovery rate = 1 - PPV = FP / (FP + TP) ROC curve 1-specificity sensitivity 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1
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Below is the code for creating ROC curves in R: install.packages("ROCR") library(ROCR) smok.pred <- prediction(fitted(smok.log),lung.c) smok.perf <- performance(smok.pred,measure="tpr",x.measure="fpr") plot(smok.perf,xlab="1-specificity",ylab="sensitivity",main="ROC curve") abline(a=0,b=1) Note that R has many options within the
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Note 4 - Stats for Clinical Trials Math 150 Jo Hardin Info...

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