Software, etc.   [J Hanley] R code to extract underlying data from Kaplan Meier and Nelson-Aalen curves. along with some worked examples.   LINK Applet (Flash) to illustrate different fitting methods and different model assumptions for a very small dataset with 2 datapoints and 1 parameter. One has 2 independent observations from the (no-intercept) model           E[y|x] = mu_{y|x} = beta times x. The y's might represent the total numbers of typographical errors on x randomly sampled pages of a large document, and the data might be y=2 errors in total in a sample of x=1 page, and y=8 errors in total in a separate sample of x=2 pages. The beta in the model represents the mean number of errors per page of the document. Or the y's might represent the total weight of x randomly sample pages of a document, and the data might be y=2 units of weight in total for a sample of x=1 page, and y=8 units for a separate sample of x=2 pages. The beta in the model represents the mean weight per page of the document. We gave this `estimation of beta' problem (x,y)=(1,2) & (2,8) to several statisticians and epidemiologists, and to several grade 6 students, and they gave us a variety of estimates, such as beta_hat = 3.6/page, 3.33/page, and 3.45! See WHY by clicking at various locations to try out various slopes: applet R code to create a 'case-base' dataset that allows for Fitting of Smooth-in-Time Prognostic Risk Functions via Logistic Regression. (See also -- under r e p r i n t s -- on my home page, the article with this title by Hanley JA and Miettinen OS in The International Journal of Biostatistics, Vol 5, Issue 1 2009).   zip file   includes R code and dataset analyzed in 2nd edition of textbook by Collett. SAS code [including link to dataset] for bootstrap standard error of estimate of First Principal Component: Appendix to article "Creating non-parametric bootstrap samples using Poisson frequencies" in Computer Methods and Programs in Biomedicine. 2006 Jul;83(1):57-62; authors: James A. Hanley and Brenda MacGibbon. SAS implementation of the 'placement' or 'U-statistics' method described in Hanley JA and Hajian-Tilaki KO. Sampling Variability of Nonparametric Estimates of the Areas under Receiver Operating Characteristic Curves: An Update. Academic Radiology, 1997 4:49-58.     SAS Program Article by Hanley and Hajian-Tilaki. Sampling Variability of Nonparametric Estimates of the Areas under Receiver Operating Characteristic Curves: An Update. Academic Radiology, 1997 4:49-58.     pdf Article by Hanley and McNeil "The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve." Radiology 1982: 143: 29-36.     pdf Article by Hanley and McNeil "A Method of Comparing the Areas under ROC curves derived from same cases." Radiology 1983: 148: 839-843.     pdf Appendix to Hanley and McNeil Radiology "A Method of Comparing the Areas under ROC curves derived from same cases." Radiology 1983: 148: 839-843.     pdf Article by McNeil and Hanley "Statistical Approaches to the Analysis of ROC curves." Medical Decision Making 1984: 4(2): 136-149.     pdf Updated: April 19, 2009