Review of Generalized Linear Models and Extensions by Hardin and Hilbe

The new book Hardin and Hilbe (Stata Press, 2001) is reviewed. Copyright 2001 by Stata Corporation

Scabies by Kenneth Mellamby

Book Review of Scabies by Kenneth Mellamby. Second Edition. E.W. Classey Limited, London, 1972

Efficient Calculation of Jackknife Confidence Intervals for Rank Statistics

An algorithm is presented for calculating concordance-discordance totals in a time of order N log N , where N is the number of observations, using a balanced binary search tree. These totals can be used to calculate jackknife estimates and confidence limits in the same time order for a very wide range of rank statistics, including Kendall's tau, Somers' D, Harrell's c, the area under the receiver operating characteristic (ROC) curve, the Gini coefficient, and the parameters underlying the sign and rank-sum tests. A Stata package is introduced for calculating confidence intervals for these rank statistics using this algorithm, which has been implemented in the Mata compilable matrix programming language supplied with Stata.

Multiple test procedures and smile plots

Scientists often have good reasons for wanting to calculate multiple confidence intervals and/or p-values, especially when scanning a genome. However, if we do this, then the probability of not observing at least one "significant" difference tends to fall, even if all null hypotheses are true. A skeptical public will rightly ask whether a difference is "significant" when considered as one of a large number of parameters estimated. This presentation demonstrates some solutions to this problem, using the unofficial Stata packages parmest and smileplot. The parmest package allows the calculation of Bonferroni-corrected or Sidak-corrected confidence intervals for multiple estimated parameters. The smileplot package contains two programs, multproc (which carries out multiple test procedures) and smileplot (which presents their results graphically by plotting the p-value on a reverse log scale on the vertical axis against the parameter estimate on the horizontal axis). A multiple test procedure takes, as input, a set of estimates and p-values, and rejects a subset (possibly empty) of the null hypotheses corresponding to these p-values. Multiple test procedures have traditionally controlled the family-wise error rate (FWER), typically enabling the user to be 95% confident that all the rejected null hypotheses are false, and that all the corresponding "discoveries" are real. The price of this confidence is that the power to detect a difference of a given size tends to zero as the number of measured parameters become large. Therefore, recent work has concentrated on procedures that control the false disco very rate (FDR), such as the Simes procedure and the Yekutieli-Benjamini procedure. FDR-controlling procedures attempt to control the number of false discoveries as a proportion of the number of true discoveries, typically enabling the user to be 95% confident that some of the discoveries are real, or 90% confident that most of the discoveries are real. This less stringent requirement causes power to "bottom out" at a non-zero level as the number of tests becomes large. The smileplot package offers a selection of multiple test procedures of both kinds.

Review of Generalized Linear Models and Extensions by Hardin and Hilbe

The new book Hardin and Hilbe (Stata Press, 2001) is reviewed. Copyright 2001 by Stata Corporation.