Biomedical subjects

Alan Agresti

Publications and source records attributed to Alan Agresti.

5 recordsLinked to original sources

PubMed2006

Multivariate extensions of McNemar's test.

This article considers global tests of differences between paired vectors of binomial probabilities, based on data from two dependent multivariate binary samples. Difference is defined as either an inhomogeneity in the marginal distributions or asymmetry in the joint distribution. For detecting the first type of difference, we propose a multivariate extension of McNemar's test and show that it is a generalized score test under a generalized estimating equations (GEE) approach. Univariate features such as the relationship between the Wald and score tests and the dropout of pairs with the same response carry over to the multivariate case and the test does not depend on the working correlation assumption among the components of the multivariate response. For sparse or imbalanced data, such as occurs when the number of variables is large or the proportions are close to zero, the test is best implemented using a bootstrap, and if this is computationally too complex, a permutation distribution. We apply the test to safety data for a drug, in which two doses are evaluated by comparing multiple responses by the same subjects to each one of them.

Biometry↗

PubMed2005

Simple improved confidence intervals for comparing matched proportions.

For binary matched-pairs data, this article discusses interval estimation of the difference of probabilities and an odds ratio for comparing 'success' probabilities. We present simple improvements of the commonly used Wald confidence intervals for these parameters. The improvement of the interval for the difference of probabilities is to add two observations to each sample before applying it. The improvement for estimating an odds ratio transforms a confidence interval for a single proportion.

Case-Control Studies↗

PubMed2005

Frequentist performance of Bayesian confidence intervals for comparing proportions in 2 x 2 contingency tables.

This article investigates the performance, in a frequentist sense, of Bayesian confidence intervals (CIs) for the difference of proportions, relative risk, and odds ratio in 2 x 2 contingency tables. We consider beta priors, logit-normal priors, and related correlated priors for the two binomial parameters. The goal was to analyze whether certain settings for prior parameters tend to provide good coverage performance regardless of the true association parameter values. For the relative risk and odds ratio, we recommend tail intervals over highest posterior density (HPD) intervals, for invariance reasons. To protect against potentially very poor coverage probabilities when the effect is large, it is best to use a diffuse prior, and we recommend the Jeffreys prior. Otherwise, with relatively small samples, Bayesian CIs using more informative (even uniform) priors tend to have poorer performance than the frequentist CIs based on inverting score tests, which perform uniformly quite well for these parameters.

Bayes Theorem↗

PubMed2004

Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data.

Binary matched-pairs data occur commonly in longitudinal studies, such as in cross-over experiments. Many analyses for comparing the matched probabilities of a particular outcome do not utilize pairs having the same outcome for each observation. An example is McNemar's test. Some methodologists find this to be counterintuitive. We review this issue in the context of subject-specific and population-averaged models for binary data, with various link functions. For standard models and inferential methods, pairs with identical outcomes may affect the estimated size of the effect and its standard error, but they have negligible, if any, effect on significance. We also discuss extension of this result to matched sets.

Logistic Models↗

PubMed2002

Unconditional small-sample confidence intervals for the odds ratio.

The traditional approach to 'exact' small-sample interval estimation of the odds ratio for binomial, Poisson, or multinomial samples uses the conditional distribution to eliminate nuisance parameters. This approach can be very conservative. For two independent binomial samples, we study an unconditional approach with overall confidence level guaranteed to equal at least the nominal level. With small samples this interval tends to be shorter and have coverage probabilities nearer the nominal level.

Journal Article↗