In stratified bilateral studies, responses from two paired body parts are correlated. Confidence intervals (CIs), which reveal various features of the data, should take the correlations into account. In this article, five CI methods (sample-size weighted naïve Maximum likelihood estimation (MLE)-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, MLE-based score CI and pooled MLE-based Wald-type CI) are derived for proportion ratios under the assumption of equal correlation coefficient within each stratum. Monte Carlo simulation shows that the complete MLE-based Wald-type CI approach generally produces the shortest mean interval width and satisfactory empirical coverage probability with close form solution; while the profile likelihood CI and the MLE-based score CI provide preferred ratio of non coverage probability and are more symmetric. Two real examples are used to demonstrate the performance of the proposed methods.
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http://dx.doi.org/10.1080/10543406.2018.1489405 | DOI Listing |
J Biopharm Stat
March 2020
a Department of Biostatistics , University at Buffalo, Buffalo , NY , USA.
In stratified bilateral studies, responses from two paired body parts are correlated. Confidence intervals (CIs), which reveal various features of the data, should take the correlations into account. In this article, five CI methods (sample-size weighted naïve Maximum likelihood estimation (MLE)-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, MLE-based score CI and pooled MLE-based Wald-type CI) are derived for proportion ratios under the assumption of equal correlation coefficient within each stratum.
View Article and Find Full Text PDFStat Med
February 2007
Samuel Lunenfeld Research Institute, Prosserman Centre for Health Research, Mount Sinai Hospital, Toronto, Ont., Canada M5G 1X5.
Logistic regression is one of the most widely used regression models in practice, but alternatives to conventional maximum likelihood estimation methods may be more appropriate for small or sparse samples. Modification of the logistic regression score function to remove first-order bias is equivalent to penalizing the likelihood by the Jeffreys prior, and yields penalized maximum likelihood estimates (PLEs) that always exist, even in samples in which maximum likelihood estimates (MLEs) are infinite. PLEs are an attractive alternative in small-to-moderate-sized samples, and are preferred to exact conditional MLEs when there are continuous covariates.
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