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- W2004848240 abstract "Abstract The small sample performance of regression estimators of the odds ratio in multiple 2 x 2 tables is studied for conditional and unconditional maximum likelihood [1], for the McCullagh approximation to the conditional maximum likelihood estimator [5] and for Davis' generalized Mantel-Haenszel (GMH) estimator [6]. The unconditional maximum likelihood estimator is biased and highly variable and is not recommended. The GMH estimator is competitive with the conditional method and superior to the McCullagh approximation in cases where the latter breaks down. A new covariance estimator for the GMH method based on the work of Robins et al. [27] is developed that is superior to both of Davis' GMH covariance estimators. We recommend the GMH method with the new covariance estimator in cases where the binomial assumption required by the conditional method is in doubt. Otherwise, we recommend McCullagh's approximation except in cases of sparse data with odds ratios greater than about 3, in which case the conditional method is computationally feasible." @default.
- W2004848240 created "2016-06-24" @default.
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- W2004848240 date "1990-03-01" @default.
- W2004848240 modified "2023-09-24" @default.
- W2004848240 title "A small sample simulation study of methods for log odds ratio regression analysis" @default.
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- W2004848240 doi "https://doi.org/10.1016/0167-9473(90)90122-x" @default.
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