- 著者
- Masahiko Gosho Tomohiro Ohigashi Kengo Nagashima Yuri Ito Kazushi Maruo
- 出版者
- Japan Epidemiological Association
- 雑誌
- Journal of Epidemiology (ISSN:09175040)
- 巻号頁・発行日
- pp.JE20210089, (Released:2021-09-25)
- 参考文献数
- 46
- 被引用文献数
- 8
Background: Logistic regression models are widely used to evaluate the association between a binary outcome and a set of covariates. However, when there are few study participants at the outcome and covariate levels, the models lead to bias of the odds ratio (OR) estimated using the maximum likelihood (ML) method. This bias is known as sparse data bias, and the estimated OR can yield impossibly large values because of data sparsity. However, this bias has been ignored in most epidemiological studies.Methods: We review several methods for reducing sparse data bias in logistic regression. The primary aim is to evaluate the Bayesian methods in comparison with the classical methods, such as the ML, Firth’s, and exact methods using a simulation study. We also apply these methods to a real data set.Results: Our simulation results indicate that the bias of the OR from the ML, Firth’s, and exact methods is considerable. Furthermore, the Bayesian methods with hyper-g prior modeling of the prior covariance matrix for regression coefficients reduced the bias under the null hypothesis, whereas the Bayesian methods with log F-type priors reduced the bias under the alternative hypothesis.Conclusion: The Bayesian methods using log F-type priors and hyper-g prior are superior to the ML, Firth’s, and exact methods when fitting logistic models to sparse data sets. The choice of a preferable method depends on the null and alternative hypothesis. Sensitivity analysis is important to understand the robustness of the results in sparse data analysis.
- 著者
- Masahiko Gosho Tomohiro Ohigashi Kengo Nagashima Yuri Ito Kazushi Maruo
- 出版者
- Japan Epidemiological Association
- 雑誌
- Journal of Epidemiology (ISSN:09175040)
- 巻号頁・発行日
- vol.33, no.6, pp.332, 2023-06-05 (Released:2023-06-05)
- 参考文献数
- 1
- 著者
- Hisashi Noma Kengo Nagashima Shogo Kato Satoshi Teramukai Toshi A. Furukawa
- 出版者
- Japan Epidemiological Association
- 雑誌
- Journal of Epidemiology (ISSN:09175040)
- 巻号頁・発行日
- vol.32, no.10, pp.441-448, 2022-10-05 (Released:2022-10-05)
- 参考文献数
- 30
- 被引用文献数
- 1 2
Background: In meta-analysis, the normal distribution assumption has been adopted in most systematic reviews of random-effects distribution models due to its computational and conceptual simplicity. However, this restrictive model assumption is possibly unsuitable and might have serious influences in practices.Methods: We provide two examples of real-world evidence that clearly show that the normal distribution assumption is explicitly unsuitable. We propose new random-effects meta-analysis methods using five flexible random-effects distribution models that can flexibly regulate skewness, kurtosis and tailweight: skew normal distribution, skew t-distribution, asymmetric Subbotin distribution, Jones–Faddy distribution, and sinh–arcsinh distribution. We also developed a statistical package, flexmeta, that can easily perform these methods.Results: Using the flexible random-effects distribution models, the results of the two meta-analyses were markedly altered, potentially influencing the overall conclusions of these systematic reviews.Conclusion: The restrictive normal distribution assumption in the random-effects model can yield misleading conclusions. The proposed flexible methods can provide more precise conclusions in systematic reviews.