rqlm-package: The 'rqlm' package.
In rqlm: Modified Poisson, Logistic and Least-Squares Regressions for Binary Outcome and Their Generalizations
rqlm-package R Documentation
The 'rqlm' package.
Description
Modified Poisson, logistic and least-squares regression analyses for binary outcomes have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate accurate confidence intervals.
References
Cheung, Y. B. (2007). A modified least-squares regression approach to the estimation of risk difference. American Journal of Epidemiology 166, 1337-1344.
Noma, H. (2025). Robust variance estimators for risk ratio estimators from logistic regression in cohort and case-cohort studies. Forthcoming.
Noma, H. and Gosho, M. (2025). Finite-sample improved confidence intervals based on the estimating equation theory for the modified Poisson and least-squares regressions. Epidemiologic Methods 14, 20240030.
Noma, H. and Gosho, M. (2025). Logistic mixed-effects model analysis with pseudo-observations for estimating risk ratios in clustered binary data analysis. Statistics in Medicine 44, e70280.
Noma, H., Sunada, H., and Gosho, M. (2025). Quasi-likelihood ratio tests and the Bartlett-type correction for improved inferences of the modified Poisson and least-squares regressions for binary outcomes. Statistica Neerlandica 79, e70012.
Shiiba, H. and Noma, H. (2025). Confidence intervals of risk ratios for the augmented logistic regression with pseudo-observations. Stats 8, 83.
Zou, G. (2004). A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology 159, 702-706.
rqlm documentation built on Nov. 23, 2025, 5:06 p.m.
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| rqlm-package | R Documentation |
The 'rqlm' package.
Description
Modified Poisson, logistic and least-squares regression analyses for binary outcomes have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate accurate confidence intervals.
References
Cheung, Y. B. (2007). A modified least-squares regression approach to the estimation of risk difference. American Journal of Epidemiology 166, 1337-1344.
Noma, H. (2025). Robust variance estimators for risk ratio estimators from logistic regression in cohort and case-cohort studies. Forthcoming.
Noma, H. and Gosho, M. (2025). Finite-sample improved confidence intervals based on the estimating equation theory for the modified Poisson and least-squares regressions. Epidemiologic Methods 14, 20240030.
Noma, H. and Gosho, M. (2025). Logistic mixed-effects model analysis with pseudo-observations for estimating risk ratios in clustered binary data analysis. Statistics in Medicine 44, e70280.
Noma, H., Sunada, H., and Gosho, M. (2025). Quasi-likelihood ratio tests and the Bartlett-type correction for improved inferences of the modified Poisson and least-squares regressions for binary outcomes. Statistica Neerlandica 79, e70012.
Shiiba, H. and Noma, H. (2025). Confidence intervals of risk ratios for the augmented logistic regression with pseudo-observations. Stats 8, 83.
Zou, G. (2004). A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology 159, 702-706.
R Package Documentation
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