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rqlm (version 4.2-1)

Modified Poisson Regression for Binary Outcome and Related Methods

Description

Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) , Noma (2025), and Cheung (2007) 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 alternative accurate confidence intervals.

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Version

Install

install.packages('rqlm')

Monthly Downloads

329

Version

4.2-1

License

GPL-3

Maintainer

Hisashi Noma

Last Published

December 15th, 2025

Functions in rqlm (4.2-1)

exdata01

A simulated example dataset
mi_glm

Multiple imputation analysis for the generalized linear model
mi_rqlm

Multiple imputation analysis for modified Poisson and least-squares regressions
stabwt

Calculating stabilized weights for IPW analysis: Single time point
rqlm

Modified Poisson and least-squares regression analyses for binary outcomes
ttemsm

Pooled logistic regression for target trial emulation
mch

A cluster-randomised trial dataset for the maternal and child health handbook
rqlm-package

The 'rqlm' package.
qlogist

Augmented (modified) logistic regression analyses for estimating risk ratio
qesci.ls

Calculating confidence interval for modified least-squares regression based on the quasi-score test
qesci.pois

Calculating confidence interval for modified Poisson regression based on the quasi-score test
exdata03

A simulated example dataset with missing covariates
bsci.pois

Calculating bootstrap confidence interval for modified Poisson regression based on the quasi-score statistic
bsci.ls

Calculating bootstrap confidence interval for modified least-squares regression based on the quasi-score statistic
coeff

Computation of the ordinary confidence intervals and P-values using the model variance estimator
stabwtmulti

Calculating stabilized weights for IPW analysis: Single time point (for more than 3 groups)
stabwtlong

Calculating stabilized weights for IPW analysis: Longitudinal data
exdata04

A simulated example dataset for target trial emulation
exdata02

A simulated example dataset