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rqlm (version 2.3-1)

Modified Poisson and Least-Squares Regressions for Binary Outcome and Their Generalizations

Description

Modified Poisson and least-squares regression analyses for binary outcomes of Zou (2004) 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 (Noma and Gosho (2024) ).

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Version

Install

install.packages('rqlm')

Monthly Downloads

297

Version

2.3-1

License

GPL-3

Maintainer

Hisashi Noma

Last Published

January 12th, 2025

Functions in rqlm (2.3-1)

mch

A cluster-randomised trial dataset for the maternal and child health handbook
qesci.pois

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

The 'rqlm' package.
mi_glm

Multiple imputation analysis for the generalized linear model
exdata02

A simulated example dataset
exdata03

A simulated example dataset with missing covariates
rqlm

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

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

Multiple imputation analysis for modified Poisson and least-squares regressions
exdata01

A simulated example dataset
bsci.pois

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

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

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