Modified Poisson Regression for Binary Outcome and Related Methods
Modified Poisson and least-squares regression analyses for binary outc...
Calculating stabilized weights for IPW analysis: Single time point
Calculating stabilized weights for IPW analysis: Longitudinal data
Calculating stabilized weights for IPW analysis: Single time point (fo...
Creating summary table for IPTW analysis using stabilized weights
Calculating bootstrap confidence interval for modified least-squares r...
Calculating bootstrap confidence interval for modified Poisson regress...
Computation of the ordinary confidence intervals and P-values using th...
Pooled logistic regression for target trial emulation
Multiple imputation analysis for the generalized linear model
Multiple imputation analysis for modified Poisson and least-squares re...
Calculating confidence interval for modified least-squares regression ...
Calculating confidence interval for modified Poisson regression based ...
Augmented (modified) logistic regression analyses for estimating risk ...
The 'rqlm' package.
Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2025)<Forthcoming>, and Cheung (2007) <doi:10.1093/aje/kwm223> 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.