qlogist: Augmented (modified) logistic regression analyses for...

In rqlm: Modified Poisson, Logistic and Least-Squares Regressions for Binary Outcome and Their Generalizations

Augmented (modified) logistic regression analyses for estimating risk ratio

Description

Logistic regression with augmented pseudo-observations for estimating risk ratios is performed. This function is handled by a similar way with lm or glm. Also, the resultant coefficients and confidence limits can be transformed to exponential scales by specifying eform. The Morel-Bokossa-Neerchaal-type small-sample corrected estimator is adopted for standard error estimation as the default method.

Usage

qlogist(formula, data, eform=TRUE, cl=0.95, digits=4, var.method="MBN")

Arguments

formula	An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	A data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.
eform	A logical value that specify whether the outcome should be transformed by exponential function (default: TRUE)
cl	Confidence level for calculating confidence intervals (default: 0.95)
digits	Number of decimal places in the output (default: 4).
var.method	Method for estimating standard errors. Standard robust variance estimator (standard) and Morel-Bokossa-Neerchaal-type corrected estimator (MBN) are available (default: MBN).

Value

Results of the augmented (modified) logistic regression analysis.

• coef: Coefficient estimates; transformed to the exponential scale if eform=TRUE.

• SE: Robust standard error estimates for coef.

• CL: Lower limits of confidence intervals.

• CU: Upper limits of confidence intervals.

• P-value: P-values for the coefficient tests.

References

Diaz-Quijano, F. A. (2012). A simple method for estimating relative risk using logistic regression. BMC Medical Research Methodology 12, 14.

Morel, J. G., Bokossa, M., and Neerchal, N. (2003). Small sample correction for the variance of GEE estimators. Biometrical Journal 45, 395-409.

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). Logistic mixed-effects model analysis with pseudo-observations for estimating risk ratios in clustered binary data analysis. Statistics in Medicine 44, e70280.

Schouten, E. G., Dekker, J. M., Kok, F. J., et al. (1993). Risk ratio and rate ratio estimation in case-cohort designs: hypertension and cardiovascular mortality. Statistics in Medicine 12, 1733-1745.

Shiiba, H., and Noma, H. (2025). Confidence intervals of risk ratios for the augmented logistic regression with pseudo-observations. Stats 8, 83.

Examples

data(exdata02)

qlogist(y ~ x1 + x2 + x3 + x4, data=exdata02)
# Augmented logistic regression analysis
# Coefficient estimates are translated to risk ratio scales

rqlm documentation built on Nov. 23, 2025, 5:06 p.m.