rqlm (version 4.2-1)

qlogist: 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")

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.

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), Morel-Bokossa-Neerchaal-type corrected estimator (MBN), Gosho-Sato-Takeuchi-type corrected estimator (GST), and Wang-Long-type corrected estimator (WL) are available (default: MBN).

References

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

Gosho, M., Sato, Y., and Takeuchi, H. (2014). Robust covariance estimator for small-sample adjustment in the generalized estimating equations: a simulation study. Science Journal of Applied Mathematics and Statistics 2, 20-25.

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.

Wang, M., and Long, Q. (2011). Modified robust variance estimator for generalized estimating equations with improved small-sample performance. Statistics in Medicine 30, 1278-1291.

Examples

data(exdata02)

qlogist(y ~ x1 + x2 + x3 + x4, data=exdata02)
# Augmented logistic regression analysis
# Coefficient estimates are translated to risk ratio scales
# MBN robust variance estimator is adopted.

qlogist(y ~ x1 + x2 + x3 + x4, data=exdata02, var.method="GST")
# GST robust variance estimator is adopted.

qlogist(y ~ x1 + x2 + x3 + x4, data=exdata02, var.method="WL")
# WL robust variance estimator is adopted.