prLogistic

prLogistic estimates prevalence ratios from binary outcome data using logistic regression with delta-method and bootstrap adjustments to provide accurate PR estimates and confidence intervals for independent and clustered cross-sectional studies.

Key Features:

• Estimation of Prevalence Ratios: Estimates prevalence ratios (PR) from binary outcome data as an alternative to odds ratios derived from logistic regression.
• Delta Method: Applies the delta method to provide analytical adjustments for PR estimates and their variance.
• Bootstrap Techniques: Uses bootstrap resampling to obtain non-parametric confidence intervals for PR estimates.
• Handling of Independent and Correlated Data: Supports analysis of independent observations and correlated binary data from clustered studies, accounting for intra-cluster correlation.
• R and CRAN Integration: Implements methods within the R statistical computing environment and references The Comprehensive R Archive Network (CRAN).

Scientific Applications:

• Cross-sectional Studies: Estimation of prevalence and exposure–outcome associations at a single time point using prevalence ratios.
• Clustered Data Analysis: Analysis of group- or cluster-sampled binary outcomes (e.g., schools, communities) with adjustment for intra-cluster correlation.
• Epidemiological Research: Improved interpretability of effect estimates in epidemiological studies where PRs are preferred over odds ratios.

Methodology:

Performs logistic regression to estimate prevalence ratios and derives PR estimates and confidence intervals using the delta method and bootstrap resampling for independent and clustered binary data.