Abstract
The interpretation of odds ratios (OR) as prevalence ratios (PR) in cross-sectional studies have been criticized since this equivalence is not true unless under specific circumstances. The logistic regression model is a very well known statistical tool for analysis of binary outcomes and frequently used to obtain adjusted OR. Here, we introduce the prLogistic for the R statistical computing environment which can be obtained from The Comprehensive R Archive Network, https://cran.r-project.org/package=prLogistic. The package prLogistic was built to assist the estimation of PR via logistic regression models adjusted by delta method and bootstrap for analysis of independent and correlated binary data. Two applications are presented to illustrate its use for analysis of independent observations and data from clustered studies.
Highlights
The concept of risk is fundamental in several research areas, being the measures of risk associated to the probability of occurrence of an event of interest
We have shown how logistic regression models can be implemented to estimate the prevalence ratios and their confidence intervals using our prLogistic package, in situations where the observations are independent or when data comes from clustered studies
The package can accommodate the information of conditional and marginal standardization, commonly used in epidemiology, as well as either delta method or bootstrap resampling for the obtention of confidence intervals
Summary
The concept of risk is fundamental in several research areas, being the measures of risk associated to the probability of occurrence of an event of interest. One can estimate OR = exp(β), where β is a parameter related to the risk factor of interest. We introduce prLogistic, an R package built to assist estimation of PRs in cross-sectional studies via logistic regression models for analysis of both independent and correlated data. Suppose that we are evaluating the effect of a binary exposure X1 (0/1) on the occurrence of Y after adjustment by k – 1 independent variables (X2, ..., Xk) In this case, PR is given by PR = 1 + exp{–β0 – β2X2 – · · · – βkXk} . 1 + exp{–β0 – β1 – β2X2 – · · · – βkXk} Note that, in this expression, PR is function of the values of the independent variables included in the model, differently from OR
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